use of modelling for improvement of a business process

Masaryk University
Faculty of Economics and Administration
USE OF MODELLING FOR IMPROVEMENT
OF A BUSINESS PROCESS
Diploma work
Thesis Supervisor:
Ing. Ondřej ČÁSTEK, Ph.D
Author:
Fredy A. RODRIGUEZ B.
Brno, 2017
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MASARYK UNIVERSITY
Faculty of Economics and Administration
MASTER’S THESIS DESCRIPTION
Academic year: 2016/2017
Student:
Fredy Alberto Rodriguez Bautista
Field of Study:
Business Management (Eng.)
Title of the thesis/dissertation:
Use of modelling and simulation for improving a business process
Title of the thesis in English:
Use of modelling and simulation for improving a business process
Thesis objective, procedure and methods used: Aim of the thesis:
To improve a chosen business process
Procedure and techniques used:
The thesis will consist of two parts: Theoretical part will contain research of available methods and their evaluation with respect to the
aim of the thesis solved in the second (practical) part of the thesis.
The second part will consist of application of chosen method(s) onto
a particular business process. This part will include analysis and economical assessment of proposed solutions.
There is expected the use of process approach and these methods:
observation, description, analysis, modeling, simulation and optimization. The use of the program Witness is an advantage.
Extent of graphics-related work:
According to thesis supervisor’s instructions
Extent of thesis without supplements:
60 – 80 pages
Literature:
LAGUNA, Manuel and Johan MARKLUND. Business Process
Mode-ling,SimulationandDesign. : Prentice Hall, 2005. 429 s.
JESTON, John and Johan NELIS. Managementbyprocess:aroadmap
to sustainable business process management. 1. vyd. Amsterdam:
Elsevier/Butterworth-Heinemann, 2008. 303 s. ISBN 0-7506-87614.
MADISON, Dan. Processmapping,processimprovementandprocess
management. : Paton Press, 2005. 313 s. Chico, California. ISBN 1932828-04-4.
GREASLEY, Andrew. SimulationModellingBusiness. : Ashgate Publishing, Ltd., 2004. 226 s.
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Thesis supervisor:
Ing. Ondřej Částek, Ph.D.
Thesis supervisor’s department:
Department of Corporate Economy
Thesis assignment date: 2016/04/06
The deadline for the submission of Master’s thesis and uploading it into IS can be found in the academic year calendar.
In Brno, date: 2017/05/12
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Abstract
The objective of this thesis is to find a solution to the problems that currently occur in the
production of coffee in the farm Villa Fran in Colombia. The production is divided into 4 macroprocesses which were analysed and we suggested solutions to improve the current situation in
the company. While at the moment the profitability of the company is high, it is losing a lot of
money by the bottlenecks, especially those found in the drying part.
In the first chapter of this work we will go through the theories that will allow us to carry out
the work successfully, having as main focus the study of BPM, the definitions necessary for the
analysis and feedback cycle that will be improved every day, in addition we will see the life
cycle of this system mentioning how we will apply it to our study.
In the second chapter we study the simulations that we are going to carry out, we will understand
the software that we are going to use and the way in which this work will be conducted, starting
with understanding the problem, making an exhaustive study to collect the necessary data to
model, a sketch of what you want to capture in the simulation, build it, verify it and start
experimenting to find the necessary solutions.
In the third part we will find a small introduction to the world of coffee in order to make the
reader understand how the current process is since coffee is planted until it reaches its coffee
rate.
And finally we find all the above applied, after 6 models developed, we gave 5 solutions which
were qualified and weighted to find the one with the greatest benefit to the coffee grower,
winning the proposal that called to invest and grow in production.
Key Words: Business process, business process management, business process modelling,
business process improvement, Witness, coffee production.
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Author’s Statement
I hereby declare that I worked out the Diploma work, Use of modelling for improvement of a
business process, myself, under the supervision of Ing. Ondřej Částek, Ph.D and that I stated in
it all the literary resources and other specialist sources used according to legislation, internal
regulations of Masaryk University and internal management acts of Masaryk University and
the Faculty of Economics and Administration.
Brno, 12.04.2017
Fredy A. Rodriguez Bautista
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Acknowledgements
First of all, I am grateful to God for the opportunity to study at this prestigious university. I
would like to express my sincere gratitude especially to my supervisor Ing. Ondřej Částek, Ph.D
for his continuous support, patience, motivation and immense knowledge. His guidance helped
me all the time during the analysis and writing of this thesis. I am also thankful to all the teachers
who shared their knowledge and advice during lessons, for their assistance, dedicated
involvement and willingness during whole 2 years.
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Special Dedication
I am very fired up with the Czech government for allowing people from different parts of the
world to study at their universities, thus enriching their culture.
Also a special mention to my parents and farm workers Villa Fran, who gave me all the support
to develop this work.
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Table of Contents
INTRODUCTION ..................................................................................................................................15
THEORETICAL BACKGROUND ......................................................................................................19
1.
BUSINESS PROCESS MANAGEMENT ..................................................................................21
1.1
BPM FOUNDATION .................................................................................................. 23
1.2
BPM CONTRIBUTION ............................................................................................... 24
1.3
DEFINITION OF TERMS USED IN BPM ...................................................................... 25
1.3.1
1.3.2
1.3.3
1.3.4
1.3.5
1.3.6
1.3.7
1.3.8
1.4
1.4.1
1.4.2
1.4.3
1.4.4
1.4.5
2.
Achievement ........................................................................................................ 25
Organization ....................................................................................................... 26
Objectives ............................................................................................................ 26
Improvement ....................................................................................................... 26
Management ........................................................................................................ 26
Control ................................................................................................................ 27
Essential processes ............................................................................................. 27
Processes ............................................................................................................. 27
BUSINESS PROCESS MANAGEMENT LIFECYCLE ...................................................... 28
Design phase: ...................................................................................................... 28
Modeling phase ................................................................................................... 29
Execution phase .................................................................................................. 29
Monitoring phase ................................................................................................ 29
Optimization phase ............................................................................................. 29
SIMULATIONS PROJECTS ......................................................................................................31
2.1
COMPUTING SIMULATION ....................................................................................... 32
2.2
CONDUCTING A SIMULATION PROJECT ................................................................... 33
2.2.1 Identify the Problem ............................................................................................ 33
2.2.1.1 Establish Objectives ........................................................................................... 33
2.2.1.2 Scope and Level of Model Detail ........................................................................ 34
2.2.2 Design the study .................................................................................................. 34
2.2.3 Design the conceptual Model.............................................................................. 34
2.2.4 Formulate inputs, Assumptions and Process definitions .................................... 35
2.2.5 Build, Verify and Validate the model .................................................................. 37
2.2.5.1 Verification and validation ................................................................................. 37
2.2.5.2 Running the Model .............................................................................................. 38
2.2.6 Experimentation and Optimization ..................................................................... 39
2.2.7 Presentation of Results and Implementation ...................................................... 39
2.3
SIMULATION ............................................................................................................ 40
2.3.1
2.3.2
2.3.3
Visual Interactive Simulation .............................................................................. 40
Discrete Event Simulation ................................................................................... 40
Continuous Simulations ...................................................................................... 40
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2.3.4
2.3.5
2.3.6
2.3.7
3.
Differences Between Continuous and Discrete Simulations. .............................. 41
Hybrid Simulation ............................................................................................... 41
Simulation Software ............................................................................................ 41
Witness simulation software ............................................................................... 42
A BRIEF HISTORY ABOUT COFFEE. ...................................................................................43
3.1.1
3.1.2
3.1.3
3.1.4
3.1.5
3.1.6
Coffee Production ............................................................................................... 44
Planting ............................................................................................................... 45
Harvesting the Cherries ...................................................................................... 45
Processing the Cherries ...................................................................................... 46
Drying the Beans ................................................................................................. 47
Milling the Beans ................................................................................................ 47
CASE OF STUDY ..................................................................................................................................49
4. USE OF MODELLING FOR IMPROVEMENT OF A BUSINESS PROCESS IN A COFFEE
FARM. ................................................................................................................................................49
4.1
DEFINE THE PROBLEM ............................................................................................. 49
4.1.1 Objectives of the study ........................................................................................ 49
4.1.2 Issues to be addressed ......................................................................................... 49
4.1.3 Domain of the study ............................................................................................ 50
4.1.4 Level of detail ...................................................................................................... 50
4.1.5 The need to model the process ............................................................................ 50
4.2
DESIGN THE STUDY.................................................................................................. 50
4.2.1 Model assumptions.............................................................................................. 50
4.2.2 Number of models Required ................................................................................ 51
4.2.3 Animation, tool and model requested ................................................................. 51
4.2.4 Business process Description ............................................................................. 52
4.2.5 Start-up conditions and model run length .......................................................... 54
4.2.6 Model input data ................................................................................................. 54
4.2.7 Model output ....................................................................................................... 54
4.3
BUILD, VERIFY, VALIDATE AND ANALYSIS THE MODEL .......................................... 55
4.3.1 Model verification and validation ....................................................................... 55
4.3.2 Current situation of the model ............................................................................ 57
4.3.3 Conclusions of the model and the base simulation. ............................................ 58
4.4
EXPERIMENTS WITH THE MODEL AND OPTIMIZATION ............................................. 60
4.4.1 Base system + a new drying machine ................................................................. 60
4.4.2 Base Model + 1 Solar Dryer ............................................................................... 64
4.4.3 Base model + Reduction in production .............................................................. 66
4.4.4 Base Model redistribution ................................................................................... 68
4.5
RESULTS AND CONCLUSIONS ................................................................................... 70
CONCLUSION .......................................................................................................................................71
LIST OF APPENDICES ........................................................................................................................73
APPENDIX A BASIC ELEMENTS ON WITNESS ..................................................................... 75
APPENDIX B PRODUCTION ACCORDING WITH THE MODEL .................................................. 79
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WORKS CITED .....................................................................................................................................81
TABLES ..................................................................................................................................................85
FIGURES ................................................................................................................................................86
ABBREVIATIONS.................................................................................................................................87
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Introduction
It is well known to everyone that one of the most important products of Colombia is coffee, this
has been a great source of foreign exchange earnings for the country and source of employment
for many Colombians in rural areas. Although it is a profitable business, its level of technology
and industrialization is very low, resulting in low yields, loss of product quality due to delays
in some crucial processes or production interruptions.
On the other hand, we have the Business Process Management, a system focused on improving
and refining any process developed to produce a good or provide a service, through data
collection and implementation of simulations help find the key points in the production chain
and the bottlenecks that are currently generated in it.
The objective of this thesis is take and combine this system of improvements and apply them
to the production of coffee, will be taken from the grain is harvested until the product is ready
for commercialization, finding the bottlenecks that are currently being generated, analyse and
give a range of solutions that help improve productivity.
All the data is taken from “Villa Fran Farm”, located in the municipality of Confines, in the
department of Santander. The company employs a workforce close to 30 people, uses 2 solar
dryers, a skin remove machine, a washing area, an auxiliary drying machine and a packing
machine. Its level of modernization is in the average of the region (medium producers) and the
production is close to 40 tons of coffee (dry unroasted) quarterly.
The main software we will use is Witness, a powerful simulation tool that fed with all data
collection, processing time and drying, among other factors, will allow us to clarify faults that
arise, bottlenecks and modify convenience until a possible solution is found.
I am very pleasant to work on this topic, because it is a field that I know a lot, although it is not
a great manufacturing, I have found many critical processes that can be improved, besides it is
a family business that probably assumes in the near future. Another factor that draws attention
is that the same simulation can be applied to other local crops with great prospects such as
sugarcane (where biofuel is extracted) and cocoa.
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Theoretical Background
This chapter is very interesting, here we will choose the theories to be used and how we will
apply them to achieve the objectives set to improve the efficiency and quality of coffee
production. Although an agro-industrial company is not far from the processes used in large
manufactures, we will take the concepts, several examples and take them to the crops, farmers,
and their traditional methods.
An elaboration of the various theoretical topics involved in our research will be provided in this
chapter. The topics we will discuss are business process management, business process
modelling, Witness and others simulations systems, Technical languages conceptual modelling,
abstraction, design principles.
