Structural Equation Modeling Approach to Overall Equipment Effectiveness in Arla Foods

Authors Anne Sophie Rahbek Hansen MSc. in Finance and IB

Christina Bkkelund Breum MSc. in BPM

Advisor Kai Kristensen

Structural Equation Modeling Approach to Overall Equipment Effectiveness

in Arla Foods

Aarhus School of Business, Aarhus University Department of Marketing and Statistics

August 2010

Structural Equation Modeling Approach to Overall Equipment Effectiveness in Arla Foods I

Foreword The present thesis represents the final project of the Master of Science programme at the Aarhus School of Business, Aarhus University, during the spring and summer 2010.

In connection with the creation of the thesis, the authors would like to thank Karen Tybjerg from the former Arla Foods Global Ingredients Supply Chain Development for agreeing to collaborate with the authors with respect to the OEE initiative in Arla Foods. Furthermore, appreciations are sent to OEE Project Manager Per Hjort Petersen as well as the Production and Technology Development managers at the Global Ingredients production sites for the helpful sparring and assistance with the creation of the OEE study. Special thanks are given to Trine Obel Gregersen and the employees at HOCO for the kindness shown to the authors at their visits to the production site.

The authors are both accountable for the entire content of the thesis.

Aarhus School of Business, Aarhus University, August 16th 2010.

Anne Sophie Rahbek Hansen Christina Bkkelund Breum

Structural Equation Modeling Approach to Overall Equipment Effectiveness in Arla Foods II

Abstract The present thesis consists of an assessment of the measurement of Overall Equipment Effectiveness (OEE), as well as an evaluation of the current and future OEE implementation in Arla Foods. The analysis is therefore divided into two sections: a theoretical part determining the components and validating the structure and relationships of OEE by means of structural equation modeling, as well as a practical analysis assessing the current effectiveness of the spray towers together with the

applicability of OEE as a key performance indicator in Arla Foods Global Ingredients (GIN), and recommending the future initiatives, which the company must consider in order to enhance the success of the future OEE roll-out. Before the theoretical and practical objectives of the thesis are pursued, several preliminary exercises are performed to prepare the grounds for the comprehensive theoretical and practical analyses.

First of all, the preliminary analysis begins with a review of the existing literature on performance measurement, management and key performance indicators, which reveals the importance of identifying, measuring, and managing the indicators central to organizational strategy. Furthermore, the empirical knowledge indicated the importance of aligning the measures at all organizational levels, so strategy can be implemented throughout the organization, and the entire company strives towards the same goal.

Secondly, a theoretical and empirical assessment of the key performance indicator, OEE, is carried out. OEE is an aggregated measure of availability, performance, and quality of individual equipment, in which the various types of effectiveness losses are subtracted from the scheduled operating time. The OEE measure is subject to several points of critique ranging from the exclusion of planned downtime in the OEE calculation to the question of whether OEE is in fact the multiplication of availability, performance, and quality with equal weights. In the light of the review of key performance indicators and the SMART criteria, it is evident that OEE, despite the critique points, can be considered a key performance indicator, but the measure is modified into OEE* to accommodate the criticism and comply with the OEE definition in Arla Foods.

Furthermore, an OEE study is designed in collaboration with Arla Foods, which is an international production company already in the process of implementing OEE with the

Structural Equation Modeling Approach to Overall Equipment Effectiveness in Arla Foods III

purpose of identifying bottlenecks, visualizing the utilization of the high capital cost equipment, and benchmarking the equipment effectiveness over time and across production sites. Arla Foods has decided to begin the OEE implementation on the spray towers at the Arla Foods GIN production sites, as the spray towers are assumed to be the bottlenecks in the production of ingredients, and at an OEE workshop in March 2010, it was therefore decided to collect data for the availability, performance, and quality of the spray towers in the fourth quarter of 2009.

Finally, the preliminary analysis is concluded by an econometric analysis of the data to discover the tendencies and effects in the data set, and assess the applicability of structural equation modeling. It is revealed that there are fixed time-constant effects for the individual spray towers and production sites, as well as lags and time effects of the respective weeks, which entail that the error terms correlate over time and across units. The correlation can be remedied by means of fixed effects estimation and inclusion of lags and dummies.

