OEE Improvement using Cost Effective Raw Material Inventory Management

(1)

OEE Improvement using Cost

Effective Raw Material Inventory

Management

Författare: Almila Berfin YAZICI

Handledare företag Jaroslaw

Modzelewski

Handledare LNU Anders Ingwald

Examinator, LNU Basim Al-najjar

(2)

(3)

ABSTRACT

The companies ought to care about OEE because it is a measure that shows the effect of the performance and quality related losses on the system or equipment. In order to improve OEE, they focus on defining the losses under each OEE elements and try to eliminate these losses. This study aims to enhance raw material inventory management in order to eliminate inventory management related losses and improve OEE. Lack of raw material and improper storage of raw material are main problems related to inventory management. A model is developed and tested in order to prevent these problems and eliminate these losses. Performance rate can be improved by preventing idle time due to lack of raw material, and quality rate can be improved by standardization and improving raw material storage procedure. In the result of model testing, it is shown that raw material inventory management has an effect on OEE and OEE can be improved by enhancing raw material inventory management.

(4)

ACKNOWLEDGEMENTS

During this study, we were lucky that people, who dealt with our questions and problems, as they were theirs, surrounded us. There are a few names that we want to tell them special thanks:

Firstly I would sincerely like to thank Saint-Gobain Emmaboda Glass for their contribution.

Jaroslaw Modzelewski - Production Manager Bertrand Lerebourg - Top Manager

And thanks to all the workers at Emmaboda Glass that kindly have answered our questions.

For her/his helpful guide and constructive feedbacks, our thanks go to our supervisor Anders Ingwald,

For his valuable feedbacks and contributions to evaluate that study thanks to Basim Al-Najjar…

Lastly, for support and understanding during this study, thanks to Mohammed Yahya and Nevin Boz …

Thanks all.

2012, May

(5)

DEFINITION OF KEY TERMS

Capacity: the total amount or of things that something can hold (Cambridge Business Dictionary, 2012).

Excess cost: difference between purchase cost and salvage value of items left over at the end of a period (Stevenson, 2005).

Holding (carrying) costs: cost to carry an item in inventory for a length of time, usually a year (Stevenson, 2005).

JIT: a technique for reducing wastage through procedures that establish good communications throughout the production process to ensure that all resources are used optimally, so that there are only minimum stocks on site for work in progress (Eti et al., 2004).

Lead-time: time interval between ordering and receiving the order (Stevenson, 2005). Ordering costs: It is the cost of ordering and receiving inventory (Stevenson, 2005).

Pareto Principle: the idea that a small quantity of work or resources (= time, money, employees, etc.) can produce a large number of results (Cambridge Business Dictionary, 2012).

Safety stock: the small extra supply of goods, materials, etc. that a company keeps in case the demand for them is greater than is expected (Cambridge Business Dictionary, 2012). Setup cost; the amount of money needed to start a business, service, process, etc. (Cambridge Business Dictionary, 2012).

Shortage: a situation in which there is less of something than people wants or need (Cambridge Business Dictionary, 2012).

(6)

LIST OF ABBREVATIONS

AHP Analytical Hierarchy Process CNC Computer Numerical Control DSS Decision Support System EOQ Economic Order Quantity EPQ Economic Production Quantity IT Information Technology

JIT Just in Time

KPI Key Process Indicator

M2 Square meter

MCDM Multi Criteria Decision Making MCIC Multi criteria Inventory Classification MM Millimeter

MS Excel Microsoft Excel

OEE Overall Equipment Effectiveness

RM Raw Material

RaMIM Raw Material Inventory Management TPS Toyota Production System

WIP Work in Process

(7)

LIST OF APPENDICES

Appendix 1;Saint-Gobain Group (from official website) ... x

Appendix 2; Photos from Current Raw material inventory ... xii

Appendix 3; Raw material items ... xiii

Appendix 4; Reasons and quantities of defect products ... xvi

Appendix 5; Production Percentage Among Standard Sizes for Class-A items... xx

Appendix 6;Production in February ... xxii

Appendix 7; Defected Products in February... xxvi

(10)

LIST OF TABLES

Table 1; Literature review ... 5

Table 2; Time frame ... 6

Table 3; The wastes in Lean Production ... 16

Table 4; Article search ... 19

Table 5; Comparison of RM inventory management models ... 22

Table 6; Eight selected raw material items ... 43

Table 7; Demand Forecasting ... 44

Table 8;Calculation of Holding costs... 44

Table 9; Calculation of Ordering cost ... 44

Table 10; Results of optimization model ... 45

Table 11; Inventory holding quantities ... 46

Table 12; EOQ Calculations ... 47

Table 13; Number of orders per year ... 47

Table 14; Demand of Class-A items According to sizes ... 49

Table 15;Standard rest plate sizes ... 49

Table 16; Rest Plate size for Class-A items ... 50

LIST OF FIGURES

Figure 1; Root cause analysis diagram ... 3

Figure 2; Study Plan ... 10

Figure 3; Connection between theories ... 12

(11)

1. INTRODUCTION

This chapter gives an introduction to the thesis. A description of the background, the problem discussion and the task developed are presented which leads to the purpose of this study. Also in this part research clarification takes place by starting a problem formulation.

1.1 Background

In early 1980s, as a reaction to high competition on market, where wastes are unaffordable and resources are very limited, the lean manufacturing concept was introduced to the public (Wan & Chen, 2009).Antony et al. (2003) mentions that the aim of lean strategy is to reduce cycle times, inventories, equipment downtime, set up times, scrap, rework and other wastes in the production system. Womack and Jones (2003) explain the concepts of waste reduction and show the effects on the operations of industries and organizations in the manufacturing sector. By preventing mistakes and eliminating wastes, a lean system produces products with lower cost, shorter lead-time, and more stable quality (Wan & Chen, 2009). Zhu et al. (1994) describes waste in a production line as; scrap, rework, equipment downtime, excess lead-time, overproduction, and lower space utilization. All these wastes also can have an effect on overall equipment effectiveness (OEE) (Antony et al., 2003).