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1. Business Process Management
Business process management (BPM) is a systematic approach towards the definition,
execution, management and refinement of business processes. A business process is a collection
of activities or tasks that produce a specific service or product, generally involving both human
interaction and computer applications. (Panagacos, 2012)
For this research, we define Business Process Management as following:
Table 1 Business Process Management Definitions
Source (Elias, 2015)
These concepts are adapting to all the processes that we are going to analyse in this work, takes
into account the human factor (very important in coffee production), as well as other sources of
information as external factors, for example, the increasing frequency of certain climate and
meteorological phenomena, the lack of labour in rural areas for harvesting, and other delays
that the artisan processes generate and affect the productivity and quality of the final product.
The business process has been marked by a shift from the view of organizations as a collection
of departments with separate functions and outputs, to a view of them as systems of interlinked
processes that cross functions and link organizational activities (James W. Dean, 1994).
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Processes are collections of activities that, taken together, produce outputs for customers
(Garvin, 1988). Customers include not only external consumers of the organization’s products
or services, but also a series of internal recipients at linkage points between processes, as
outputs from upstream processes become the inputs for subsequent processes. Although
programs and awards like TQM, ISO 9000, Six Sigma, the Malcolm Baldrige Award, and
Business Process Management differ in scope and approach, they share a core focus on
measuring, improving, and rationalizing organizational processes (Harrington, 1997) (Lai,
2013) (Mary J. Benner, 2001)
Examples of BPM areas where remarkable progress has been made include:
•
The syntactic verification of complex business process models before putting them into
production, to avoid potentially costly mistakes. (Aalst W. M., Business Process
Management Don’t forget to improve the process, 2004)
•
The systematic identification of typical process behaviors based on scientific insights
provided by the Workflow Patterns initiative. (Aalst W. M., Business Process
Management Don’t forget to improve the process, 2004)
•
The automatic creation of configurable process models from a collection of process
model variants, used to guide analysts in selecting the right configuration. (Aalst W. M.,
Business Process Management Don’t forget to improve the process, 2004)
•
The automatic execution of business process models based on a rigorously- defined
semantics, and through a variety of BPM systems. (Aalst W. M., Business Process
Management Don’t forget to improve the process, 2004)
•
The adaptation of processes on-the-fly and the evaluation of the impact of their changes,
in order to react to (unexpected) exceptions. (Aalst W. M., Business Process
Management Don’t forget to improve the process, 2004)
•
The automatic discovery of process models from raw event data produced by common
information systems found in organizations. (Aalst W. M., Business Process
Management Don’t forget to improve the process, 2004)
Technological innovation is a central engine of organizational adaptation. As such, to
understand how process management techniques affect organizational adaptation (Mary J.
Benner, 2001), we first address how it affects exploratory as well as exploitative innovation.
A firm’s ability to innovate provides its senior team with options to either reinforce or
destabilize a technological regime (Burgelman, 1983) (McGrath, 2007).
Innovations generated within or absorbed by firms provide variation to proactively
shape or reactively respond to technological transitions (Mary J. Benner, 2001)
An organization’s dynamic capabilities depend on simultaneously exploiting current
technologies and resources to secure efficiency benefits and on creating variation
through exploratory innovation (Nelis, 2008)
As process management techniques focus on continuous improvement in routines and
variation reduction their increased utilization in an organization affects the balance
between exploratory and exploitative innovation. (Garvin, 1988).
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1.1 BPM Foundation
Business process management (BPM) is rooted in both management science and computer
science as a result, it is difficult to pinpoint its starting point.
Business Process Management (BPM) has various roots in both computer science and
management science. Therefore, it is difficult to pinpoint the starting point of BPM. Since the
industrial revolution, productivity has been increasing because of technical innovations,
improvements in the organization of work, and the use of information technology. Adam Smith
(1723–1790) showed the advantages of the division of labour. Frederick Taylor (1856–1915)
introduced the initial principles of scientific management. Henry Ford (1863–1947) introduced
the production line for the mass production of “black T-Fords.” It is easy to see that these ideas
are used in today’s BPM systems. (Aalst W. M., 2013).
In the last century many process modeling techniques have been proposed. In fact, the wellknown Turing machine described by Alan Turing (1912–1954) can be viewed as a process
model. It was instrumental in showing that many questions in computer science are undecidable.
Moreover, it added a data component (the tape) to earlier transition systems. Petri nets play an
even more prominent role in BPM as they are graphical and able to model concurrency. In fact,
most of the contemporary BPM notations and systems use token-based semantics adopted from
Petri nets. Petri nets were proposed by Carl Adam Petri (1926–2010) in 1962. This was the first
formalism treating concurrency as a first-class citizen. Concurrency is very important as in
business processes many things may happen in parallel. Many cases may be handled at the same
time and even within a case there may be various enabled or concurrently running activities.
Therefore, a BPM system should support concurrency natively. (Aalst W. M., 2013)
A good starting point for establishing the road to BPM is from a management concept referred
to as business process re-engineering (BPR) In this concept a new way of organizing companies
on the basis of business processes was proposed. Following their campaign for the radical
redesign of business processes, many companies initiated BPR projects to review and redesign
their processes. However, in the late 1990s, the interest in BPR deteriorated and many
enterprises terminated their BPR projects and stopped supporting further BPR initiatives.
The emergence of BPM can be seen from two key renewed ideas behind BPR (Dumas, 2013).
The first is the revelations from the empirical studies, which showed that process-oriented
organizations did better than non-process-oriented organizations (Dumas, 2013). As a result of
the confirmation of this picture by the follow-up studies, the credibility of the process-oriented
concept was renewed. (Elias, 2015)
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Figure 1 Historic view on information systems’ development illustrating that BPM systems can be used to push “process
logic” out of the application
Source (Aalst W. M., Process Mining Data Science in Action, 2016; Aalst W. M., 2013)
The second is the technological development. Following BPR in the late 1990s, enterprise
resource planning (ERP) systems and workflow management (WFM) systems gained
organizational focus ERP is a business management software, usually a suite of integrated
applications, used for managing the business and automating the back office functions related
to services, technology and human resources. It integrates all components of an operation,
ranging from product planning, development and manufacturing to marketing and sales. On the
other hand, WFM systems are systems that distribute work to various actors in a company on
the basis of process models (Dumas, 2013) (Elias, 2015)
BPM can be perceived as an extension of workflow management (WFM). While the primary
focus of WFM is to automate business processes, the scope of BPM is broader – ranging from
process automation and analysis to organization of work and operations management. On the
one hand, BPM is aimed at improving business processes, perhaps without the use of
technologies (Elias, 2015) For example, management may get ideas on how to reduce costs by
modelling and analysing a business process using simulation.
1.2 BPM Contribution
One promising direction to better link BPM to the concrete improvement of process KPIs lies
in exploiting event data present in the organization. For example, Six Sigma (PYZDEK, 2003)
has for long applied statistical analysis tools to organizational data in order to measure and
reduce the degree of business process variability. The idea is to identify and remove the causes
for such variability, e.g. in terms of errors, defects or SLA violations in the output of business
processes, and to control that such processes effectively perform within the desired performance
targets (e.g. ensuring that there are no more than 10 SLAs per month). However, while Six
Sigma is focused on improving business processes by statistically quantifying process
performance changes, the data used for such analyses is typically collected manually, e.g.
through surveys or observation. This makes the employment of such techniques, when carried
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out properly, very costly and time consuming. Moreover, Six Sigma rarely looks inside end-toend processes. The focus is on a specific step in the process or on aggregate measures. (Aalst
W. M., Business Process Management Don’t forget to improve the process, 2004)
This problem can be obviated through the use of techniques that automatically extract process
knowledge from event data logged by common information systems, e.g. ERP or ticketing
systems. In this context, process mining (Aalst W. M., Process Mining Data Science in Action,
2016) has emerged as a range of methods and tools for exploiting such data to automatically
discover a process model, or check its compliance with existing reference models or norms, or
to determine the causes for process deviations or variants. The advantage of relying on logged
data as opposed to data that has been collected manually is that any insight extracted from this
data is based on evidence, rather than on human confidence, and thus is a more accurate
representation of reality. Moreover, the artefacts extracted through process mining, e.g. process
models, can be enhanced with (live) process performance information such as statistics on
activity duration and resource utilization. This allows organizations to look inside end-to-end
processes. For these reasons, process mining methods are now being used across all phases of
the BPM lifecycle, from discovery through to monitoring. However, while a wide range of
techniques have been developed in this field, the research community has mostly devoted its
attention to the quality of the artefacts produced (e.g. the accuracy of the process models
extracted from the logs), rather than to improving the actual processes for which such logs are
available. (Aalst W. M., Business Process Management Don’t forget to improve the process,
2004)
Therefore, a possible research direction is to bridge the current gap between process mining
and Six Sigma. For instance, process mining techniques could be used to extract detailed and
accurate process performance measurements (e.g. in the form of process models enhanced with
performance statistics) on top of which Six Sigma techniques could be applied to pinpoint
causes for variability, and to identify and evaluate the impact of different process changes on
the process KPIs. (Aalst W. M., Business Process Management Don’t forget to improve the
process, 2004)
Another avenue to obtain better processes consists in applying techniques from Operations
Research to the realm of business processes. Operations Research (OR) is a well-established
research area that aims to solve complex decision-making problems by employing a variety of
mathematical techniques, such as simulation, queuing theory, optimization and statistics. Many
process improvement problems can in fact be traced back into typical problems investigated by
OR, since there are typically a number of constraints and options making it hard to find optimal
solutions. In a way, the goal is to optimize a process according to given KPIs (typically time
and resources usage).
1.3 Definition of terms used in BPM
1.3.1
Achievement
Realizing the strategic objectives as outlined in the organization’s strategic plan. At a project
level, it is about realizing the value or business benefits as outlined in the project business case.
(Nelis, 2008)
Apply the BPM model in order to reach a mayor for the production of coffee in the analysed
farm, and to be precedent to be applied in any other agro-industry process.
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1.3.2
Organization
The organization in this context refers to an enterprise or parts of an enterprise, perhaps a
business unit that is discrete in its own right. It is the end-to-end business processes associated
with this part of an organization. This end-to-end focus will ensure that a silo approach does
not develop. (Nelis, 2008)
The organization in this context is the productive unit (Labour force, Machines and crops) that
harvests coffee, and transforms the grain to be ready for commercialization and subsequent
export.
1.3.3
Objectives
The objectives of a BPM implementation range from the strategic goals of the organization
through to the individual process goals. It is about achieving the business outcomes or
objectives. BPM is not an objective in itself, but rather a means to achieving an objective. It is
not ‘a solution looking for a problem’. (Nelis, 2008)
1.3.4
Improvement
Improvement is about making the business processes more efficient and effective. (Nelis, 2008).
Which processes should we improve? We are facing a process that has been doing the same
way for many years, the lack of improvements in the processes that go from the collection of
coffee and its various traditional techniques have affected productivity and efficiency, here it is
important to look to other regions (Brazil or Vietnam, for example) where improvements in
their processes have led to their being the first and second largest producer in the world,
displacing Colombia to the third place.
1.3.5
Management
Management refers to the process and people performance measurement and management. It is
about organizing all the essential components and subcomponents for your processes. By this
we mean arranging the people, their skills, motivation, performance measures, rewards, the
processes themselves and the structure and systems necessary to support a process. (Nelis, 2008)
In reference to this process will be measured and managed the ideal performance to be achieved
by each person who is harvesting the grain (process that uses most of the labour) and other links
in the production chain. This point will be the central axis of the practical part of this work.
Proponents of process management have long cited the expected benefits of these practices.
Non-value-added activities are removed as processes are streamlined, resulting in reduced costs
and efficiency improvements in the form of increased yields and less rework and waste. Tighter
intra- organizational linkages increase efficiency by streamlining the handoffs between
activities, and speeding development and delivery times (Garvin, 1988) (Dean, 2006). Further,
these products are likely to better satisfy customers, leading to increases in revenues. Ultimately,
as revenues increase and costs decrease, profits are expected to improve. (Mary J. Benner, 2001)
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1.3.6
Control
BPM is about managing your end-to-end business processes and involves the full cycle of plan–
do–check–act (Deming circle, Walton, 1986). An essential component of control is to have the
ability to measure correctly. If you cannot measure something, you cannot control and manage
it. (Burlton, 2001)
The simulation should be done with as many facts as possible to be taken into account in the
production process, it will include climatic factors (which directly affects the drying of the
product) as well as the labour shortages that are generated in Some seasons, the difficulty of the
ground to reach the collection center, usually damages to the machines, etc.