The theoretical part of the thesis is based on a structural equation modeling approach to OEE to investigate the components and structure of OEE, as well as assess whether a relationship between OEE and financial performance exists. The outer specification of the Partial Least Squares model is underidentified, and several of the manifest indicators load poorly on the associated latent construct. The inner model indicates that performance and quality loss, as expected, relate negatively to OEE, whereas availability loss relates positively to OEE, and the established relationship between OEE and financial performance proves to be weak. Even though not statistically significant, the path coefficients for the relations between the components and OEE reveal that validation of the multiplication of availability, performance, and quality with equal weights is not feasible. The data basis has deemed it difficult to draw conclusions based on the findings of the structural equation model, and it is therefore only possible to indicate the tendencies of the model.

The practical part of the thesis comprises the computation of the current OEEs of the spray towers, and benchmarking of the OEE results across production sites by means of Repeated Measures ANOVAs in an attempt to discover the best practices. It is evident that the OEE results fluctuate significantly during the 12 week period, and that the defined threshold of 56% for satisfactory effectiveness is not always achieved. The

Structural Equation Modeling Approach to Overall Equipment Effectiveness in Arla Foods IV

Repeated Measures ANOVA for OEE reveals that differences in the OEE levels exist between the spray towers, and the reasons behind these differences are discussed in the light of the development in the variables for the OEE components. FIMIX-PLS segmentation is performed to discover whether the spray towers belong to latent groups, and even though the analysis reveals that a grouping into three segments is feasible, this segmentation cannot be substantiated by a pattern, which is intuitively reasonable, and it is therefore disregarded.

The practical part of the thesis is concluded by recommendations for the future initiatives, which Arla Foods should consider in order to enhance the success of the complete OEE roll-out. It is essential that the production sites are harmonized and the data basis is improved, so benchmarking can be attempted and best practices communicated throughout the organization. Furthermore, it is necessary to create buy-in and a sense of ownership of the OEE project, from the front workers to the top management, as well as incorporate a cost perspective in the OEE consideration, so decisions concerning OEE are well informed. Finally, Arla Foods is in the process of implementing several strategic initiatives, and the company must therefore identify the must win battles, so the efforts and resources can be directed towards these particular initiatives.

Structural Equation Modeling Approach to Overall Equipment Effectiveness in Arla Foods V

Table of Contents 1 Introduction .............................................................................................................. 1

1.1 Problem Statement .............................................................................................. 2

1.1.1 Structure of Thesis ..................................................................................... 3 1.2 Delimitation ......................................................................................................... 4

1.3 Research Methodology ........................................................................................ 5

2 Performance Management ...................................................................................... 7 2.1 Performance Measurement Systems ................................................................... 7

2.2 From Measurement to Management.................................................................... 8

2.3 Key Performance Indicators ................................................................................ 8

3 Overall Equipment Effectiveness .......................................................................... 12 3.1 Total Productive Maintenance........................................................................... 12 3.2 Overall Equipment Effectiveness ...................................................................... 13

3.3 Critique of Original OEE .................................................................................. 14

3.4 OEE as KPI ....................................................................................................... 16 3.5 Modification of OEE into OEE* ....................................................................... 19

3.5.1 Availability ............................................................................................... 21

3.5.2 Performance ............................................................................................. 22 3.5.3 Quality...................................................................................................... 23

4 OEE* Initiative in Arla Foods ............................................................................... 24 4.1 Profile of Arla Foods ......................................................................................... 24

4.1.1 OEE Initiative .......................................................................................... 25

4.2 Optimal Measurement of OEE* in Arla Foods GIN ......................................... 26 4.2.1 Availability in Arla Foods GIN ................................................................ 27

4.2.2 Performance in Arla Foods GIN.............................................................. 27 4.2.3 Quality in Arla Foods GIN ...................................................................... 28

5 Design of OEE* Study ............................................................................................ 29 5.1 Data Collection for OEE* ................................................................................. 29

5.1.1 Background Variables ............................................................................. 30 5.2 Productivity and Financial Performance ........................................................... 31

5.2.1 Proxies for Productivity ........................................................................... 31 5.2.2 Proxies for Financial Performance ......................................................... 32