Nakajima (1988) defines six big losses under the elements of OEE, which covers the wastes in lean manufacturing. OEE is a measure for effectiveness of either production system or production equipment. There are 3 elements of OEE, which are performance rate, quality rate and availability. Availability related losses are breakdowns, setup and adjustments, performance related losses are idling and reduced speed and lastly quality related losses are yield and rework (Nakajima, 1988). Godfrey (2002) also classified those wastes based on OEE elements, which also highlights the effects of waste elimination on OEE. Even though OEE is a technical measure, each loss related to OEE also has a cost in the system. This is why OEE improvement by reducing these losses can also reduce the costs related to these losses (Konopka & Trybulla, 1996).In this case cost effectiveness takes the attention because implementation of a loss/waste elimination procedure also has its own cost. The loss/waste elimination should have higher outcomes than itself.

(12)

production system starts with inventory. There are several inventory types such as; raw material (RM), work in process (WIP) and spare part inventories. Each inventory has different impacts of system effectiveness. As an example, spare part management effects the time of maintenance activities, which also affect the availability of the equipment. Furthermore, RM inventory management is a two-sided coin, which connects the supply chain to production, which is the first step of material flow in the production. A successful RM inventory management has several benefits in both sides but as a common, it provides optimization for the flow of material and traces the raw material requirement continuously (Rai & Singh, 2011). This is why raw-material inventory management affects performance and quality losses. Also preventing such losses decreases the production related costs and optimum RaMIM minimizes the total inventory cost. Since there are several RaMIM techniques, cost effectiveness must be considered to select the suitable technique.

1.2 Problem Discussion

The companies ought to care about OEE because it is a measure that shows the effect of the performance and quality related losses on the system or equipment. In order to improve the OEE, they focus on defining the losses under each OEE elements and try to eliminate these losses.

Performance rate is an indicator to measure the production speed. Performance problems occur from speed losses, small stops, idling or empty positions, (Eti et al., 2004).These losses, stoppages and idling may have different reasons related to inventory management. As an example, the line may be not running, because of lack of raw material. Lack of raw material is a problem in material flow, which causes delays and stoppages. These delays and stoppages are reasons for high production equipment idle time. These losses lead to low OEE (Eti et al., 2004). In this study production equipment downtime is defined as the time that the equipments available to function but remains idle.

(13)

stored in the inventory appropriately, the quality of the raw material decreases. Low quality raw material cannot be processed as a high quality product. So if the raw material quality is low, the product quality will be low. Improvement and standardization of raw material storing can overcome this problem.

The last element of OEE is availability. In this study, availability is defined as a measure of production equipment’s condition to function. Therefore, it does not cover the availability of raw material and does not have any relation with RM inventory. This is why the availability is assumed to be constant during the study. The focus is given on performance rate and quality rate because; these measures can be improved by enhancing inventory management.

1.3 Presentation of Problem

What kind of inventory related problems could occur that reduces OEE? Lack of raw material and inappropriate storing of raw material are two common problems related to inventory management, which can reduce OEE. Also, these are only some possible problems but how do they affect the OEE? They consume working time, work force, delay the production, decrease the production quantity, decrease the quality rate and increase production equipment idle time. The summary of the problem is shown in Figure 1.

Figure 1; Root cause analysis diagram

Low OEE

Performance Rate

Quality Rate

High Idle time

High Cycle time

High Setup time

(14)

How inventory management is related to these problems? Raw material inventory management function is essentially dealing with two major functions; inventory planning and inventory tracking (Nourpanah & Ansary, 2012). Optimizing RM inventory and following up RM inventory levels in order to improve performance rate, planning the way of storing raw material quality high in order to improve the quality rate, also improves OEE.

Max (2003) advises inventory cost for economical evaluation of alternative inventory management methods. In this study, cost effectiveness is utilized in decision-making step to decide the alternative management method. Companies may identify if their inventory management improvement is reducing or increasing inventory cost, but it is also important to identify the impacts of new inventory management on production i.e. does it generate losses or savings? There are already many studies about inventory planning and management tools and methods but there is a lack of theory about how to apply these methods and evaluating the effect of inventory management on OEE, which shows an interesting problem.

1.4 Problem Formulation

The problem formulation is based upon the presentation of the problem and it is given in the following.

- How to enhance raw material inventory management in a cost effective way in order to improve overall equipment effectiveness?

1.5 Purpose

The purpose is to develop a model for raw material inventory management in a cost effective way in order to improve OEE. Inventory management consists of planning and control. Inventory planning model consists of raw material classifications and raw material storing strategy while inventory control model covers optimization of the inventory level and order quantities. Evaluation will be done in technical and economical perspective in order to select the cost effective management model.

1.6 Relevance

(15)

2008). OEE is a common measure for company’s effectiveness. It is also very important to improve effectiveness and reduce the waste of production while you reduce the production cost. The literature on optimal inventory management uses criteria of cost minimization or profit maximization (Koumanakos, 2008), which also makes it cost-effective.

All companies would like to have a higher effectiveness in their production system to be stronger in todays though competition. Bose (2006, p.22) mentions the importance of inventory management in order to do efficient management of resources in any organization. This is why all companies can benefit from a cost effective raw material inventory management by eliminating the wastes in the system and improve their effectiveness. A cost effective raw material inventory management, keeps the material flow continuous, increases production rate and quality rate with optimum cost. Therefore this subject is of great relevance to study.

Beyond the practical relevance, a detailed literature survey is carried out to show the academically relevance. This review is given in Table 1.

Table 1; Literature review

Database Keywords Hits Relevant

Hits

Onesearch “Raw material inventory management” and “OEE” 0 0

Google scholar “Raw material inventory management” and “OEE” 0 0

LIBRIS “Raw material inventory management” and “OEE” 0 0

WorldCat “Raw material inventory management” and “OEE” 0 0

Google Books “Raw material inventory management” and “OEE” 0 0

IEEE “Raw material inventory management” and “OEE” 0 0

Emerald “Raw material inventory management” and “OEE” 0 0

(16)

In the literature review there is no directly related study found in the theory. There are only 2 hits about Raw material inventory management (RaMIM) and availability, which covers the availability of raw material, not equipment, so they are not relevant to this study. In this review, one article had mentioned a book written by Bose (2006) that mentions the effect of RaMIM on performance. Although Bose (2006) underlined that there are visible and invisible effects of inventory management on performance rate theoretically, but no model is given to prove this theory. As a result, there is still a gap of existing theories about how raw material inventory management affects OEE.