1.3.7
Essential processes
Process management is composed of three main practices: mapping processes, improving
processes, and adhering to systems of improved processes. Once underlying processes have
been recorded through process mapping, process improvement involves developing measures
of how well a process meets customer requirements, and using statistical methods to continually
eliminate variation in processes and outputs (Wageman, 1995) (Mikel J. Harry, 2006). Process
improvement not only involves rationalizing individual work processes, but also streamlining
the handoffs between processes (Harrington, 1997) (Garvin, 1988). Organizational participants
are trained in effective ways to facilitate cross-functional team meetings and learn standard
approaches for identifying and solving problems. These tools, by design and intent, help
integrate and coordinate a broad set of activities throughout the organization (Repenning, 1999)
(Mary J. Benner, 2001)
The last element of process management is adherence to processes that have been mapped and
improved. This ensures that processes are repeatable, allowing for ongoing incremental
improvement, and the realization of benefits of improvement efforts.
As the one responsible for managing resources - funding, facilities, and personnel – the
producer makes a significant commitment to the improvement process and wants to ensure that
this investment is worthwhile. One way to do this is to focus on improving essential processes
- those which are key to the functioning, products, or services of the work unit. Not every
process in an organization contributes towards the achievement of the organization’s strategic
objectives. Essential processes are the ones that do (Burlton, 2001).
1.3.8
Processes
What is a process? There are as many definitions of process as there are processes. One we
agree with is Roger Burlton’s, where he says that ‘a true process comprises all the things we
do to provide someone who cares with what they expect to receive’ (Burlton, 2001). This
covers a true end-to-end process, from the original trigger for the process to the ultimate
stakeholder satisfaction. Burlton adds that the ‘final test of a process’s completeness is
whether the process delivers a clear product or service to an external stakeholder or another
internal process’. (Nelis, 2008)
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1.4 Business Process Management Lifecycle
Like all processes, we have to take into account their development phases, from the beginning
until they reach a level of maturity that allows the system to be feedback and improve
continuously, with this project starts from zero, we found some studies in the field of the coffee,
but there are few and are not extendable to all farms, either by location, availability of labour,
level of modernization and a long etcetera. Then we will see each of its stages and how they
relate to our work, its importance and the necessary way that has been done to get there.
All BPM activities can be attributed to one of the five phases of the BPM lifecycle:
1
Figure 2 BPM lifecycle
Source (Dumas, 2013)
1.4.1
Design phase:
Capture the business processes at a high level, typical information required for the identification
of business processes are tasks, quantifiable deliverables (e.g. documents), responsibilities,
computer systems and required resources.
1
The five phases of the BPM life-cycle shown in figure 2 form a never-ending loop, known as the
continuous process improvement cycle.
28
Gather just enough detail to understand conceptually how the process works and concentrate
on ensuring the high level detail is correct without being distracted by the detail of how it’s
going to be implemented.
1.4.2
Modeling phase
During which the information gathered during the design phase is made explicit in a business
process model. These models are usually created with elaborate modelling tools, using a
standard for business process modelling, such as the Business Process Modelling Notation
(BPMN) and subsequently stored in a so-called modelling repository. (Aguilar-Saven, 2003)
1.4.3
Execution phase
During which computer applications are deployed to support the automation of the business
process. In traditional organizations, there are many computer applications that each perform a
specific task. These applications are not unaware of each other’s existence nor are they aware
of the context in which they are used. Business process orchestration attempts to connect these
applications by a main application, which is aware of the business process [6]. In addition, more
recent developments of BPM technology aim to use the business process model itself as a basis
for automation. By formalizing the business process model, it can be interpreted and
subsequently executed by a so-called business process engine. This approach allows computers
to be aware of the actual business process as it is executed, which in turn enables a variety of
advantages to the business. (Aguilar-Saven, 2003)
1.4.4
Monitoring phase
During which the performance of the implemented business processes is measured. The depth
of the analysis depends on what BPM technologies were implemented during the execution
phase. When a feature rich BPMS is used, one can measure process performance both at the
global level and at the instance level. This information can subsequently be aggregated and
displayed in comprehensive monitoring dashboards, which give managers quantifiable real-life
data of the performance of their business. When this degree of rigor is employed during the
monitoring phase, the act of monitoring becomes a goal by itself, which is also known as
business activity monitoring. (Aguilar-Saven, 2003)
1.4.5
Optimization phase
During which business processes are optimized based on the findings of the monitoring phase.
These optimizations may lead to the redesign of existing business processes or the design of
additional business processes. As a result, the optimization phase can be the initiator of a new
design phase, thus completing the BPM life-cycle. (Aguilar-Saven, 2003)
29
30
2. Simulations Projects
Simulating is to use a model to calculate and provide a certain output variable in order to
produce similar evolutions in the presence of identical stimuli. In a simulation it is necessary to
know the value of the inputs (manipulability or not), since, what is tried to calculate is the value
of the outputs that depend on them. To perform the simulation, a model, an initial situation and
the value of the inputs are provided at each subsequent time point (Arahal M., 2006)
The industry is forced to build costly pilot plants to simulate and test the behaviour of new
processes to later apply the knowledge acquired in the actual plant. This simulation is often
complicated and costly, and sometimes imprecise due to changes in scale between the pilot
plant and the actual plant; Hence the need to develop computer simulations of industrial plants.
Simulation is a working tool that was developed in parallel with the appearance of the computer
and that, little by little, has been imposed thanks to the greater speed and capacity that
computers have been offering to the industry.
Simulation is one method of determining the feasibility and efficiency of the proposed
redesigned process options. Simulation can also be used to test the logic and consistency of
processes before their implementation. It requires a significant amount of effort, and should not
be underestimated or undertaken lightly. There will be a need to gather the necessary metrics
and assumptions to run the simulations. Simulation is a good method to test the various
scenarios. (Nelis, 2008).
Having in our hands a tool like the simulation, we will question all possible scenarios that may
arise, such as rains that do not allow the correct drying in the sun, labour shortages, breakdowns
in crucial machines and the Impact its repair has on production. This multiplicity of scenarios
allows us to anticipate any problem and make better decisions based on simulated results.
(Banks, 1998)
The simulated ‘runs’ should then be evaluated and, ultimately, activity based costing and
capacity planning estimates completed. This will assist in the determination of performance
measurement for the process options. (Nelis, 2008)
31
2.1 Computing Simulation
A computer simulation is a program that is run on a computer and that uses step-by-step
methods to explore the approximate behaviour of a mathematical model. Usually this is a model
of a real-world system (although the system in question might be an imaginary or hypothetical
one). Such a computer program is a computer simulation model. One run of the program on the
computer is a computer simulation of the system. The algorithm takes as its input a specification
of the system's state (the value of all of its variables) at some time t. It then calculates the
system's state at time t+1. From the values characterizing that second state, it then calculates
the system's state at time t+2, and so on. When run on a computer, the algorithm thus produces
a numerical picture of the evolution of the system's state, as it is conceptualized in the model.
(Zalta, 2015)
This sequence of values for the model variables can be saved as a large collection of “data” and
is often viewed on a computer screen using methods of visualization. Often, but certainly not
always, the methods of visualization are designed to mimic the output of some scientific
instrument—so that the simulation appears to be measuring a system of interest. (Waller, 2012)
Two types of computer simulation are often distinguished: equation-based simulations
(Equation-based simulations are most commonly used in the physical sciences and other
sciences where there is governing theory that can guide the construction of mathematical
models based on differential equations (Zalta, 2015)) and agent-based simulations (These are
most common in the social and behavioural sciences, though we also find them in such
disciplines as artificial life, epidemiology, ecology, and any discipline in which the networked
interaction of many individuals is being studied (Zalta, 2015)). Computer Simulations of both
types are used for three different general sorts of purposes: prediction (both pointwise and
global/qualitative), understanding, and exploratory or heuristic purposes. (Zalta, 2015)
32
2.2 Conducting a Simulation Project
Projects that involve simulation have several unique aspects which must be managed
particularly carefully to ensure their success. The topics in this chapter outline a typical
sequence of events in a project, using a practical methodology:
6Figure 3 Systematic simulation approach.
Source. AI-Aomar, R. (2006). Reproduced with permission of John Wiley & Sons, Inc.
2.2.1
Identify the Problem
Any simulation study should start by defining the problem to be solved and improvement
opportunity to be explored. System design challenges, operational problems, and improvement
opportunities are the main categories of simulation problems. The simulation problem is
defined in terms of study scope, study objectives, and model assumptions.
The study scope includes a clear description of the system problem or opportunity, the overall
goal of simulation, the challenges, limitations, and issues with the current state. It starts by
describing the system structure, logic, and functionality. The problem can be structured using a
schematic diagram or process map to make it easy for the analyst to understand different aspects
of the problem. The problem statement includes a description of the situation or the system of
the study and the problem that needs to be solved. Formulating the problem in terms of an
overall goal and a set of constraints provides a better representation of the problem statement.
A thorough understanding of the elements and structure of the system under study often helps
in developing the problem statement. (Raid Al-Aomar, 2015)
2.2.1.1 Establish Objectives
This is the first and most important phase of any simulation project. The aim of any simulation
project should be to make a better business decision. You, as simulation modeller, must
33
understand this business decision as it is likely to have important implications for the content
of your simulation model. (Lanner Group Ltd, 2009)
The current objectives are to improve production, efficiency in the use of resources (especially
human capital) and, therefore, the quality of the grain; Through the use of simulations we will
find on paper the possible solutions to the problems that affect the production of coffee today.
2.2.1.2 Scope and Level of Model Detail
The scope of a simulation model refers to where it begins and where it ends. It is important to
limit the scope of the model as far as possible. With regard to the level of detail contained within
a model, the golden rule is to model the minimum necessary in order to achieve the model's
objective. (Lanner Group Ltd, 2009)
Our simulation starts from the time the coffee beans are harvested until the product is packed
and ready for transport and subsequent export, are the processes on which we have direct action
and which can impact the production, in addition they are the ideal ones to measure and take
corrective action if necessary as specified in the results of such simulations.
2.2.2
Design the study
Based on the problem formulation, a set of objectives can be assigned to the simulation study.
Such objectives represent the criteria through which the overall goal of the study is achieved.
Study objectives simply indicate questions that should be answered by the simulation study.
Examples include testing design alternatives for plant operations, studying the effects of using
trucks rather than rail system in a supply chain and so on.
Specifying study objectives serves various purposes. First. we can decide if simulation is the
right tool to solve the underlying problem. Do we have enough data to determine metrics for
the defined objectives? Can we use analytical methods to answer the questions raised? Is the
software tool capable of presenting and analyzing study requirements so that the study
objectives are achieved? Such questions can be better answered by unambiguously stating the
objectives of the simulation study. (Raid Al-Aomar, 2015)
2.2.3
Design the conceptual Model
An important final step before building the simulation model is to structure it. This will identify
the most difficult areas for the model building and highlight any additional data requirements
that may have been overlooked up to now, such as a transfer time for parts between processes.
(Lanner Group Ltd, 2009)
Developing a conceptual model is the process through which the modeler abstracts the structure,
functionality, and essential features of the real-world system into a structural and logical
representation that is transferable into a simulation model. The model concept can be a simple
or complex graphical representation such as a block diagram, a flowchart, or a process map that
depicts key characteristics of the simulated system such as inputs, elements, parameters, logic,
flow, and outputs. The conceptual model should be eventually programmable and transferable
into a simulation model using available simulation software tools. Thus, a successful model
concept is one that takes into consideration the method of transferring each abstracted
34
characteristics, building each model element, and programming the conceptual logic using the
software tool. (Raid Al-Aomar, 2015)
The model concept is developed taking into consideration the formulated problem and the
objectives of the simulation study. Study objective is the main factor that facilitates the structure
of the conceptual model and the level of detail to be used in the model.