5.3 Validity and Reliability ..................................................................................... 33

Structural Equation Modeling Approach to Overall Equipment Effectiveness in Arla Foods VI

6 Econometric Analysis of Longitudinal Data ........................................................ 34 6.1 Sample Size and Missing Data ...................................................................... 35 6.2 Descriptive Statistics ......................................................................................... 35 6.3 Regression Analyses.......................................................................................... 36

6.3.1 Model 0: Panel LS Estimation ................................................................. 36 6.3.2 Model 1: Cross-Sectional FE Estimation ................................................ 37

6.3.3 Model 2: Cross-Sectional FE Estimation with Week Dummies............... 40 6.4 Discussion of the Final Models ......................................................................... 42

7 Structural Equation Model .................................................................................... 44 7.1 Design of the Structural Equation Model .......................................................... 45

7.1.1 Formulation of Hypotheses I, II, and III .................................................. 45 7.1.2 Approach to Structural Equation Modeling ............................................ 45 7.1.3 Estimation Technique............................................................................... 48

7.2 Estimation of the Structural Equation Model .................................................... 52 7.2.1 Theoretical Re-Estimation ....................................................................... 53 7.2.2 Practical Re-Estimation ........................................................................... 54

7.3 Validation of the Structural Equation Model .................................................... 56 7.3.1 Theoretical Evaluation of Fit Criteria ..................................................... 56 7.3.2 Practical Evaluation of Fit Criteria ........................................................ 57

7.4 Interpretation of the Structural Equation Model ............................................... 59 7.4.1 Relationships and Scores ......................................................................... 59 7.4.2 Components and Structure ....................................................................... 62

7.4.3 Validation of Hypotheses I, II, and III ..................................................... 64 8 OEE* Results in Arla Foods GIN ......................................................................... 66

8.1 Effectiveness of Spray Towers in Arla Foods GIN ........................................... 66 8.1.1 Analysis of OEE* at Factory Level .......................................................... 67

8.2 Benchmarking across Spray Towers in Arla Foods GIN .................................. 69 8.2.1 Formulation of Hypotheses IV and V....................................................... 69 8.2.2 Repeated Measures ANOVA .................................................................... 69 8.2.3 FIMIX-PLS Segmentation ........................................................................ 75

8.2.4 Validation of Hypotheses IV and V .......................................................... 77 8.3 Applicability of OEE* in Arla Foods ................................................................ 78

8.3.1 Transparency of Utilization of Equipment ............................................... 78

Structural Equation Modeling Approach to Overall Equipment Effectiveness in Arla Foods VII

8.3.2 Best Practice ............................................................................................ 78

8.3.3 Concerns of OEE* Implementation ......................................................... 79 8.4 Recommendations for Further Roll-out of OEE* in Arla Foods ...................... 80

8.4.1 Harmonization ......................................................................................... 81

8.4.2 Buy-In and Ownership ............................................................................. 82 8.4.3 Bottlenecks and Interdependence of Production Sites ............................. 82 8.4.4 Data Basis ................................................................................................ 83

8.4.5 Cost Perspective....................................................................................... 83

8.4.6 Must Win Battles.................................................................................. 84

9 Further Studies ....................................................................................................... 86 10 Conclusion ............................................................................................................... 88 11 Literature List ......................................................................................................... 91

List of Appendices

Structural Equation Modeling Approach to Overall Equipment Effectiveness in Arla Foods VIII

List of Figures

Figure 1 Structure of Thesis .......................................................................................... 3

Figure 2 OEE and the Six Big Losses ......................................................................... 14

Figure 3 OEE* ............................................................................................................. 21

Figure 4 Cross-Sectional FE Estimation ..................................................................... 38

Figure 5 Cross-Sectional FE Estimation with Week Dummies and Whites Cross-Section Standard Errors .................................................................................................. 42

Figure 6 Approach to SEM .......................................................................................... 46

Figure 7 Path Diagram ................................................................................................. 47

Figure 8 PLS Algorithm .............................................................................................. 49

Figure 9 Model 0: PLS Algorithm ............................................................................... 52

Figure 10 Model 0: Bootstrapping .............................................................................. 53