1.7 Limitations and Delimitations

The developed model will be tested in one relevant case company, which is delimitation. This study is limited with cutting department for model testing and measurements. One raw material inventory will be selected for model testing.

1.8 Time Frame

In Table 2, the primary time frame for conducting this study is shown.

(17)

2. METHODOLOGY

In this chapter an explanation of different approaches that can be used when conducting a scientific report are presented. Lastly, the chosen approaches and the reasons for these approaches are also presented.

2.1 Scientific perspective

In general, logical aspects of reasoning processes are divided into two categories: deduction, and induction. But there is also a combination of these two aspects, which is called abduction (Kudo et al., 2009).

Deduction is a reasoning process for concluding specific facts from general rules (Kudo et al., 2009). So it depends more on logical thinking than facts. On the other hand induction provides general rules from specific facts (Kudo et al., 2009). So the conclusions are built based on facts. Abduction is a process for providing hypotheses that explain the given facts (Kudo et al., 2009). It is a combination of these two aspects that creates logical hypotheses to explain empirical facts.

2.2 Research approach

Mainly there are two types of research methods, which are qualitative and quantitative approaches. In the qualitative approach, goal is discovery-oriented to understand processes while the research process is iterative and emerging (Forman et al., 2008). It applies inductive reasoning and used to create theories so the validity is assessed through methodological rigor, researcher experience and skill, and relevance (Forman et al., 2008).

In the quantitative research method the goal is to measure and determine the relationships among variables (Forman et al., 2008). Data analysis is deductive so validity is assessed through expert judgment, correlation and prediction, and mathematical modeling (Forman et al., 2008). It is commonly used for testing theories; therefore findings are generalized from sample to population.

2.3 Methods for data collection

(18)

cases designed to point out specific problems areas (Brewer, 2001). Data is the source of information for any case study in order to enable research to analysis or to prove any phenomenon. There are many methods of collecting data. The most common of these methods are interviews, observations and document reviews (Tashakkori & Teddlie, 2003). Interviews could be face to face or via phone. Data can be collected also via e-mail. These data can be personal experience and/or idea. Observations are main methods for case studies, as long as case study is a study of firsthand experience. The historical data for a study can be gathered by document reviewing and/or from electronic data.

2.4 Evaluation of results

In this paragraph validity, reliability and generalization of results will be described briefly. A research is reliability if results are consistent over time and if the results can be reproduced under a similar practice, (Golafshani, 2003). Validity determines whether the research measures which it was intended to measure, (Golafshani, 2003).

2.4.1. Validity

Validity determines whether the research truly measures that which it was intended to measure or how truthful the research results are (Joppe, 2000 cited in Golafshani, 2003). In other words, validity tests the consistency of measurements and results. There are three kinds of validity. These are construct, internal and external validity (Riege, 2003). Construct validity is about data gathering method and can be improved by having more than one source for each data (Riege, 2003). This method is also called as triangulation. Triangulation is a method to use of multiple sources of evidence in the data collection phase, to prevent biases and gather more realistic data, Golafshani (2003) and Riege (2003). Internal validity concerns about data analysis, which can be improved by crosschecking the analysis method (Riege, 2003). External validity concerns about research design. Model can be tested again in different case studies, (Riege, 2003).

2.4.2. Reliability

(19)

gathering by observations, data should be recorded permanently (Riege, 2003).

2.5. Thesis research methods

This report is written according to the official report template of Terotechnology department. It consists of developing a theoretical model, testing of this model and evaluation of results.

This is a case study, so we deal with empirical facts, on the other hand we develop models and methods based on general rules. Case study is selected as a method because; the developed model can be tested and validated by empirical data. This study combines both inductive and deductive approach in an "iterative" process. At first induction will be used for problem development and reasoning. Then the deductive approach will be used for model development and testing. In this point of view this study combines quantitative and qualitative approaches.

Data collection is based on two parts. Data that will be used for the theoretical chapters is through literature, scientific journals and books. Data that will be used for empirical findings and analysis is through interviews, observations, document reviews, electronic data and personnel experience.

All data, which will be gathered within the study, will be analyzed in order to evaluate the validity and the reliability. For achieving high validity, deviations will be investigated for finding the causes. In this study, all numerical data is gathered and processed with MS Excel. Therefore, validation by MS Excel is used to check the inconsistent data. To increase the validity of data gathering, each historical data is gathered from related department. Moreover, electronic data is checked with paper-based documents and consulted to related manager to increase the construct validity of the data. In this study, triangulation will be applied. Since the case company uses more than one IT software for keeping historical data, to assure the reliability, same data will be pulled from all alternative IT systems and be cross checked. For internal validity, each calculation will be done forward and backward to crosscheck the validity of data analysis. External validity of this study cannot be tested since the model is delimited to test on one case company.

(20)

explained in the report. Each data source will be given and observations will be recorded permanently. Assuring the reliability of personal experience is one issue that will be faced during the study. To overcome this issue, the background of person will be explained.

The author has her roots in industrial management, four years of bachelor degree in industrial engineering, of which one year is study within the field of planning and optimization models and two years of Master of Science degree in life cycle management of industrial assets. Courses studied that are the basics for this project are mainly facilities planning, operations management, production planning and case study II. The author is used to work with problem-based learning. Therefore, the author is qualified to make wide literature reviews, discuss and use theoretical knowledge in order to develop models for a specific problem and design a research method for this study.

2.6. Study Plan

The plan of this study is given in Figure 2.

(21)

(22)

3. THEORETICAL FRAMEWORK

In this chapter all necessary theories for conducting this research are presented in order to make the reader more acquainted with the subject and to increase the understanding.

3.1. Overview of theory

In Figure 3, different theories are presented together with the connections between them.

Figure 3; Connection between theories

At first raw material inventory management (RaMIM) is presented which is the base for conducting the model development. RaMIM consists of inventory planning and control so; raw material classification and inventory optimization techniques will be explained. In this study, these techniques will be utilized in a form of decision support system (DSS). Lean manufacturing is a strategy that affects the inventory planning while overall equipment effectiveness (OEE) is a measure to compare the changes in overall management. Finally cost effectiveness is defined to motivate the cost-effective selection of management model.