The art of conceptual modeling also requires system knowledge and model building skills. The
modeler starts by establishing a thorough understanding of the simulated system whether it is a
new system or an existing one. The modeler studies system inputs, elements, structure, logic,
flow, and outputs and abstracts the overall structure and the interrelationships of structure
elements into a conceptual model. (Raid Al-Aomar, 2015)
2.2.4
Formulate inputs, Assumptions and Process definitions
Simulation models are data-driven computer programs that receive input data, execute a
designed logic, and produce certain outputs. Hence, data collection step is a key component of
any simulation study. Simulation data can be, however, collected in parallel to building the
model using the simulation software. This is recommended since data collection may be timeconsuming in some cases and building the model structure and designing model logic can be
independent of model data. Default parameters and generic data can be used initially until the
system data is collected.
The quality of data used in the model drives the overall model quality and validity. It also
impacts the accuracy of the model results and collected statistics.
Data elements required for constructing a simulation model are often determined based on the
model concept, model structure, and the nature and type of model outcomes and statistics to be
collected. For example, in our model the main input is the coffee beans that arrive every day
and are unloaded into the tank before being processed by the milling machine.
Depending on the nature of the simulation study, model data is collected by reviewing historical
data, observing and monitoring system operations, or using benchmark data assumptions. (Raid
Al-Aomar, 2015)
Figure 4 Collecting Model data
Source (Raid Al-Aomar, 2015)
35
Data collection approaches for qualitative research usually involves:
•
Direct interaction with individuals on a one to one basis
•
Or direct interaction with individuals in a group setting
Qualitative research data collection methods are time consuming, therefore data is usually
collected from a smaller sample than would be the case for quantitative approaches - therefore
this makes qualitative research more expensive.
The benefits of the qualitative approach are that the information is richer and has a deeper
insight into the phenomenon under study The main methods for collecting qualitative data are:
Individual interviews: Qualitative interviews should be fairly informal and participants feel
they are taking part in a conversation or discussion rather than in a formal question and answer
situation.
There is skill required and involved in successful qualitative research approaches - which
requires careful consideration and planning. A good quality qualitative research involves
Thought, Preparation, the development of the interview schedule, Conducting and analysing
the interview data with care and consideration, (University of Leicester, 2015)
Focus groups: The use of focus groups is sometimes used when it is better to obtain
information from a group rather than individuals. Group interviews can be used when Limited
resources (time, manpower, finances), The phenomena being researched requires a collective
discussion in order to understand the circumstances, behaviour or opinions and Greater insights
may be developed of the group dynamic - or cause and consequence. (University of Leicester,
2015)
Observations: Observation involves may take place in natural settings and involve the
researcher taking lengthy and descriptive notes of what is happening. It is argued that there are
limits to the situations that can be observed in their 'natural' settings and that the presence of
the research may lead to problems with validity. (University of Leicester, 2015)
Action Research: doesn't just involve asking about it, it involves doing it. Action Research is
a framework that is Collaborative, there is a practical intervention made - i.e. you do something
to make a change or intervention in a situation that you research and The researcher will be
actively involved in the planned intervention
Information for a model is likely to fall into one of three categories:
•
Available - data is readily available and it is in an appropriate format that the model can
use immediately.
•
Not available but collectable - data is either in an incorrect format or it has not been
collated before. You might need to perform a small work study in order to collect this
type of data (for example, timing certain processes manually). (Lanner Group Ltd, 2009)
•
Neither available nor collectable - data is not currently available and it is not easily
collectable (for example, for a model of a new factory on a green-field site with new
machinery). If the data is neither available nor collectable, you must use estimates.
(Lanner Group Ltd, 2009)
36
We will take the majority of data through observation, we will take the average harvesting time,
transport, data of the efficiencies of the machines, the average time of drying, among others
explained with more precision in the practical part of this work.
2.2.5
Build, Verify and Validate the model
It is recommended that you build the model incrementally, and that you test each stage
thoroughly before you build the next stage. If you do this, it is easier to find problems in a
model than if you have to search through an entire model. This ability to build a model
incrementally, testing each section as you go, is a powerful aid to productivity, and generates
confidence in the validity of your model. (Lanner Group Ltd, 2009)
Data collection and model building often consume the majority of the time required for
completing a simulation study. To reduce this time, the modeller should start building the
simulation model while data is being collected. The conceptual model can be used to construct
the computer model using assumed data until the collected data become available. The overlap
between model building and data collection does not impact the logical sequence of the
simulation procedure. Constructing model components, flow of entities, and logic depends
mostly on the model concept and in most cases is independent of model data. Once the model
is ready, model input data and parameter settings can then be inserted into the model. Also,
since a large portion of a simulation study is often spent in collecting model data, building the
model simultaneously reduces significantly the overall duration of the simulation study and
provides more time for model analysis and experimentation. (Raid Al-Aomar, 2015)
There is no standard procedure for building a simulation model. The procedure is often based
on the modeller’s approach and on the simulation software tool used.
2.2.5.1 Verification and validation
Model verification is the quality control check that is applied to the built simulation model.
Like any other computer program, the simulation model should perform based on the intended
logical design used in building the model. Although model logic can be defined using different
methods and can be implemented using different programming techniques, the execution of the
logic when running the model should reflect the way the programmer or the modeller has
initially designed. Different methods are, therefore, used for debugging logical (programming)
errors as well as errors in inputting data and setting model parameters. Corrected potential code
and data discrepancies should always be verified by carefully observing the changes in model
behaviour. (Raid Al-Aomar, 2015)
Verification is the task of determining if the implementation of a model has been done correctly,
ensures that the content of the model is consistent with your expectations. this means that
verification data needs to be generated at various points in the model for comparison with
expected values. A good example is the output of mean interarrival times at points matching
those from stability analysis. Where differences are substantial, in all likelihood, part of the
model implementation has an error. For example, establish that the parts are travelling along
the correct routes between elements and that any labour used is attending to the correct elements
in the correct priority order. (Lanner Group Ltd, 2009)
37
Model validation is the process of checking the accuracy of the built model representation to
the simulated real-world system. It is simply about answering the following question: does the
model behave similarly to the simulated system? Since the model will be used to replace the
actual system in experimental design and performance analysis, can we rely in its representation
of the actual system? (Raid Al-Aomar, 2015)
Validation (which usually follows verification) investigates the accuracy of the model
compared with the real world. A typical validation exercise might involve providing a typical
set of inputs (for example, a part arrival and production schedule) and studying a set of model
outputs (for example, the average level of work-in-progress for a part, or part throughput times).
(Lanner Group Ltd, 2009)
2.2.5.2 Running the Model
After defining, displaying and detailing the elements of your model, you can run it immediately,
then modify it by adding, changing or deleting elements. You can then run the model again in
order to assess the impact of these changes.
38
2.2.6
Experimentation and Optimization
Table 2 Experimentation and optimization
Model Validation
Method
Comparison to Other
Models
Face Validity
Historical Data
Validation
Parameter Variability
– Sensitivity Analysis
Predictive Validation
Description
Various results (e.g., outputs) of the simulation model being validated
are compared to results of other (valid) models. For example, (1)
simple cases of a simulation model are compared to known results of
analytic models and (2) the simulation model is compared to other
simulation models that have been validated.
Asking individuals knowledgeable about the system whether the
model and/or its behaviour are reasonable. For example, is the logic in
the conceptual model correct and are the model’s input-output
relationships reasonable?
If historical data exist (e.g., data collected on a system specifically for
building and testing a model), part of the data are used to build the
model and the remaining data are used to determine (test) whether the
model behaves as the system does.
This technique consists of changing the values of the input and internal
parameters of a model to determine the effect on the model’s
behaviour of output. The same relations should occur in the model as
in the real system. This technique can be used qualitatively—
directions only of outputs—and quantitatively—both directions and
(precise) magnitudes of outputs. Those parameters that are sensitive
(i.e., cause significant changes in the model’s behaviour or output)
should be made sufficiently accurate prior to using the model.
The model is used to predict (forecast) the system’s behaviour, and
then the system’s behaviour and the model’s forecast are compared to
determine if they are the same. The system’s data may come from an
operational system or be obtained by conducting experiments on the
system, e.g., field tests.
Source Author
When you are satisfied that the model resembles the behaviour of the real-life situation, you
can investigate a number of what-if scenarios. The scenarios should have been defined within
the original objectives of the simulation study.
Successful experimentation typically involves using a warm-up period or starting conditions,
deciding on a suitable run-length, and running the model with more than one random number
stream. (Lanner Group Ltd, 2009)
2.2.7
Presentation of Results and Implementation
The method of presentation for results depends on the size of the simulation project and the
culture of your organization. An animated model provides an effective communication tool to
support business decisions, particularly if you have enhanced its graphical display. (Lanner
Group Ltd, 2009)
39
2.3 Simulation
Simulation has much to offer all organizations, whether they are in manufacturing or in the
service industries. The role of simulation is to evaluate practical alternatives available either in
support of major strategic initiatives which might involve a large financial outlay, or in support
of the continuous search for better performance at operational and tactical levels. Examples of
such evaluations include changes to the product mix, increases or decreases in volumes,
improvements in throughput, shorter lead times and improved customer response times.
Simulation provides the user with a greater breadth and depth of information on which to base
decisions: it is not an optimizing tool. It is capable of handling the complexity of large systems,
even a whole factory. In addition, the simulation approach supports sensitivity analysis by
allowing rapid changes to the model logic and data. (Lanner Group Ltd, 2009)
2.3.1
Visual Interactive Simulation
“Visual Interactive Simulation is one which has features for graphical creation of simulation
models, dynamic display of the simulated system and user interaction with the running program.
Interaction implies that the simulation halts and requests information from the user or the user
stops the simulation at will and interacts with the running program.” (Lanner Group Ltd, 2009)
2.3.2
Discrete Event Simulation
In discrete event simulation, the operation of a system is represented as a chronological
sequence of events. Each event occurs at an instant in time and marks a change of state in the
system. For example, an event could be "level 6 button pressed", with the resulting system state
of "lift moving" and eventually (unless one chooses to simulate the failure of the lift) "lift at
level 6". (Waller, 2012)
Table 3 Simulation terminology
Terminology
Element
State
Event
Meaning
A building block used to represent a real life ‘operation’ in the
simulation. For example a Call coming into a call center, a Press
in a car factory or a Clerk at a bank counter.
An element’s state is the condition that it is in at a specific time
e.g. it may be Busy, Idle or Blocked. There are also other states
that an element can take, that are detailed later.
An event occurs when an element changes its state. A change of
state is instantaneous i.e. it has a zero duration time.
Source (Lanner Group Ltd, 2009)
2.3.3
Continuous Simulations
Continuous simulators are characterized by the extensive use of mathematical formulae which
describe how a simulated component responds when subjected to various conditions. (Cellier,
1991), several equations which describe their respective behaviours. A continuous simulator
40
would apply those equations in the context of the components' environment and connectivity
and produce a continuous graph which accurately reflects how the components would react if
they were actually hooked up in reality.
2.3.4
Differences Between Continuous and Discrete Simulations.
Discrete event simulation is appropriate for systems whose state is discrete and changes at
particular time point and then remains in that state for some time. An example of such a system
is the number of farmers collecting coffee: The number of farmers is discrete (integer) and the
number of farmers only changes when someone enters or finishes its work at the farm.
Continuous simulation is appropriate for systems with a continuous state that changes
continuously over time. An example of such a systems is the amount of liquid in a tank and or
its temperature. Such a system can be described by differential equations. Continuous
simulation is a technique to solve these equations numerically.
2.3.5
Hybrid Simulation
Hybrid simulation deals with mixing discrete events and continuous variables. These tools
combine the features of continuous simulators and discrete simulators (i.e., they solve
differential equations, but can superimpose discrete events on the continuously varying system).
Very limited applications in the real-world are approached by hybrid simulation. These
application target systems with intermittent flow or when a portion of the flow has a delay or
wait time. Other examples include the case where it is possible to mix fluids with parts in the
discrete machine element. This feature is commonly used when filling vessels with fluid (paint
cans with paint, bottles with soft drinks, etc.) It is also commonly used when a fluid of some
type is being consumed at a machine that is processing discrete parts. In such situations, there
may be a coolant or lubricant being consumed during the machining of a part, or detergent or
other cleaning agent being consumed in a part clean-up stage.