Figure 11 Model 2: PLS Algorithm ............................................................................. 55

Figure 12 Model 2: Bootstrapping .............................................................................. 56

Figure 13 Validation of Model 2 ................................................................................. 58

Figure 14 Path Coefficients from PLS Estimation ...................................................... 60

Figure 15 OEE* Results .............................................................................................. 67

Figure 16 Benchmarking across Spray Towers ........................................................... 70

Figure 17 OEE* Mean Differences ............................................................................. 74

Figure 18 Fit Criteria of FIMIX-PLS Segmentation ................................................... 76

Structural Equation Modeling Approach to Overall Equipment Effectiveness in Arla Foods 1

1 Introduction

The current global business environment has led to an increased need for performance management systems, as continuous attention to and improvement of key strategic areas are essential to align organizational activities with strategic objectives and sustain competitiveness. Performance management is not only a method for measuring performance, but a comprehensive framework permeating the underlying culture of the organization.

There is a tendency that organizations, which up until today have not focused on performance management, are initiating the implementation of key strategic activities

leading to the development of a comprehensive performance management framework. In manufacturing companies, the implementation of performance management is

particularly directed towards the optimization of the production and utilization of the production facilities. In these types of organizations, competitiveness is highly dependent on the availability and productivity of the equipment (Muchiri and Pintelon 2008), however, the effectiveness of equipment is difficult to assess, as many external and uncontrollable factors affect the individual machine. In spite of the abstract nature of the effectiveness concept, comprehensive research in the area has led to the development of several effectiveness measures, and many organizations have chosen to employ the measure overall equipment effectiveness (OEE) to increase the focus on utilization and productivity of individual equipment. However, empirical research has revealed that the method for measuring and computing OEE is ambiguous with certain limitations, and a critical analysis of the OEE measure is therefore interesting to conduct.

It is desired to evaluate OEE from both an academic and practical viewpoint, and it is therefore ideal to collaborate with a large manufacturing company already in the process of implementing OEE, so the present thesis can contribute to a successful implementation, and the company can serve as the basis for the study.

Arla Foods is an international production company, which has acknowledged the benefits of implementing performance management in the organization, and is currently in the process of launching OEE among several other value-creating initiatives. Collaboration with Arla Foods has been established in order to conduct the present


study, which can assist in the introduction of OEE, as well as in the complete roll-out of OEE in the entire production of all the business units.

1.1 Problem Statement The aim of the present thesis is twofold, as it is desired to provide a theoretical assessment of the measurement of OEE together with an evaluation of the practical aspect of OEE in Arla Foods.

The theoretical part of the thesis aims at testing the components and structure of OEE based on data from the spray towers in Arla Foods, and thereby attempts to clarify the following research questions:

How should the components and structure of OEE be specified?

i) Which variables best depict effectiveness losses when measuring OEE?

ii) How do availability, performance, and quality affect OEE?

iii) To which extend can a relationship between OEE and financial performance be established?

iv) Is the multiplication of availability, performance, and quality with equal weights the best expression of OEE?

The practical aspect of the thesis aims at analysing the implementation of OEE in Arla Foods through the following research questions:

What is the value of implementing OEE in Arla Foods?

v) How effective are the spray towers in Arla Foods GIN currently?

vi) How can benchmarking of the effectiveness of the spray towers in Arla Foods GIN be performed?

vii) What is the applicability of OEE as a key performance indicator in Arla Foods?

viii) What are the recommendations for further roll-out of OEE in Arla Foods?

The thesis will aim at answering the problem statement by means of analysis and discussion of empirical research, as well as knowledge gained at Arla Foods. For the


questions preparing the ground for statistical inference, i.e. questions ii), iii), iv), and vii), hypotheses will furthermore be formulated, tested and validated in the relevant chapters.

1.1.1 Structure of Thesis When striving to answer the problem statement, it is reasonable to structure the thesis according to the outline illustrated in Figure 1.