3.2. Raw material inventory management

Inventory is a stock of goods that contain economic value, and are held for use or sale in a future time (Nourpanah & Ansary, 2012).A manufacturing organization can hold inventory of raw materials, which are necessary for production in order to keep the material flow.

According to Nourpanah & Ansary (2012), RaMIM deals with two major functions; First function is inventory planning and the second one is inventory tracking. As inventory management, the task is to analyze demand and decide the inventory levels and to decide when to order and how much to order. Nourpanah & Ansary (2012) classified inventory

RAW MATERIAL INVENTORY MANAGEMENT

(23)

management approach as:

1. EOQ: Economic Order Quantity method determines the optimal order quantity, which minimizes the total inventory cost.

2. EPQ: Economic Production Quantity is developed model of EOQ, which determines optimal production quantity.

3. Continuous Order Model: When the inventory level reaches predetermined safety level, the order is triggered with optimal quantity.

4. Periodic System Model: It works on the basis of placing order with optimal order quantity after a fixed period of time.

These approaches are useful for batch or continuous manufacturing. There is also make-to-order manufacturing that nothing is specified and everything depends on customer make-to-orders. These approaches, given above, cannot answer such type of manufacturing.

3.3. Raw material Inventory Classification

Inventory classification is a necessary application to manage a large number of inventory items. ABC analysis is one of the most common techniques in inventory classification (Chen, 2011). It is based on the Pareto principle. The inventory items are divided into three classes. Class A is the high runner and/or very important class, which usually cover the 80% (Chen, 2011) of the overall score. Class B is the medium runner / important class which is the next 15% of overall score. Class C is the low runners / not important class which the remaining items in the inventory. Horbal et al. (2008) defines these classes as follows:

Class A; Components for products ordered by most of the customers in large volumes. Class B; Components for products often ordered by the customers.

Class C; Components ordered rarely or in very low volumes.

Based on the requirements in industry, traditional ABC analysis is also improved from one criterion to multi criteria. These methods are explained in the following.

3.3.1. Traditional ABC Classification

(24)

2006). This method classifies the inventory items based on one criterion, which is decided by the decision maker. In practice, this method is not very common because of this limitation.

3.3.2. Multi-criteria ABC Classification

Value of items and number of items are most common criteria for RM classification (Stevenson, 2005). Some other suggested criteria are; inventory cost, part criticality, lead time, commonality, obsolescence, substitutability, number of requests for the item in a year, scarcity, durability, substitutability, repairability, order size requirement, stockability, demand distribution, and stock-out penalty cost (Ramanathan, 2006).

Flores et al. (1992) provide a matrix-based model. This matrix is allowing only for two criteria. To increase the available criteria quantity, multi-criteria decision making (MCDM) tools are utilized. The most common MCDM tool is the analytic hierarchy process (AHP). The general idea of AHP is to calculate a score of importance of each inventory item by using weighting system (Ramanathan, 2006). As the common issue of AHP, this weighting system is subjective. To address this problem, Ramanathan (2006) developed a simple weighted linear optimization model whose criticality factor is a subjective value, aims to maximize the score, and calculates the importance score. Later, Ng (2007) developed a multi-criteria ABC classification model, which aims to compute a scalar score for each inventory item to compare them. On the other hand, this model was in lack of utilizing criteria weighting in score calculations. Hadi-Vencheh (2010) discovers this missing point in the model of Ng (2007) and develops it in a form that scalar scores are dependent on criteria weighting. Chen (2011) improved this model for inventory items that has effect on each other by peer estimation of criteria weighting.

3.4. Raw material Inventory Optimization

(25)

•Annual demand requirements known •Demand is even throughout the year •Lead-time does not vary

•Each order is received in a single delivery •There are no quantity discounts

These are the common assumptions for any optimization model for optimizing EOQ. So the optimization can be carried out for each item individually assuming that demand is known and lead-time is constant. If the demand is assumed as constant through the year, fixed order interval can be calculated by utilizing EOQ formula, given in Equation 1, (McIntosh, 2001).

Eq 1; EOQ = 2𝐷𝑂/𝐻

“D” represents the annual demand of the item, “O” is the fixed order cost of the item and “H” is the annual holding cost per unit item. Beside optimization of order quantity, different factors can be optimized according to the manufacturing types and raw material kind. These factors can be the size, weight, width, volume, etc. of the item. As an example, for items that cannot be counted with quantity, liquids can be optimized based on volume.

High runner items are checked continuously, Medium runner items can be periodic review policy and low runner items are controlled using a periodic review policy or only optimized for once (Ghelman, 2010).

3.4.1. Moving Average Forecasting Method

(26)

3.5. Decision Support System (DSS)

Decision support system is a computer-based system that helps and supports during decision-making process. In order to achieve more effective decisions, a DSS utilizes mathematical models borrowed from disciplines, which are applied to the data contained in the system (Vercellis, 2011). Decision support systems can be developed in any proper software. The aim of DSS is to take the data and put it into mathematical models and shows the result. The use of analytical models to transform data into knowledge and provide support is what distinguishes a DSS from an information system (Vercellis, 2011).

3.6. Lean Manufacturing

Lean manufacturing is a strategy developed by Toyota manufacturing plants and nowadays widely adopted around the world, Kahraman and Yavuz, (2010). Lean manufacturing is with lower cost, shorter lead-time, and more stable quality than the traditional mass production systems by preventing mistakes and eliminating wastes (Wan & Chen, 2009).

Kahraman and Yavuz (2010) stated that lean manufacturing is an umbrella term for JIT; hence, it can be explained to produce necessary units in necessary quantities at the necessary time with lean production continuous improvement is achievable by eliminating wastes. Utilizing lean production would help companies to use less but achieve more through improvements in the business process (Alsouf, 2011). Furthermore, according to lean manufacturing strategy, all kind of wastes is considered as anything that does not add value to product. Over producing, over processing, keeping high inventories and unnecessary transportation are all considered as wastes, Kahraman and Yavuz, (2010). Originally, seven kinds of waste are defined, but later seven new kinds of waste have been added to lean principles, given in Table 3 (Pham, Dimov and Hagan, 2001):

Table 3; The wastes in Lean Production

Original wastes New wastes

1 Waste of overproduction Waste of human potential

2 Waste of waiting Waste of inappropriate systems

3 Waste of transporting Waste of energy and water

4 Waste of inventory Waste of material

5 Waste of processing Waste in service or office

6 Waste of motions Waste of customer time

(27)

So, improving a production system according to lean manufacturing needs to analyze all of these topics above in plant to see where to make improvements in eliminating waste (Pham, Dimov and Hagan, 2001).