Such systems can be modelled as either discrete event or continuous based on the level of detail
required. Discrete-event models provide much more details about the operation of a system
than continuous models. The ability of a commercial simulation tool to allow both continuous
and discrete entities provides powerful modelling tool. Here some elements are controlled by
differential equations others by events. For example, in a steel mill model there would be an
event for heating of steel to start, with temperature rise determined by differential equations
according to amount of power applied. (Raid Al-Aomar, 2015)
2.3.6
Simulation Software
Simulation software tools are common nowadays among analysts and engineers in various
types of applications and for different purposes. The major benefit simulation packages provide
to practitioners is the capability of dynamic system modelling under close-to-real-world
conditions. Processes of transactional nature in both manufacturing and services often behave
in a complex, dynamic, and stochastic manner. Such capability, therefore, allows analysts to
better express new concepts and designs for transactional processes, measure process
performance with time-based metrics, conduct statistical analyses to capture variability, and
improve/optimize new and current systems.
41
Indeed, many simulation software tools were initially built by practitioners to model some
particular real-world systems such as manufacturing systems, health-care systems, supply chain
systems. They were built from entities and processes that mimic the objects and activities in the
real system. To further meet the needs of practitioners and engineers, simulation vendors
developed and integrated modules into their software products to simplify model building,
facilitate model customization, allow for the creation of impressive animation, and enable
analysts to conduct statistical analyses and run optimization searches.
2.3.7
Witness simulation software
Figure 5 Witness Simulation Software
Source (Lanner Group Ltd, 2009)
WITNESS is Lanner Group’s simulation software package. Witness is a simulation tool for
dynamic process simulation of manufacturing and business processes in 2D or 3D models. With
these models real processes can be emulated already within the planning phase and used for
experiments.
Figure 6 Witness Simulation software tools
Source (Lanner Group Ltd, 2009)
The WITNESS Manufacturing Performance Edition is the version of WITNESS specially
designed for manufacturing applications. It is ideally suited to a variety of production and
storage layout and logistical modelling scenarios. (Lanner Group Ltd, 2009).
42
3. A brief history about coffee.
Colombia is the 2nd biggest coffee producer and the biggest producer of Arabica coffee, which
is considered the highest quality bean. Cultivation, processing, trading, transportation and
marketing of coffee provide employment to many people in Colombia. The major coffee
importing countries are the United States, Germany, Japan, Italy and other European countries.
There are 570,000 producers. The high quality is guaranteed even for the coffee which is
cultivated with shade trees. In recent years, Colombia is enthusiastic about organic coffee that
does not use chemical fertilizer or pesticides. Still, the unpredictable natural calamity such as
El Nino and imbalance on demand and supply in the market hit coffee industry in Colombia.
(American University , n.d.)
43
3.1.1
Coffee Production
Between the time they’re planted, picked and shipped, coffee beans go through a typical series
of steps: (Belgium Coffee Bar, 2016)
Figure 7Coffee Processing Flowchart
Source (Belgium Coffee Bar, 2016)
44
3.1.2
Planting
Figure 8 Coffee plant.
A coffee bean is actually a seed. When dried, roasted and
ground, it’s used to brew coffee. If the seed isn’t processed,
it can be planted and grow into a coffee tree.
Coffee seeds are generally planted in large beds in shaded
nurseries. The seedlings will be watered frequently and
shaded from bright sunlight until they are hearty enough to
be permanently planted. Planting often takes place during
the wet season, so that the soil remains moist while the
roots become firmly established. (National Coffee
Association of U.S.A, 2015)
Source Author
3.1.3
Harvesting the Cherries
Figure 9 Coffee beans
Depending on the variety, it will
take approximately 3 to 4 years for
the newly planted coffee trees to
bear fruit. The fruit, called the
coffee cherry, turns a bright, deep
red when it is ripe and ready to be
harvested.
There is typically one major
harvest a year. In countries like
Colombia, where there are two
flowerings annually, there is a main
and secondary crop.
In most countries, the crop is
picked by hand in a labourintensive and difficult process, Source Author
though in places like Brazil where
the landscape is relatively flat and the coffee fields immense, the process has been mechanized.
Whether by hand or by machine, all coffee is harvested in one of two ways:
•
Strip Picked: All of the cherries are stripped off of the branch at one time, either by
machine or by hand.
•
Selectively Picked: Only the ripe cherries are harvested, and they are picked
individually by hand. Pickers rotate among the trees every eight to 10 days, choosing
only the cherries which are at the peak of ripeness. Because this kind of harvest is labour
intensive and more costly, it is used primarily to harvest the finer Arabica beans.
45
A good picker averages approximately 100 to 200 pounds of coffee cherries a day, which will
produce 20 to 40 pounds of coffee beans. Each worker's daily haul is carefully weighed, and
each picker is paid on the merit of his or her work. The day's harvest is then transported to the
processing plant. (National Coffee Association of U.S.A, 2015)
3.1.4
Figure 10 Washing Tank
Processing the Cherries
Once the coffee has been picked, processing must
begin as quickly as possible to prevent fruit
spoilage. Depending on location and local
resources, coffee is processed in one of two ways:
The Dry Method is the age-old method of
processing coffee, and still used in many countries
where water resources are limited. The freshly
picked cherries are simply spread out on huge
surfaces to dry in the sun. In order to prevent the
cherries from spoiling, they are raked and turned
throughout the day, then covered at night or during
rain to prevent them from getting wet. Depending
on the weather, this process might continue for
several weeks for each batch of coffee until the
moisture content of the cherries drops to 11%.
The Wet Method removes the pulp from the coffee
cherry after harvesting so the bean is dried with
only the parchment skin left on. First, the freshly
harvested cherries are passed through a pulping
machine to separate the skin and pulp from the
bean.
Source Author
Then the beans are separated by weight as they pass
through water channels. The lighter beans float to the top, while the heavier ripe beans sink to
the bottom. They are passed through a series of rotating drums which separate them by size.
After separation, the beans are transported to large, water-filled fermentation tanks. Depending
on a combination of factors -- such as the condition of the beans, the climate and the altitude - they will remain in these tanks for anywhere from 12 to 48 hours to remove the slick layer of
mucilage (called the parenchyma) that is still attached to the parchment. While resting in the
tanks, naturally occurring enzymes will cause this layer to dissolve.
When fermentation is complete, the beans feel rough to the touch. The beans are rinsed by
going through additional water channels, and are ready for drying. (National Coffee Association
of U.S.A, 2015)
46
3.1.5 Drying the Beans
Figure 11 Solar dryer
If the beans have been processed by the
wet method, the pulped and fermented
beans must now be dried to approximately
11% moisture to properly prepare them
for storage.
These beans, still inside the parchment
envelope (the endocarp), can be sun-dried
by spreading them on drying tables or
floors, where they are turned regularly, or
they can be machine-dried in large
tumblers. The dried beans are known as
parchment coffee, and are warehoused in
jute or sisal bags until they are readied for
export. (National Coffee Association of
U.S.A, 2015)
Source Author
Figure 12 Sorting Machine
3.1.6
Milling the Beans
Before being exported, parchment coffee
is processed in the following manner:
Hulling machinery removes the
parchment layer (endocarp) from wet
processed coffee. Hulling dry processed
coffee refers to removing the entire dried
husk — the exocarp, mesocarp and
endocarp — of the dried cherries.
Polishing is an optional process where
any silver skin that remains on the beans
after hulling is removed by machine.
While polished beans are considered
superior to unpolished ones, in reality,
there is little difference between the two.
Source Author
Grading and Sorting is done by size and weight, and beans are also reviewed for colour flaws
or other imperfections.
Beans are sized by being passed through a series of screens. They are also sorted pneumatically
by using an air jet to separate heavy from light beans.
Typically, the bean size is represented on a scale of 10 to 20. The number represents the size of
a round hole's diameter in terms of 1/64's of an inch. A number 10 bean would be the
approximate size of a hole in a diameter of 10/64 of an inch, and a number 15 bean, 15/64 of
an inch.
Finally, defective beans are removed either by hand or by machinery. Beans that are
unsatisfactory due to deficiencies (unacceptable size or colour, over-fermented beans, insectdamaged, unhulled) are removed. In many countries, this process is done both by machine and
47
by hand, ensuring that only the finest quality coffee beans are exported. (National Coffee
Association of U.S.A, 2015)
3.1.6.1
Figure 13 Coffee Bags
Packing and Shipping
The milled beans, now referred to as green
coffee, are loaded onto ships in either jute
or sisal bags loaded in shipping containers,
or bulk-shipped inside plastic-lined
containers. (National Coffee Association
of U.S.A, 2015)
Source Author
48
Case of study
4. Use of modelling for improvement of a business process in a Coffee Farm.
The case study is a WITNESS simulation project that was conducted in a Coffee farm in
Colombia.
In this case it will be simulated by the WITNESS software the whole process of coffee
production, in addition to explain the data collection that was carried out, how the model was
built, the results and possible solutions to the problems currently presented by the production.
To take the coffee from the plant to our cups, a complex industrial process is carried out, which
requires time and great care in order to have the best export type. This production is only given
once a year, for the region where the farm is located production starts in October and ends
around January.
At the first sight we find several problems, the main bottleneck that delays all previous
processes is the drying of coffee, where investing in new infrastructure is not viable (given the
size of the company), we have to analyse the better way to find solutions without having a
strong impact on the producer's pocket.
4.1 Define the problem
4.1.1
Objectives of the study
The project will allow us to:
1. Identify bottlenecks in production and find possible solutions to put into practice
2. Prove that these solutions are economically and efficiently viable and yielding the highest
levels of quality in the grain.
Being a quarterly production, the simulation will allow us to take action before the other harvest
takes place, fix the problems and solve the bottlenecks that are currently generated and that are
evidenced by the simulation
4.1.2
Issues to be addressed
Knowing the cause before carrying out the simulation which is present in several productions
we can assume the main problems consist in:
• The coffee drying infrastructure has not evolved proportionally with the increase in production.
• The bottleneck generated by drying, delays the process and causes the coffee to lose quality
(not being processed fresh, is fermented lowering the quality of the grain)
• It depends on the weather (almost 40% of the coffee is being dried in the sun), the situation is
aggravated if we encounter periods of rain.
• The loss of quality impacts on the price, premium coffee has in price up to 300% higher than
the second quality.
49
4.1.3
Domain of the study
The information required to develop the study is relevant to the four macro production
processes (milling, drying, selecting and packaging) that are affecting us. In addition, we will
have a constant flow of raw material with a price that includes its collection, cost of
maintenance of the plant, energy costs will be part of the simulation, the cost of capital (land)
and their respective deprecations.
In other side parts of production that will not be taken into account, given their irrelevance are
the collection process of grain, since this is a process based on artisanal labour, with no current
opportunity to technify (given the conditions of the land, the sensitivity of the plants, the cost
of research), the cost of water, the disposition of the waste (the skin resulting from pulping
turned into composting), among others as food and lodging of staff, etc.
4.1.4
Level of detail
The level of detail of the process will be very close to reality, it will be a question of addressing
all the issues that impact the model, such as climatic factors, the variation of input of raw
material to the system, recurrent faults in the system, factors that affect the quality of the grain
in the production, the maximum current capacities of the machines and their processing times,
among other factors that will be able to be appreciated later during the implementation.
4.1.5
The need to model the process
This process has a particularity that is given only once a year, as mentioned above the
production is for only 3-4 months per year what gives us a great advantage as well as a
disadvantage at the same time.
To highlight we have a model that we can fix now without need to stop some production
(currently it is not harvest) and we consider as a disadvantage that the data can only be taken
for a short period of time, hence the need to simulate, a good model in WITNESS will allow us
to see graph, the current behaviour of the system at any period of time and how the proposed
improvements will work.
4.2 Design the study
4.2.1
Model assumptions
In the development of the simulation model, we will take as assumptions the following criteria
that will allow us to perform all kinds of experiments.
• Raw material (coffee beans) will always be available.
• Every day will arrive an average of 4000 kg with a variation of +/- 1000 Kg.
• The arrival time is 7 am and the process will be repeated until the end of the simulation.
• The price of coffee took a historical average of the last 3 years, where the premium coffee
costs 320,000 / 50kg and the second 80,000 / 50kg (prices 2016 in Colombian pesos).