Figure 1 Structure of Thesis

Source: Own creation

The thesis commences with a general introduction to performance management and key performance indicators, and continues with a presentation and critique of OEE as an indicator of equipment effectiveness. Hereafter, the OEE initiative in Arla Foods is introduced, a research study is designed, and a preliminary analysis of the data set collected at Arla Foods is performed, before the twofold objective of a theoretical and practical OEE analysis is fulfilled. Finally, the thesis is completed by ideas for further studies, which consist of the issues surfacing in the theoretical and practical analysis that are interesting to investigate further, but is considered outside the scope of the present thesis.


1.2 Delimitation Since a significant part of the thesis relies on collection of data for OEE, it has been deemed necessary to set a terminal date for accepting alterations in terms of structural changes in Arla Foods, as well as further changes in the data basis, and it is therefore decided not to incorporate information obtained after April 30th 2010.

Eventually, it is desired to implement OEE across the entire production of Arla Foods, however, as it is unrealistic to successfully implement such a comprehensive initiative

at once, it has been decided by top management in Arla Foods to begin with the production sites of the business group Global Ingredients (Arla Foods GIN). In the production of milk powder and whey protein, the spray towers are considered the bottlenecks, and the present thesis is therefore purposely limited to the spray towers on the Arla Foods GIN production sites.

In addition, since it is already decided to continue with a complete roll-out of OEE, the objective of the thesis is not to make recommendations as to whether or not OEE should be implemented throughout the entire production in Arla Foods. Instead the thesis is limited to recommendations and suggestions as to how the full potential of OEE can be exploited, when attempting a successful implementation of the measure in the rest of the production.

At the OEE Workshop in March 20101, it was furthermore decided, in collaboration with the OEE Project Manager and the Production and Technology Development (PTD) managers at the respective production sites, that the study is limited to the fourth quarter of 2009, and that all data therefore is collected retroactively, thereby accepting the constraints this decision may impose on the analysis. In addition, it was decided to refrain from collecting data for all spray towers, and the PTD managers at the respective plants therefore made a selection of spray towers, for which the data is collected. In this connection, it must be mentioned that the production site Visby is not represented in the selected sample, since it has not been possible to retrieve any data from this plant.

Arla Foods GIN Supply Chain Development has developed a dashboard consisting of selected key performance indicators, and even though it is relevant to evaluate OEE in

1 A brief description of the workshop can be found in section 4.2.


the light of the other indicators in the dashboard, it is considered outside the scope of this thesis, and only OEE will be evaluated.

In addition, it must be noticed that the econometric analysis performed in the thesis is not an objective in itself, but simply a preliminary exercise to discover the patterns and tendencies in the data set and assess the applicability of a structural equation model on this data. A comprehensive econometric analysis of the issues that are not relevant for the application of the structural equation model, is therefore purposely omitted. It must be mentioned that when discussing statistical significance, the significance level is set at = 0.05, if nothing else is stated in the thesis.

1.3 Research Methodology The methodological approach to the present thesis is based on the fundamental beliefs of post positivism, in which prediction and control is the aim. The ontological aspect implies a focus on critical realism, where reality exists, but can never fully be apprehended, as it is impossible to truly perceive the real world due to peoples imperfect sensory and intellectual abilities. With regard to the epistemological stance, the post positivistic approach focuses on a modified objectiveness, where objectivity is the guiding ideal, which can only be approximated and not attained. In the attempt to approach the ideal, emphasis is placed on the critical tradition implying that research is grounded in and consistent with existing traditions of the field. Furthermore, emphasis is placed on a modified experimental and manipulative methodology, in which, first of all, the triangulation of data, theories, investigators, and methods is central, as the human sensory and intellectual mechanism cannot be relied upon. Secondly, post positivism allows for many imbalances necessary to achieve realistic and objective research. Particularly important is the imbalance between internal and external validity, in which the researcher must sacrifice the degree of generalization of the findings to achieve internal validity. In order to compensate for the imperfect intellectual abilities, development of critical thinking is necessary. From a methodological viewpoint, this will create the grounds for deductive reasoning, where the hypotheses in the study are theoretically founded and lead to a confirmation or rejection of the original theories (Guba 1990).

The aim of this thesis is to attempt to verify the theory of OEE by means of an empirical study in Arla Foods, acknowledging that the study is not able to fully reproduce the


objective reality, and in order to approximate this reality, a triangulation of theoretical and empirical research is performed. The study is initiated by a deductive approach seeking to verify or falsify the well-founded theory, however, it is desired to create a basis for an inductive approach of generalizing the findings.