3.7. Overall Equipment Effectiveness

Slack et al. (2009) define Overall Equipment Effectiveness (OEE) as a popular method of judging the effectiveness of capacity, which is based on Time (for which the asset is available, availability); Speed (or throughput rate of the equipment; performance rate) and quality of the product or service (quality rate). So, OEE is calculated by multiplying performance rate, quality rate and availability (Godfrey, 2002).SME (1995) gives the calculation for performance rate and quality rate as in Equation 2 and 3:

Eq.2; Performance rate = Ideal cycle time * Output / Total operating time

Eq.3; Quality rate = Number of good products / Output = Output – Defected products / Output

To calculate OEE, the critical parameters require detailed performance data (Dal et al., 2000). This means initially data collection is highly complex. On the other hand, Dal et al. (2000) suggests a simplification for data gathering; rather than recording the actual time of each downtime and speed loss separately, the frequency of these losses could be recorded.

Dal et al. (2000) defines three main usage are for OEE. Firstly, OEE can be utilized to measure the initial performance of a manufacturing plant. Thus, the initial OEE measure can be compared with future OEE values, which shows the amount of improvement in time. Secondly, OEE can be measured for each production line to compare line performance across the factory. Thirdly, if the machines process individually, OEE measure can identify which machine performance is lowest to specify the problem.

(28)

3.8. Cost Effectiveness in Raw Material Inventory Management

There are many cost-analysis approaches in evaluation and decision-making, which are related, but different. There is a common mistake in theory in distinguishing the terms of cost effectiveness, cost/benefit. Lewin & McEwan (2000) separates each term clearly and describes cost effectiveness approach as selecting the alternative, which has the maximum effectiveness per level of cost or minimum cost per level of effectiveness.

(29)

4. MODEL DEVELOPMENT

In this chapter the development of a model to enhance raw material inventory management is presented. Search for relevant theories is made and then these theories are evaluated. In the last step a new model is created.

4.1 Literature Search

Before developing a model for raw material inventory management (RaMIM), a search for existing theories within this area will be done. The search is performed in Google scholar and Onesearch search engines. Within these engines, different databases such as, Emerald, Springer, Science direct and IEEE were available for scientific articles search. To assure the validity of these articles they are selected from scholar journals. Keywords used and relevant information is shown in Table 4.

Table 4; Article search

Keywords Search

Engine

Hits Relevant Hits

Reference(s)

“Raw material inventory management”

OneSearch 3 0 -

“Raw material inventory management”

Google Scholar

41 3 Ghelman (2010)

Nourpanah & Ansary (2012) Tang et al. (2008)

“Raw material inventory optimization”

Google Scholar

3 0 -

“Raw material management” Google Scholar

16 0 -

“Raw material optimization” Google Scholar

5 0 -

The relevant hits for these keywords are selected based on the scholar journals and study area. Since the navigator brings all articles that have these keywords in the entire text, many irrelevant hits had occurred. Studies related to manufacturing industry and covers inventory planning and control models are selected for relevant hits.

4.2 Discussion

(30)

The criteria for evaluating the theoretical models are given in the following; i. Understandable and applicable management model

ii. Utilization of Cost effectiveness iii. Covers OEE elements

These criteria are developed to criticize and compare the models available in literature. Since this study is about a cost effective inventory management in order to improve OEE, each criterion is defined to select the suitable model for this study.

Criterion (i) concerns whether the model is understandable and applicable or not. This criterion concerns about the management point of view. Since inventory control is a continuous process, the model should be applied repeatedly. Each variable and parameter notation should be defined clearly so the other applicants can understand the aim of the model.

Each step of model application should be given in an order. The steps should be defined in an overview and each step should also be given in details. The mathematical model should be understandable and able to be solved without any special requirement. Each step of calculations should be explained clearly. This feature prevents wrong implementation of model and eases the implementation plan.

Required data should be defined to enable other applicants to gather these data before implementing. This prevents time wastes during the model implementation and increases the reliability of the implementation. Also if any data was assumed or forecasted, the author should explain which method he utilized for this forecasting. As an example, if the demand data is assumed to be constant in the model, the author should mention it, or if it is forecasted, he should give the forecasting method in the model.

(31)

the material flow continuously. In these models to fulfill cost-effectiveness, the models should select an optimal order quantity, which is not allowing raw material shortages while minimizing the inventory cost. Inventory cost has two main cost factors, ordering cost and holding cost. An accurate inventory cost calculation should cover them both. Also inventory cost and ordering policy would differ for different raw material classes. A cost effective model cannot specify the same order quantity for high runners and low runners as long as high runners requires higher amount of material flow. So raw material classification is required to assure the cost effectiveness of the model.

Criterion (iii) concerns if the available models are discussed according to their effect on OEE. Since this study is discussing the effect of raw material inventory management on OEE, the models are compared according to this criterion. Models can discuss any element of OEE separately such as performance rate quality rate or availability. Moreover, shortage is another element that affects performance rate. If the shortage is allowed, the lack of raw material is allowed which reduces performance rate.

The comparison of three available models in the theory according to defined criteria is in Table 5. Each criterion is gathered from available models from theory. The criteria which are satisfied by available models from theory are marked with “+” sign.

Criteria I

(32)

example of application and no structured figure of model application. So this model also does not satisfy criteria (i). As a result none of the available models satisfy criteria (i).

Table 5; Comparison of RM inventory management models

Authors Criteria

Ghelman (2010)

Nourpanah & Ansary (2012) Tang et al. (2008) CRITERIA I Easy application of management

Definition for notations + + + Illustrative example +

Explanation of mathematical model

calculations

+ + +

List of required data Constant demand or forecasting method + + + CRITERIA II Cost effectiveness Economic order quantity + + + Cost minimization + + + Holding cost + + + Ordering cost + RM classification + CRITERIA III Overall equipment effectiveness Shortage amount + + Performance Rate + Quality Rate Equipment Availability

+: Model satisfies this criterion

Criteria II

(33)

summary, the only model that satisfies cost-effectiveness criteria belongs to Ghelman (2010).