• The product is packaged in sack bags (weight negligible) with 50 kg of dry coffee each.
50
• Workers' hours range from 6 in the morning to 7 at night (with their respective breaks) and
the hours of operation of the machines are from 6 in the morning to 6 in the afternoon. The
dryer is the only one that works 24 hours a day.
• In the pulping process, the result is 70/30, i.e. the removed skin is equal to 70% of the weight
and the product that can be used the remaining 30%.
• During the drying process, the coffee beans lose a weight of 60% when releasing the moisture
gained.
• The selection process of the grain has a variable result, if there are no delays in the pulping
process, the premium quality will reach 96%, but if this is delayed, the premium coffee can
lower at rates of 60% of the total production.
• The hours of operation of the solar dryers, will be determined by a climate model, where the
shadow hours will be reported as a failure.
• The final product is never blocked and is dispatched immediately.
• The currency used for calculations are Colombian pesos (COP).
• The value of the Kilowatt hour is COP 400 / hour.
• The costs of water use are neglected due it is taken freely from a small river.
4.2.2
Number of models Required
At the beginning and under the current concepts it is planned to make a total of 3 models apart
from the base model for a total of 4 models:
• Alternative 1 is intended to expand drying capacity by adding a new dryer machine,
eliminating dependence on the sun.
• In alternative 2, is intended to expand drying capacity by adding a new solar dryer we intend
to change the current model of how the coffee is distributed for drying, pre-drying in the sun
and then drying it completely by machine.
• In alternative 3, reduce the production.
• Alternative 4 and 5 we intend to change the current model of how the coffee is distributed for
drying, pre-drying in the sun and then drying it completely by machine.
These models are initially thought, if during the implementation of the simulation more ideas
are urged, other alternatives will be formulated.
4.2.3
Animation, tool and model requested
Initially the model was handled as discrete, but already in the study, it was found that the
processes responded better to a continuous model, although the two models are mixed, because
at the end of all stages, the coffee is packed and its price is calculated based on a bag of 50 kg.
not with respect to its volume.
A 2D simulation in WITNESS with both continuous and discrete process will be developed.
The scope of the model included four macro process, milling process (which includes the
process of arrival of the product, its storage and pulping), the drying process (which includes
both solar and fire driven dryer), selection process and finally the packaging process.
51
4.2.4
Business process Description
Figure 14 Business Process of coffee Production
Source Author
2
The coffee process is separated into 4 large macro processes, which will be detailed below:
Milling process:
The milling process is taken from the feeding of the system to the peeling of the grain, it covers
the unload process, the tank that supplies the mill, the mill, the composting tank and the washing
tank, the flow is made by gravity, although support in the unload by the worker is needed. It is
composed of the following processes:
• Every day the model is fed with an average of 4000 kg of coffee bean.
• The coffee is deposited in the tank that supplies the mill, with a capacity of 10000 kg,
this process requires a worker to help unload the shipment.
• Due to gravity, coffee falls directly to the mill, which has a nominal capacity of 1000
kg / hour and an energy consumption of 1 kW / h, and 100 litters of water / hour
• The mill is responsible for separating the coffee bean from its skin, resulting in two
products, 70% being the skin removed and 30% the useable grain.
• The milling machine has a preventive maintenance on average every 30 cycles, a task
that requires a worker and a time of approximately 60 minutes.
2
*60% of the weight is lost due to humidity
52
• Gravity removed skin falls into the compost pile, where it is stored.
• The useable grain goes to the washing tank, with a capacity of 2000 kg where the
drying process awaits.
Drying process:
The drying process is taken from the coffee leaving the washing tank, until it is sent to the
selected process, the drying machine is part (it works with dried coffee husks), the two solar
dryers.
• From the washing tank, the coffee is sent by means of piping and by gravity to the
different dryers.
• Priority number one is given to the drying machine, since it works 24 hours a day, with
a nominal capacity of 600 kg and an operation time of 18 hours.
• The drying machine requires a cleaning for each process that takes an average of 2
hours, in addition to a preventive maintenance in a range between 5 and 15 processes
with a duration of 2 hours, both tasks are done by the worker.
• Priority number two and three have solar dryer 1 (600 kg capacity) and solar dryer 2
(400 kg capacity), which do not require maintenance but depend on the daily sunshine
hours. The process of drying in the sun requires approximately 24 hours.
• Solar dryers require a cleaning that takes the worker approximately 2 hours.
• In both systems coffee loses 60% of its weight given its moisture.
• The three drying stations are connected to the dryer by piping, where a worker is
required to make this flow.
Selection process:
The process of selection begins with the entrance of the coffee and dry to the selector, process
in which separates the coffee of Premium quality of the second quality.
• The dryer is a machine that uses a sieve and vibration to select the quality of the coffee.
• It has a nominal selection capacity of 1000kg / hour and an energy consumption of 0.5
kW / hour
• Requires to be cleaned for each process, which takes approximately 20 minutes.
• It has a preventive maintenance that takes place between 10 and 25 processes
(depending on the volume of work) that is done by the worker, a process that takes
approximately 60 minutes.
• It has two output pipes (premium and second quality) that communicate with the
packaging machines.
Packing Process:
It is the last part to be analysed of the coffee production, the two packing machines (one for
each quality) are part of it.
• Each machine requires a worker to operate, where it is responsible for putting the bag
and make the process of filling and closing the bag
53
• This process takes at least 2 minutes with a maximum of 6 minutes
• Each bag has a value of $ 2500 pesos.
• Energy consumption is 0.15kW / hour
• The average quality of premium coffee is 96% of the total production, quality that can
be affected by delays
4.2.5
Start-up conditions and model run length
The first sack of coffee will be taken as the warmup time and the duration of the harvest as the
total time of the simulation, which would estimate the following:
• The warmup time will be 5000 minutes, equivalent to three days and 10 hours
approximately, time where the first finished product is expected
• The total harvest lasts 12 weeks, giving us a simulation value of 125,960 minutes
(taking into account the start value).
4.2.6
Model input data
The data that feed the model are as follows:
• Process times
• Climate model of sun hours’ daily
• Energy prices
• Set-up times, cleaning, maintenance.
• Work shifts
• Hours of operation of the machines
• Packaging costs
• Fixed costs for the use of machines and land.
4.2.7
Model output
The following are the outputs of the model:
• Reductions: Moisture lost, skin removed.
• Production: Premium quality and second quality coffee
• Percentage of quality
• Costs, profits and returns
54
4.3 Build, verification, validation and analysis the model
Figure 15 Base model in built face
Source Witness Software
In order to construct the model and its respective variants we already have all the tools, from
the identification of the problem, the definition of the processes, the input and output elements
that will give life to the simulation, as well as the warmup time and the duration of the
simulation. It should also be remembered that a mixed model (a combination of a continuous
and discrete model) was chosen as this one will allow us to bring the simulation closer to reality.
4.3.1
Model verification and validation
The technique used to measure if our model fit the reality is a comparison with the production
of the year 2016, getting to have a simulation that fits up to 95%, for that we had to make the
following calibrations.
Table 4 Production 2016 vs Simulation Results
PerformanceMetric
Premium
Second
CoffeeGrains
Compost
%Evaporated
Quality
Actual3
40000
5000
300000
200000
50000
89%
Simulation
41812
4690
317405
190644
49013
89.70%
%difference
4.53%
-6.20%
5.80%
-4.68%
-1.97%
0.90%
Source Author
•
The model had to be calibrated several times to meet the standards shown in the system
description, the changes at the beginning of the simulation from discrete to continuous
3
According to the Producer estimated production 2016
55
(product unload to the milling tank) and at the end of the continuous simulation to
Discrete (packing process) are the most attention called when modelling.
•
Another process with a degree of complexity was to adjust the solar dryers to the climate
model (sun/hours), the solution found was to plan the shadow hours, that is to say, by
means of a distribution (With variable equal to hours / sun and one Weight equal to
annual repetition according to the climatic model of 2016) the dryer will only work in
an interval where the most frequent is to have 8 hours of sun daily and 4 of shade, but
always varying up or down depending on the variable and the weight Assigned.
Figure 14Climate Bright Sunshine
Climate: Bright sunshine
Sun's Hours per day
0
2
4
6
8
10
12
0
20
40
60
80
100
120
Days per year
Source Institute of Hydrology, Meteorology and Environmental Studies (Colombia)
•
Another difficult issue to address was the loss of quality given the delay in the milling
processes (fermentation of the grain), for this purpose a warning was placed on the tank
of the mill that is activated when the levels pass 90%, this activates a conditional on the
selector that penalise the Premium quality, lowering it from 96% to 60%.
•
For other scenarios the description of the processes was followed without any
inconvenience.
56
4.3.2
Current situation of the model
Figure 17 Model base results
Source Witness Simulation Software
Already running the simulation of the current model, we can see graphically that the bottlenecks
actually occur in the first instance to the low capacity or poor distribution of the dryers, as it
was seen in figure 23, the operation of the mill is affected, and that almost 60% of its time is in
the process of emptying, i.e. in the transfer of the grain to a washing tank that remains full.
Figure 18 Milling Machine Statistics in the base Model.
Source Witness Simulation Software
57
The consequences are the grain is fermented by staying in wait for the milling machine. In
normal conditions the coffee must have a quality near 96%, but at the moment it is located at
89.70% more than 6 percentage points below, this turned money is an approximate loss in the
entire harvest of 14 million pesos (15% of the net profit)
Figure 19 Model Base results
4 = 96% − 89.7%
 = 6.3% ∗ 46337  2,919.23
 =
 ∗ 50 50
 = 58 ∗ $240,000 $ − $ 5 = $13,920,000
Source Witness Simulation Software
Another point where we can appreciate the effect and weight of the dryer delays is in the selector,
as shown in figure 25, this machine is underutilized, it waits to be filled 65% of its time and
only keeps working 7% Of its time.
Figure 20 Selector Statistics in the base Model.
Source Witness Simulation Software
4.3.3 Conclusions of the model and the base simulation.
The results of the simulated model give us a clearer map of how we are currently working
within the production of coffee, this is clear that the main problem is the bottleneck generated
in the dryers and their impacts on the quality of the product it represents almost 15% of the total
gross profit of the company, but an unexpected result is to see how the other part of the system
is being underutilized, since most other machines are used at an average of 20%. This
information is key to make decisions, where initially these scenarios will be given:
4
ML=Model Loss,
In the equation we took the value of the standard quality and compared with the value thrown by the
base model, this result is multiplied by the total production of coffee (before being selected) and finally we
convert it into bags of coffee, giving as Result that the inefficiency of the base model is generating 58 bags of
second quality coffee more than in normal conditions, this in prices gives us an approximate of 13,920,000
5
58
• Investing in the drying infrastructure (new drying machine) could be cheaper than continuing
to work in this way and losing money due to delays and process service could be provided to
other farms.
• The planting of new plants has now been slowed down in order not to intensify the problem
when processing coffee, solving 1 of the 4 macro-processes is believed to have sufficient
installed capacity to deal with it without problem.
• Another point to consider is the reduction in production, if quality is being affected and prices
are reduced up to 300% of the value of premium coffee, it is possible that lowering production
will achieve higher profit margins.
To clarify all these doubts generated in the current model, a series of tests will be carried out,
with the base model + some improvements will be run the simulation evidencing that results
throws us.
59
4.4 Experiments with the model and optimization
After the analysis and the failures found to the base model, we allow to enter to analyse 4
possibilities with which we will improve the process, one of then includes large investments in
infrastructure, others a rearrangement of the drying machines, a combination of the two
previous and finally a reduction in production.
The experiences will be evaluated with pros and cons in order to make a decision that will be
applied in this year's production (2017).
4.4.1
Base system + a new drying machine
This is one of the most logical solutions in the first moment, with 2 drying machines the
bottleneck is solved and in this way to improve the quality of the product (everything would be
freshly grounded). These results must be compared in the same way with the initial investment
that would have to be made and see if the company is in a position to do this.