Criteria III

To fulfill this criterion, the models should discuss the effect of inventory management on OEE and/or OEE elements. According to this explanation, the only model that discusses the effect of inventory management on performance rate is the model of Nourpanah & Ansary (2012). On the other hand, the models of Nourpanah & Ansary (2012) and Ghelman (2010) discuss about the effect of shortage amount in inventory planning. Since shortages in inventory affects performance rate, this model is covering performance rate discussions. On the other hand, none of the available inventory models mention about quality rate and/or equipment availability. As a summary none of the available models fulfills criteria (iii).

4.3 Model Development

(34)

Start

Phase 1- Raw Material Classification

a. Specify criteria for RM Comparison b. Estimate weight for each criterion c. Calculate score for each RM item d. Classify RM items based on scores

(Explained in Chapter 4.3.3)

Phase 2 – Order Quantity Optimization

2.1. Continuous Review a. Specify the assumptions b. Define parameters and

variables

c. Define objective function d. Specify the constraints of

the model

e. Calculate optimum order

quantities

2.2. Periodic Review a. Specify the assumptions b. Define parameters and

variables

c. Define total inventory cost d. Calculate optimum order

quantity

e. Calculate order interval

Phase 3 – Selection of cost effective inventory control strategy

a. Calculate total inventory costs

b. Select the minimum total inventory cost of optimum order

quantities from Phase 2

Phase 4 – Raw Material size Standardization

a. Specity RM items to optimize in size b. Specify the assumptions

c. Define standard sizes

d. Select the optimum size for each RM item

End

Initial OEE Calculation

Final OEE Calculation

Figure 4; Overview of the developed model

(35)

The model starts with Phase 1, raw material classification because classification model distinguishes the high runner, medium runner and low runner items to make the model more cost-effective and highlight the items to focus on in the rest of the model. RM Classification improves cost-effectiveness by evaluating and optimizing high runner items and low runner items separately. This classification will be utilized in inventory optimization step by determining different ordering policy and order quantity for each class in order to decrease the inventory cost. Multi criteria classification model is selected since it classifies according to more than one criterion (supported in Chapter 3.3.2). Each criteria will be weighed (Ramanathan, 2006) and score calculation of each item will be done (Hadi-Vencheh, 2010).

Phase 2 is for computing optimal order quantity for RM items. For order quantity optimization, there are three alternative methods: periodic review, continuous review or no review. For inventory cost minimization high runners must be optimized by periodic or continuous review (Ghelman, 2010). Medium and low runners do not have to be reviewed. This decision belongs to decision maker. In this study, high runners will be chosen for this optimization model. In Phase 2, for chosen RM items, both review policies will be applied.

Then in Phase 3, the total inventory cost of these review policies will be compared to select the minimum inventory cost. This total inventory cost is calculated according to the definition of Stevenson (2005) for cost-effective RaMIM model. Since both optimization technique does not allow shortages and have the same effect on system effectiveness, this selection also fulfills the cost-effectiveness criteria of the model.

Lastly, Phase 4 is for defining standard sizes for RM items and select an optimum size for each RM item according to demand of item. This phase is specifically applicable for RM items, which are being purchased in bigger size and stored in smaller size.

When the RaMIM model is implemented, a final effectiveness will be calculated to show the effect of RaMIM on OEE.

4.3.1. Data Collection for the model

(36)

the model. If the available data is not relevant and/or enough, data gathering should be applied before starting the model. Before starting collecting data, all required data should be listed and defined. This will prevent wasting time on collecting unnecessary data. During the data collection process, there are some issues that should be considered. These data must be valid in the time of the study and after. Same data can be compared from different sources to increase validity. If there is a missing data, no data should be fabricated instead. The source of the data should be noted to increase the reliability of the data.

For OEE Calculation: For performance rate calculation, production quantity, cycle and total production time is required. For quality rate calculation, defected product quantity or number of good products is required. And for availability, the time for maintenance activities, total downtime of the equipment and total available time of the equipment.

For Phase 1: Data collection of raw material prices can be found in purchasing orders. Technical data such as quantity, size, and weight according to demand can be gathered production order or customer orders. Furthermore, amount of use in production and frequency of use should be obtained from historical data. It is important to find all relevant data for all raw material types.

For Phase 2: For optimization models, required data are annual demand, holding cost of one item, ordering cost, lead-time of RM items. Annual demand can be approximated from historical demand data or can be forecasted by forecasting techniques. Lead-time can be found in purchasing related database. Ordering cost and holding cost is an approximation from product price and can be calculated in different ways.

For Phase 4: The demand of each RM item according to customer order size is required to analyze the most desired size for each item. Also size of supplied RM is necessary to define standard sizes.

4.3.2. Initial OEE Calculation

(37)

In this study, the equipment availability is assumed to be constant, since no effect of raw material inventory management is shown on availability. The performance rate can be calculated with Equation 2. Every company can have different ways of calculating performance rate. The main focus is to include the problem in the calculations and measure the speed of production accurately. As an example, if the performance rate problem is related to lack of raw material, the time period of data should include such a problem. The data of OEE calculations should be chosen carefully in order to represent the actual situation. The quality rate can be also calculated with Equation 3. Quality rate represents the rate of quality items so it is a value, desired to improve also.

4.3.3. Phase 1- Raw material Classification

In this stage the available raw materials are going to be classified. The explanation for each step is given in the following.

1.a.Specify criteria of economical and technical aspects for RM comparison. These criteria used to compare raw material to define which of them are high runners, medium runners or low runners. The aim of RM Classification is to distinguish RM items according to production amount and price. Therefore the criteria are the price of raw material, frequency of use, size, weight and amount of use. These criteria can be utilized all or some of them. This decision belongs to decision maker and changes the result of classification.

1.b. Estimate weight for each criterion. Weighting of criteria is heuristic approach that depends of the decision maker opinion. Each criterion has an importance in this analysis that may differ according to type of industry and RM item. Weighting can be more accurate according to the experience of decision maker. Heuristic weighting of criteria is a feature of AHP technique (Ramanathan, 2006).