Figure 21
Base Model + a new drying machine
Source Witness Simulation Software
Analysing the results, we have found in this model, that the improvement made would give us
excellent results and a quality coffee above the estimated 96%. Another factor that shows us
this is a possible solution is the profit, with almost 34 million pesos more than the previous
model, which represents around a 30% growth, something reasonable due to the investments
that must be made. In contrast, if we take a look at the milling machine, the two dryers and the
selector, we realize that the installed capacity is well above what is being worked, i.e. Figure
60
22 plant is in the capacity to receive more coffee since they are kept waiting to be fed at an
average of 53%, 25% And 46% respectively.
Figure 22 Base Model + a new drying machine Analysis
Source Witness Simulation Software
As a test in this experiment, it will increase in production from 5% to 25% to see the reaction
that has the implementation of this new dryer and that we can both move the production
boundary.
Table 5 Expansion Base Model + a new drying machine
No Dryer
% Increase
Revenue
Cost
Profit
% 1Dryer
% 2 Dryer
1
-
COP273,120,000
COP177,447,000
COP95,673,000.00
0%
-
2
0%
COP322,800,000.00
COP191,720,295.44
COP131,079,704.56
37.01%
0.00%
2
5%
COP341,120,000.00
COP200,426,500.00
COP140,693,500.00
47.06%
7.33%
2
15%
COP365,760,000.00
COP212,955,739.00
COP152,804,261.00
59.72%
16.57%
2
20%
COP356,160,000.00
COP206,504,318.00
COP149,655,682.00
56.42%
14.17%
25%
COP357,200,000.00
COP209,172,800.00
COP148,027,200.00
54.72%
12.93%
2
Source Author
In table 5 we can see the relative increases accordingly, first the base model with a single drying
machine and second with respect to the model of alternative one with two drying machines and
a gradual increase of the amount of coffee with which the system is fed.
61
Figure 23 Profit vs % Production Increase
Profit vs % Production Increase
$155,000,000.00
Profit in COP
$150,000,000.00
$145,000,000.00
$140,000,000.00
$135,000,000.00
$130,000,000.00
$125,000,000.00
0%
5%
10%
15%
20%
25%
% production Increase
Source Author
According to the results, we can see that the optimal capacity that we can increase production
when added in a new dryer is 15%, what instead of this will generate an additional utility of
60% with respect to the base model (approximately 57 million pesos)
A cost that we have to take into account is the new drying machine, valued at approximately 80
million pesos (Cash payment value), in an optimistic result, this will be covered in less than
three harvests (without any increase in production) with the additional gains of implementation.
Implementation and possible financing
Taking advantage of the fact that there is currently no production, there is no need to stop the
production line, the implementation would be planned to be carried out in the months before
harvest, according to the dryer manufacturer, it takes 15 days to install the new unit of drying
in the farm, we must add the current situation of the producer, which does not allow him to
make the investment with his own resources, and has to collect money from third parties. We
investigated loans focused on agriculture in Colombia and we have found the following form
of financing:
Table 6 Loan Simulation
Source Finangro
It should be noted that the credit application was made by the credit line to the promotion of
agriculture, having a rate of 22.42% effective annually (the market interest is more than 30%),
and the company must pay a value of $ 36,976,999.2 this year, which will be recovered with
the substantial improvements made in production.
62
Taking into account the financing, the projected results for the 2017 harvest, when buying a
new drying machine, would be those shown in Table 7.
Table 7 Profit after Financial costs
Scenari
o
Base
Model
Base
Model +
1 Dryer
Base
Model +
1 Dryer
+ 15%
Coffee
Revenue
Cost
Finance Cost
Profit
Profit
variation
COP 273,120,000
COP177,447,000
0
COP 95,673,000
0%
COP 322,800,000
COP
191,720,295
COP 36,976,999
COP 94,102,705
-1.64%
COP 365,760,000
COP
212,955,739
COP 36,976,999
COP 115,827,261
21.07%
Conclusions
As predicted in the beginning, this is a very good alternative to make production more efficient,
if we add a new drying machine and at the same time we make an increase in production as the
model suggests. It will be possible to maximize the profits of the Company almost 60% year
after year, despite the fact that it has to make a strong investment, its recovery is almost
guaranteed in the first three years.
One of the cons that may appear in this solution is the economic risk we could face. Remember
that coffee is a commodity regulated by an international price, although in recent years to steady
state, nothing guarantees us that for the year 2018 and 2019 where it is expected to recover the
money invested (Price has to be close to Premium Bag $ 320,000 and Second Bag 80,000).
Taking a pessimistic result and implementing solution 3 (Base Model + 1 Dryer + 15% Coffee)
we have a safety margin of around 21%, according to Table 7.
63
4.4.2
Base Model + 1 Solar Dryer
Another alternative that can be contemplated is the construction of a new solar drying patio or
solar dryer, its investment is low and can help, not in the same way as a drying machine that
works 24 hours, but may relieve the Bottleneck that is currently being generated in production.
For this, if you simulate a drying yard for 600 kg (current size of solar dryer 2).
Figure 24 Base Model + 1 Solar Dryer
Source Witness Simulation Software
Analysing the results of this simulation with the new drying yard we have found very good
results, similar to those of the drying machine implementation, although these results must be
taken with caution given that this system depends 100% on the Sun, another factor that causes
the result is similar is the lower cost of operation of the one against the other. But on the contrary
to the previous model, we will not be able to increase the production taking into account that
the three solar dyers are working almost to their maximum capacity (saving the time that they
remain inoperative either by cleaning or lack of sun).
Table 8 Profit Base System + 1 Solar Dryer
SunHours
Variable
Variable
Revenue
Cost
COP273,120,000 COP177,447,000
COP319,120,000 COP190,425,000
Source Author
64
Profit
COP95,673,000.00
COP128,695,000.00
Profit
variation
0%
35%
Implementation and possible financing
A solar dryer, is a cement plate covered with a structure of “guadua6” and plastic, which allows
the internal circulation of air. The concrete slab is 26 m2 and 5 cm thick (6.5 m long and 4.0 m
wide) requires a plastic covering of 7.5 m in length and 6.2 m in width. The upper height of the
arch formed by the roof should reach 2.10 meters and the heights of the two lateral ends of the
roof about 60 centimeters. Its construction and operation takes about 15 days, and its cost is in
the order of 8 million pesos.
Table 9 Profit after financial cost Profit Base System
Revenue
Cost
Finance cost
Profit
Profit variation
Base
COP273,120,000
COP177,447,000
COP-
COP95,673,000
Base + 1 Sun dryer
COP319,120,000
COP190,425,000
COP8,000,000
COP120,695,000
0%
26%
Source Author
The cost of this infrastructure can be assumed by the company without any problem, since its
implementation generates a return on investment, covering the total value of the first harvest.
Conclusions
Although at first the idea of the solar dryer was not taken very seriously, the simulation shows
that it is very helpful, with a 10% investment that would be invested in the construction of the
solar dryer, it can be done almost the same (of course, without allowing us to expand
production). On the other hand, the company would be left without any debt and receiving a
high rate of return.
One of the biggest cons in this alternative is the dependence of the sun on this model, although
the farm is in the tropics and the hours of sun are around 10 hours a day, global warming and
constant changes of climate have made that this varied a lot and that in the future we will have
rains in times where the sun is expected to shine.
This measure we recommend in the short term, its easy construction and low initial value pushes
us to move to this system and in the long term investments can be made in more sophisticated
infrastructures such as drying machines, which compensate the value for their great capacity
and guarantees a 24-hour operation without the weather affect us.
6
Guadua is the most important American bamboo. Due to its quality, the genus has been widely used
for house construction along the inter-Andean rivers of Colombia and in coastal Ecuador
65
4.4.3
Base model + Reduction in production
This alternative is the easiest one to put into practice, we see that the current bottleneck is
costing the company lost in grain quality, which results in a considerable loss of money as
analysed in the base model. For the coffee grower it would be profitable to lose a portion of his
crop and sell more premium coffee, than to continue the current model, where he is losing about
15% of the profit. In this alternative we will analyse a gradual reduction of the system feed until
finding the point where it becomes more profitable to produce.
Figure 25 Base model + Reduction in production
Source Witness Simulation Software
Evidently, according to the thought that a reduction would help to decongest the system and
thus have a greater profit, after simulating these 5 scenarios these are the results we have:
Table 10 Reductions Scenarios
%
Decrease
Revenue
Cost
Profit
Profit
Variation
0%
COP273,120,000
COP177,447,000
COP95,673,000.00
0
5%
COP277,440,000.00
COP175,393,200.00
COP102,046,800.00
6.66%
10%
COP279,120,000.00
COP172,703,600.00
COP106,416,400.00
11.23%
15%
COP269,920,000.00
COP164,127,400.00
COP105,792,600.00
10.58%
20%
COP256,720,000.00
COP155,685,800.00
COP101,034,200.00
5.60%
Source Author
66
The most effective reduction is made when we reduce by 10% the coffee from the harvest,
increasing the profits in the order of 12%.
Figure 26 Profit vs Production Decrease
Profit vs Production Decrease
$108,000,000.00
Profit in COP
$106,000,000.00
$104,000,000.00
$102,000,000.00
$100,000,000.00
$98,000,000.00
$96,000,000.00
$94,000,000.00
0%
5%
10%
15%
20%
% Production Decrease
Source Author
Implementation
This implementation is very simple, the producer has to reduce the number of harvesters, that
is, the number of people who are currently harvesting the grain. This has no initial cost, this
would lose the harvest of approximately 10% of the plants that are, but their yields are better
than if the grain is harvested. Although according to the producer, it is not good for the plants
that their harvest is not collected and fall to the ground, attracting pests to the coffee, thus
lowering productivity in the long term, something that is currently difficult to estimate by means
of being simulation.
Conclusions
It is an easy way to make 12% more profit, but this has its long-term consequences, the coffee
grower does not have to invest any amount of money with respect to the other two proposals,
but risks that the next harvests are affected with some plague by the rotting of the grains that
are going to stop picking. This further truncates the growth of the company, as this lazy output
will affect future harvests. Not recommended in any case.
67
4.4.4
Base Model redistribution
As a final test we will modify the current configuration of the dryers, going from a configuration
in parallel to a configuration in series, where it has been decided to do two experiments, the
first of them is to dry half the time in the drying machine and continue in the process in the
solar dryers and the other experiment would otherwise dry half the time in the solar dryers and
finish the process in the drying machine. Here we venture out if this measure can be effective
or if on the contrary it makes the bottleneck bigger.
Everything is aimed at finding solutions where economic investment is not required and have
a real impact on production.
Figure 27 Alternatives 4 and 5
Source Witness Simulation Software
According to the results of the model, this alternative does not good results, since as noted in
Table 11the yield in the coffee processing decreased in the two models an approximate 70%.
Table 11 Profit alternatives 4 and 5
Scenarios
Revenue
Cost
Profit
COP273,120,000
COP177,447,000
COP95,673,000
COP148,960,000
COP116,190,980
COP32,769,020
COP145,280,000
COP117,995,020
COP27,284,980
Base Model
0%
2 Solar Dyer +Dryer Machine
Dryer Machine + 2 Solar
Dryer
Profit
variation
-65.75%
-71.48%
Source Author
Analysing the reason for this failure, we find that the proposed model accentuates the bottleneck
instead of solving it, we no longer have a capacity in the first line of 1600 kg of drying (base
68
model), depending on the model can vary from 1000 kg (2 Solar dryers first) to 600 (drying
machine first) and where we detected the problem, as in the previous models the milling
machine shows us how the other things are working, since if this is blocked, This will affect the
quality of the grain and the money that enters in the company.
Figure 28 Milling Machine Statistics Alternative 5
Source Witness Simulation Software
According to statistics at the end of the harvest (simulation), this machine will be 75% of its
time waiting for a space in the washing tank to deposit the coffee. All this generated by the
difference so abysmal between the different machines, because while the mill and the selector
takes about an hour to do their work, the dryers spend between 18 and 24 hours.
Conclusions
Obviously it is a model that the producer will not implement, since it increases the bottleneck
for which this study was formulated, this model of drying in series also diminishes the quality
of the coffee, because when blocking the system of grinding, the coffee begins to ferment,
lowering the quality of the base model from 89% to 70% (ideally 96%), where this increase is
affecting the profit.