(38)

Eq 4; Objective Function

In equation 4, “i” represents the raw material items;“j” represents the criteria to consider. “Si”

is the score of item “i” which is multiplication of weight and measurement of each criteria. “Yij” is the measurement of criterion “j” of item “i” which needs be converted to a value

between 0-1. This converted value is represented by “CYij” and computed by Equation 5.

Eq 5;Conversion of measurements

Weighting of contribution of each criteria of each item is shown as “Wij”. Total amount of all

criteria weights of each item must be 1, . For a discrete linear model, no variable can be less than zero so, Wij≥0 for all weighting values.

1.d. Classification of items. As a result of this model, highest score is grouped as Class A, middle values are Class B and low values are Class C. These classes are also called as high, medium and low runners. There is no specific rule for deciding the limits of classes. Based on Pareto principle, until 85% of all scores are classified as Class A, from 85% to 95% of all scores is Class B and the rest is Class C.

4.3.4. Phase 2 – Order quantity Optimization

In this stage the focus is given in Class A and Class B items as long as they cover the high amount of all production. Class-A items will be checked with the cost effective review policy, Class-B items will be checked by periodic review policy and Class-C items will be only optimized for once.

Phase 2.1. Continuous Review

(39)

2.a. Specify the assumptions of the model. Inventory tracking model will be developed both as periodic review and continuous review models. In both models, demand can be forecasted and order quantity can be calculated periodically so the optimization can to be improved according to new empirical data. In this study, two approaches will be applied both and compared according to cost-effectiveness.

For demand forecasting, moving average forecasting technique is applied in this model. There are various forecasting methods and a suitable one should be selected to data. If there is any seasonal of linear trend, moving average method cannot be utilized.

2.b. Define notations of the optimization model. The indices, parameters and variables of the model are defined in the following.

Indices;

i = raw material (i = 1,...N) t = time period

T = length of planning horizon for each model run

Parameters;

Dit = Demand for raw material i in period t, i = 1,...,N and t = s, s+1,..., s+T-1

Cit = unit cost of raw material i in period t

Li = Lead time for raw material i

Hi = Inventory holding cost per unit of raw material i per period

Capt = Total normal storage capacity for all raw material in period t (in kilograms)

Ai = the unit weight of each raw material i  (in kilograms)

Ot = Fixed order cost of raw material i in period t

M = Very large number (e.g. M = 9999)

Variables;

Xit = Amount of raw material i ordered in period t

Iit = Inventory of raw material i at the end of period t

(40)

2.c.Defining objective function of the model. The objective of this model is minimization of total cost. Total cost consists of inventory holding cost and ordering cost. The formulation is shown in Equation 6.

Eq 6. 𝑀𝑖𝑛 z = 𝑇_𝑡=1 𝑁_𝑖=1(𝐻_𝑖 ∗ 𝐼_𝑖𝑡) ∗ (O𝑡𝛿𝑡); (∀ 𝑖, 𝑡)

2.d.Specify constraints. There can be some constraints related to supply chain, production quantity, time or special contracts with different customers. These constraints should be involved in the model to get more realistic results. Constraints of the model are explained in the following;

Demand Constraints

This constraint (Equation 7) is to make sure that in every period the inflows are the same with the outflows. Shortage is not allowed so the amount items coming inside the inventory must be equal to the amount of item going out from inventory. Inflow amount is calculated according to lead-time, so it is not the amount ordered but the amount reached to inventory.

Eq 7. Ii, t-1 + Xi,t-Li = Dit + Iit ; ∀ 𝑖, 𝑡

Capacity Constraints

This constraint (Equation 8) is developing to limit the raw material storage according to the limits of inventory storage units.

Eq 8. 𝐴𝑖 𝐼𝑖,𝑡−1+ 𝑋𝑖,𝑡−𝐿𝑖 = 𝐶𝑎𝑝𝑡

𝑁

𝑖=1 ; ∀ 𝑖, 𝑡

Binary Constraints

This constraint (Equation 9) will make sure that the orders quantities have a relationship with the actual ordering in every period. If no orders are placed in a period, these order quantities have to be zero for that period.

Eq 9.X𝑖𝑡 ≤ 𝑀𝛿𝑡; ∀𝑖, 𝑡

Xit≥ 0; (∀𝑖, 𝑡)

(41)

These are non-negativity constraints that will make sure that the order and inventory quantities will never be less than zero.

2.e. Calculating order quantity. This is a mathematical model so it should be solved with proper software. Since there is a binary variable, “Lindo” or “Lingo” cannot solve this model but “MS Excel Solver” or “Gams” can solve it.

Phase 2.2. Periodic Review

The steps of Phase 2.2 are given in the following.

2.a.Specify the assumptions of the model. In periodic review, annual demand can be gathered from historical data or demand can be forecasted for a year time to gather annual demand. Holding cost, ordering cost also assumed to be constant during the year.

For periodic review, as an EOQ assumption annual demand must be constant and known, on the other hand demand can be stochastic in continuous review model (Stevenson, 2005). For demand forecasting, moving average forecasting technique is applied in this model, because there is no trend in demand data (Explained in Chapter 3.4.1).

2.b. Define notations of the optimization model. The notations of the model are defined in the following.

Parameters;

Di = Annual demand for raw material i, i = 1,...,N

HCi = Annual inventory holding cost per unit of raw material i

OCi = Fixed order cost of raw material i

Variables;

EOQi = Economic order quantity for item i

TC = Total inventory cost

(42)

2.c.Defining total inventory cost. Total inventory cost consists of inventory holding cost and ordering cost. The cost calculations are the same in both review techniques to be able compare them. The formulation is shown in Equation 10.

Eq 10. TC = 𝑁_𝑖=1[(𝐻𝐶_𝑖 ∗ 𝐸𝑂𝑄_𝑖) ∗(OCi * Oi)], (∀𝑖, 𝑖 = 1. . 𝑁)

For determining Oi, another equation is required, given as Equation 11. The annual demand

should be divided by order quantity to compute how many orders are required for this time period.