69
4.5 Results and conclusions
In order to take a good decision, statistical weights were given to variables such as Productivity,
production growth, external dependencies, quality, cost / benefit and implementation and it was
graded depending on the results of the simulation from the lowest result, which is 1 to the
highest result that is 10, in order to make a decision that would fit all the needs of the producer.
As there are several alternatives within each model, we will work with the highest profits
generated.
Table 12 Proposals Elections, Author
Alternative
A.1. Base
system + a
new drying
machine
A.2. Base
System + 1
Solar Dryer
A.3. Base
model +
Reduction in
production
A.4/A.5. Base
Model
redistribution
Weight
30%
10%
15%
20%
20%
5%
100%
Profit
Productivity
Grown
Possibility
External
Dependence
Quality
Cost
Imple
mentation
Total
COP152,804,261
9
10
8
8
5
5
7.75
COP120,695,000
8
4
3
8
8
10
6.95
COP106,416,400
8
1
7
8
9
9
7.4
COP32,769,020
1
1
5
3
7
8
3.55
According to the results the best proposal is the investment in a new drying machine, given that
it offers a future expansion capacity to the producer and does not depend on any external agent
for its operation (besides working 24 hours a day) and the quality of the grain produced is above
the standards (96%). Although its cost is high the rate of return is high, paying the entire loan
in less than 3 years (only with the money of more raised by the investment made)
As mentioned above there are risks that can affect the producer and his finances, although coffee
is a commodity with a stable behaviour, there is the possibility of both a high growth and a
sharp fall.
Noted this risk, we were able to find a possible solution in order to take care of the financial
health of the company, in consultations with the National Federation of Coffee Growers of
Colombia (Federacion Nacional de cafeteros de Colombia), We found that the price of coffee
can be agreed in advance 2 years, i.e. sell a Futures contract, where the two parties would reach
a price agreement, thus shielding the coffee grower and encouraging him to make this
investment.
On the day of publication of this work, the company has initiated the documentary collection
to request the money from the bank to make the investment.
70
Conclusion
In the study carried out in this work we could find how the tools like BPM and the simulations
can be implemented from a big companies like IBM, General Motors, Airbus, to key processes
in small production units. In this case we are talking about the companies belonging to the
agroindustry sector and the results obtained in this diploma work can help them become more
efficient and find its best utility rate.
These types of tools are not very common in farms in Colombia, where everything is
handcrafted and many decisions are made on the basis of trial and error and making decisions
about processes can be expensive.
My focus on working with the family business was mainly due to reasons mentioned above,
having an additional knowledge given by the Masaryk University, it was intended as a goal that
could help not only to my family but also to build better things in my country.
Despite the fact I do not change the world by improving the production unit of a small farm, I
think I have contributed with my work and effort to make better the place that has been under
the domination of war for more than 60 years.
The results are obvious, the recommendations given in the work are being made, the decision
to invest in infrastructure was waiting for a long time, but with simulate data and with financial
planning the coffee producer was encouraged to make the proposed improvements in order of
having a better production in key references such as quality and rate of return, at the same time,
the producer is aware that it can grow to a certain point (15% of the current production) without
re-presenting the same problems of bottlenecks in production.
Another thing to be grateful for to the producer is the documentation of all the processes, which
had never been done, all the calculations obtained were estimated and handled according to the
experience. Although there is much work to be done, a great step has already been taken and an
efficient coffee production process has been consolidated, with sustainability and possibilities
for orderly growth.
71
72
LIST OF APPENDICES
Appendix A Basic Elements on WITNESS
Appendix B Production according with the model
73
74
Appendix A Basic Elements on WITNESS
Element
Description
Examples
Parts
Parts (Entities) are
used to represent
those discrete items
that move around the
model.
Buffer
These are places
where parts can be
held.
Parts can represent:- products (cars,
engines, etc.),product batches, calls
in a telephone exchange, tiny
electronic components or whole
computers
Buffers can represent: parts
awaiting an operation on a factory
floor, people in a queue, the space
containing aircraft waiting to land.
Machine
Conveyor
Labor
Path
These are powerful
elements which are
used to represent
anything that takes
parts from
somewhere,
processes them and
sends them on to
their next
destination.
These are used to
move parts from one
fixed point in the
model to another
over time
This element can be
used to model
both human and
physical resources
which may be
required in the
model.
A path is an element
that parts or labor
units can travel
along in order to get
Machines can represent a machine
tool, lathe or a press. A complete
shop or a single supermarket
checkout. An entire plant or an
individual work cell.
There are two types of conveyors.
•Fixed conveyors maintain a
constant distance between parts. If
the conveyor stops, the distance
between the parts on the conveyor
remains the same.
•Queuing conveyors allow parts to
accumulate. If the conveyor
becomes blocked, the parts will
slide together until the conveyor is
full.
For example, tools, people or
equipment
You can use it to represent the
length and the physical route of a
real life journey in your model.
75
Designer
element
Icon
Attribute
Variables
Distribution
Function
Shift
Module
Pie chart
Timeseries
from one element to
another element
These are
characteristics of a
specific part or labor
unit.
Variables are values
which can be
accessed from
anywhere in the
model
Distributions allow
you to build
variability into a
model by including
data which you have
collected from the
real world.
WITNESS provides
a large number of
built-in functions
which you can use to
build intelligence
into the logic of your
model.
The shift element is
used to simulate a
shift pattern which
is, in effect, a
sequence of working
an nonworking
periods
A module is an
element consisting
of a collection of
other WITNESS
elements.
A reporting element
to display a pie
chart.
A reporting element
to display a time
dependent values.
For example, the number of
cylinders in an engine could be held
in an attribute, and you could then
use this attribute to determine the
amount of time required for tuning
and adjustment
For example, a variable could be
used to record the value of items in
an inventory
For example, if observations show
that the milling operation on type X
widgets takes between 5 and 10
minutes but most often takes 8.2
minutes, the information could be
introduced into the model using a
distribution.
For example, you could use a
function to detect the number of
parts currently on a conveyor. You
may also create your own functions.
Shift patterns may be applied to
labor and other elements in order to
simulate shift working.
Modules may be used to facilitate
“black-box” or hierarchical model
building.
Pie charts allow you to present
simulation results on the screen in
the standard pie chart format.
Time series allow you to present
simulation results on the screen in
the form of a graph which plots
values taken from the simulation
against time. Up to seven values
may be plotted with seven different
colors
76
Histogram
Histograms allow
you to present
simulation results on
the screen in the
form of a bar chart.
This is useful for determining the
range of values observed for some
parameter of the simulation.
77
78
Appendix B Production according with the model
Base Model
Base Model + 1 a new drying machine
Base Model + 1 Solar Dryer
79
Base model + Reduction in production
Base Model redistribution
80
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84
Tables
TABLE 1 BUSINESS PROCESS MANAGEMENT DEFINITIONS .....................................................................................21
TABLE 2 EXPERIMENTATION AND OPTIMIZATION ....................................................................................................39
TABLE 3 SIMULATION TERMINOLOGY .....................................................................................................................40
SOURCE (LANNER GROUP LTD, 2009) .....................................................................................................................40
TABLE 4 PRODUCTION 2016 VS SIMULATION RESULTS ..........................................................................................55
TABLE 5 EXPANSION BASE MODEL + A NEW DRYING MACHINE ..............................................................................61
TABLE 6 LOAN SIMULATION....................................................................................................................................62
TABLE 7 PROFIT AFTER FINANCIAL COSTS ..............................................................................................................63
TABLE 8 PROFIT BASE SYSTEM + 1 SOLAR DRYER .................................................................................................64
TABLE 9 PROFIT AFTER FINANCIAL COST PROFIT BASE SYSTEM .............................................................................65
TABLE 10 REDUCTIONS SCENARIOS ........................................................................................................................66
TABLE 11 PROFIT ALTERNATIVES 4 AND 5...............................................................................................................68
TABLE 12 PROPOSALS ELECTIONS, AUTHOR ...........................................................................................................70
85
Figures
FIGURE 1 BPM HISTORIC VIEW ON INFORMATION SYSTEMS ...................................................................................24
FIGURE 2 BPM LIFECYCLE ......................................................................................................................................28
FIGURE 3 SYSTEMATIC SIMULATION APPROACH. .....................................................................................................33
FIGURE 4 COLLECTING MODEL DATA ......................................................................................................................35
FIGURE 5 WITNESS SIMULATION SOFTWARE ..........................................................................................................42
FIGURE 6 WITNESS SIMULATION SOFTWARE TOOL ..................................................................................................42
FIGURE 7 COFFEE PROCESSING FLOWCHART ..........................................................................................................44
FIGURE 8 COFFEE PLANT. ........................................................................................................................................45
FIGURE 9 COFFEE BEANS .........................................................................................................................................45
FIGURE 10 WASHING TANK .....................................................................................................................................46
FIGURE 11 SOLAR DRYER ........................................................................................................................................47
FIGURE 12 SORTING MACHINE ................................................................................................................................47
FIGURE 13 COFFEE BAGS.........................................................................................................................................48
FIGURE 14 BUSINESS PROCESS OF COFFEE PRODUCTION .........................................................................................52
FIGURE 15 BASE MODEL IN BUILT FACE ...................................................................................................................55
FIGURE 16 CLIMATE BRIGHT SUNSHINE ..................................................................................................................56
FIGURE 17 MODEL BASE RESULTS ...........................................................................................................................57
FIGURE 18 MILLING MACHINE STATISTICS IN THE BASE MODEL. ...........................................................................57
FIGURE 19 MODEL BASE RESULTS ...........................................................................................................................58
FIGURE 20 SELECTOR STATISTICS IN THE BASE MODEL. .........................................................................................58
FIGURE 21 BASE MODEL + A NEW DRYING MACHINE ..............................................................................................60
FIGURE 22 BASE MODEL + A NEW DRYING MACHINE ANALYSIS .............................................................................61
FIGURE 23 PROFIT VS % PRODUCTION INCREASE ...................................................................................................62
FIGURE 24 BASE MODEL + 1 SOLAR DRYER ...........................................................................................................64
FIGURE 25 BASE MODEL + REDUCTION IN PRODUCTION..........................................................................................66
FIGURE 26 PROFIT VS PRODUCTION DECREASE .......................................................................................................67
FIGURE 27 ALTERNATIVES 4 AND 5 .........................................................................................................................68
FIGURE 28 MILLING MACHINE STATISTICS ALTERNATIVE 5 ...................................................................................68
86
Abbreviations
6M
Machine, Method, Material, Man, Measurement, Milieu
4P
Policies, Procedures, People, Plant/Equipment
7PMG Seven Process Modelling Guidelines
ABC Activity-Based Costing
APQC American Productivity and Quality Center
BAM Business Activity Monitoring Bill-of-Material
B2B
BPA
Business Process Analysis
Business Process Model & Notation
BPMS Business Process Management System
BPR
Business Process Reengineering
BTO
Build-to-Order
BVA
CEO
Business Value-Adding
Chief Executive Officer
CFO
Chief Financial Officer
CIO
Chief Information Officer
CMMI Capability Maturity Model Integrated
COO Chief Operations Officer
CPO
Chief Process Officer
CRM Customer Relationship Management
CT
Cycle Time
DBMS Database Management System
DCOR Design Chain Operations Reference (product design)
DES
Discrete-Event Simulation
DMS Document Management System
EPA
Environment Protection Agency
EPC
Event-driven Process Chain
ERP
Enterprise Resource Planning
FIFO First-In-First-Out
HR
Human Resources
IDF3 Integrated Definition for Process Description Capture Method Internet Service
Provider
IT
Information Technology
KM Knowledge Management
KPI Key Performance Indicator
OMG Object Management Group
OS
Operating System
87
PD
Product Development
PDSA Plan-Do-Check-Act
PO
Purchase Order
POS Point-of-Sale
PPM Process Performance Measurement
ROBAC Role-based Access Control
RFI Radio-Frequency Identification
RFQ Request for Quote
ROI Return-On-Investment
TCT Theoretical Cycle Time
TOC Theory of Constraints
TQM Total Quality Management
UIMS User Interface Management System
VA
Value-Adding
VCH Value Creation Hierarchy
VCS Value Creation System
VRM Value Reference Model
WIP Work-In-Progress
WfMC Workflow Management Coalition
WfMS Workflow Management System
88