Eq 11.Oi = _𝐸𝑂𝑄𝐷𝑖

𝑖(∀𝑖, 𝑖 = 1. . 𝑁)

2.d. Calculating order quantity. Economic order quantity is calculated with a formula given in Equation12. This equation is based on the theory given in Chapter 3.

Eq 12.EOQi =

2∗𝐷𝑖∗𝑂𝐶𝑖

𝐻𝐶𝑖

2.e. Calculating order interval. Since the order quantity is known, by dividing total demand to order quantity, we can compute how many orders should be given during the year. This order number will be given in 12 months so by dividing 12 by order number, we can find the fixed order interval.

4.3.5. Phase 3 – Selection of Cost-effective model

This phase satisfies the cost-effectiveness of the model by selecting the minimum inventory cost for the same effect. Stevenson (2005) defined cost effective RaMIM model as a model that includes inventory holding cost and ordering cost as total inventory cost. So total inventory cost is calculated by adding annual holding and ordering cost of items. The data used in both models are the same and the same forecasting technique is utilized for demand forecasting. Since both models are not allowing shortages, the effect of both models are the same on performance rate.

(43)

3.b. Selection of model. The total inventory cost values, gathered in Phase 2 will be compared and the model with minimum cost will be selected to as the application.

4.3.6. Phase 4 – Standardization of Raw material size

This phase is applicable for items that require being stored in different size than supplied. The main purpose of the optimization is to minimize scrap rate, ease to locate the RM item and standardize the inventory holding while preventing some quality issues.

4.a. Select RM items for size standardization. Not all the RM items have to standardize in size. Since they have the highest effect on the effectiveness, high and medium runner items can be standardized in size to ease the locating of RM item and prevent quality issues. The items can be high-runners, medium runners, specific items which needs to be standardized in size for easily handling, items that has quality problems related to holding in different size.

4.b. Specifying assumptions. This model assumes that the production percentage of each size will be constant. If scrap rates changes in time or one or more of the selected size becomes unnecessary, the procedure can be repeated in time. Also, the minimum production percentage that should be covered by a selected size must be more than 20%.

4.c. Determining Standard sizes. The step is the main standardization process. According to supplying size, other smaller sizes should be determined. Also, these standard sizes should be easy to consider and process for the workers. There are two main criteria for determining standard size; production rate according to size and easiness to gather from original size.

4.d. Select optimum size. A decision support system will be developed to select the optimum size(s) for selected items. This decision is based on the production amount for each standard size. The aim is to select the most suitable size with maximum production rate and minimum scrap rate.

(44)

scrap by using 5kg for 20% of production instead of 15kg.

4.3.7. Final OEE Measurement

(45)

5. EMPIRICAL FINDINGS

This chapter contains the description of the case company, the company's aim, current applications of inventory management and work areas, material handling and resources.

5.1. Presentation of Saint-Gobain Group

Saint-Gobain, the world leader in the habitat and construction markets, designs, manufactures and distributes building materials, providing innovative solutions to meet growing demand in emerging economies, for energy efficiency and for environmental protection. It was established in 1665 in France and today about 195,000 people work in 64 countries. Saint-Gobain Group is active in 4 main sectors such as; innovative materials, construction products, building distribution and packaging. The innovative materials sector comprises the flat glass and high-performance materials divisions.

The company has more than 60,000 employees in 45 countries that are responsible within the innovative materials sector. Innovative Materials Sector has eight core businesses; abrasives, ceramic materials, plastics, textile solutions, flat glass manufacturing, processing of glass for the building industry and domestic appliances, processing of glass for the automotive and mass transit markets, solar energy solutions. More information about Saint-Gobain Group is given in Appendix 1.

5.2. Presentation of Saint-Gobain Emmaboda Glass Factory

Emmaboda Glass factory was established in 1919. In 1946, 30% of the facility was taken over by Saint-Gobain Group. Later in 1974, the facility was totally owned by Saint- Gobain Group. Emmaboda Glass facility is also important for Saint-Gobain Group for being the first investment in Sweden.

(46)

5.3. Cutting Department

In this facility, there are two main production lines, which are for fire-resistant glass products and isolation glass products. Both production lines start with a common process, which is cutting. Since, cutting department is the first step of all production, there are 2 shifts only in this department. It pulls the raw material (RM) from inventory and pushes the items to different production processes.

There are two types of cutting departments in the facility. The first one is computerized cutting department and the second one is manual cutting department. Both cutting departments feed the production lines with items. In this study, we focus on manual cutting department.

5.3.1. Computerized Cutting Department

In computerized cutting department, there are two CNC tables for glass cutting, which is operated by workers. The raw material is being brought from inventory by forklifts to the machines. The machine pulls and locates the glass on the table and start processing. This cutting process is planned and optimized in production planning department. The CNC machines are only allowed to use jumbo size glasses (3250-6000mm). There are two kinds of CNC cutting tables; one is for single glass and the second one is for laminated glass. Laminated glass is a glass that consists of more than one glass stick to each other. These glasses can be only processed on computerized laminated cutting table.

5.3.2. Manual cutting Department

Manual cutting department consists of one cutting machine and its own RM inventory. This department is responsible of cutting “remakes” and not-optimized pieces, which cannot be planned for CNC machines. Remake is producing the same of a defected item. There is one operator responsible for each shift. The process order is sent from production planning. No glass cutting optimization is utilized for this department. Also no data is recorded about the performance rate of this department.

(47)

the glasses to find the closest size to his production order. He pulls the overhead cranes to handle the item to cutting machine. After he places the glass on the cutting table, he gives the information on the work order to computer and operates the machine. This machine is making 2 millimeters (mm) cuts on the glass surface. When the cutting process finishes, the worker separates the Work in Process (WIP) product from scrap manually. He dumps the scrap to recycle box and places WIP product in the handling rack to send to the next process.

5.4. Rest Plate Inventory

In the facility, there are two kinds of raw material inventory; for computerized cutting tables, and manual cutting table. As it was mentioned as delimitation, this study will be conducted in the inventory for manual cutting table, which is also called as “rest plate inventory”. Rest plates are cut into smaller sizes from jumbo size plates. To plan the jumbo size inventory, rest plate requirement and demand should be specified. This is the reason; this study focuses on Rest Plate Management.

OEE Improvement using Cost Effective Raw Material Inventory Management