The research process for the present study is inspired by the validity network schema, stating that research must include some set of concepts, methods, and focal problems of interest, which entail a combination of the conceptual, methodological and substantive domains. Depending on the order of the application of the domains, three different approaches to the research process are generated, i.e. the experimental, theoretical, and empirical paths (Brinberg and McGrath 1989). The theoretical path is chosen in this study, as it is desired to build a set of hypotheses based on elements from the conceptual and substantive domains and test them by evaluating them with the appropriate techniques from the methodological domain. This approach is chosen, since the main focus is to formulate and test hypotheses regarding the concept of OEE and its application in Arla Foods. The focal point of the thesis is derived from the initial research, which has revealed OEEs potential insufficient predictive ability of the equipment effectiveness, as well as the possible inappropriateness of OEE as a performance indicator in Arla Foods.

The present thesis is characterised by both an academic and practical orientation, as it is the objective to test the theoretical basis for OEE, as well as to evaluate the OEE implementation in Arla Foods. According to Brinberg (1986), an academic orientation refers to conducting research, focusing on concepts and the relations between those concepts, whereas a practical orientation refers to conducting research focusing on a system, organization, or set of events in the real world. The difference between the orientations is the purpose guiding the study, and the objectives the researcher attempts to maximize. Due to the nature of the present thesis and the triangulation of the academic and practitioner approach, the authors expect to face decisions requiring certain compromises in order to satisfy both orientations in terms of theoretical tests of OEE, as well as problem oriented research necessary to conduct Arla Foods OEE study in a satisfactory and understandable manner.


2 Performance Management

The drive for quality management in the last couple of decades has enhanced the interest for performance measurement and management systems, as well as for the implementation of key performance measures, which is considered an important part of managing strategy (Eckerson 2009). There are several different approaches to and studies about performance measurement and management systems, but common for these approaches is the recognition of key performance indicators (KPIs) as an essential tool for alignment of strategy and performance.

2.1 Performance Measurement Systems In the late 1970s and 1980s, academic interest began to move towards a more balanced performance framework (Keegan, Eiler and Jones 1989), due to the dissatisfaction with traditional backward looking accounting-based performance measurement systems (Bourne et al. 2000). Researchers began to propose performance measurement systems that were more balanced between internal and external measures, as well as financial and non-financial measures (Keegan, Eiler and Jones 1989). Kaplan and Norton (2005) introduced the Balanced Scorecard, among other performance frameworks, which was intended to bring balance by focusing on external success as well as internal performance, and give an early indication of future performance while still recording achievements from the past. In the creation of the Balanced Scorecard, Kaplan and Norton (2005) emphasized the role of the performance measurement process in the development of the organizations strategy.

As formulated by Simons (2000), a performance measurement system is information systems that managers use to track the implementation of business strategy by comparing actual results against strategic goals and objectives.

There is strong consensus that performance measures should be derived from strategy, provide links between business unit actions and strategic plans, and enable monitoring of the progress towards organizational goals (Keegan, Eiler and Jones 1989). In order to ensure the alignment of strategy and performance, it is necessary to implement a performance measurement system and integrate KPIs.


2.2 From Measurement to Management It is important to realize that measurement of performance is not the end goal, but a tool for more effective management. The results from performance measurement only

indicate what has happened, not how to take action. In order to make effective use of performance measurement, a transition towards performance management must occur.

According to the Procurement Executives' Association (1999), performance management is the use of performance measurement information to help set agreed-upon performance goals, allocate and prioritize resources, inform managers to either confirm or change current policy or program directions to meet those goals, and report on the success in meeting these goals.

Focusing on the balanced frameworks led to the need of measuring performance in the light of its future potential. Performance management creates the context for and the measures of performance, and guides the actions to be taken based on the information gathered when measuring performance. Performance measurement is therefore the practical and technical exercise within the much wider performance management practice (Ferreira and Otley 2009).

A performance management strategy can be seen as a four step cycle, which involves creating strategy and plans, monitoring the executions of those plans, and adjusting activity and objectives to achieve strategic goals (Eckerson 2009). The cycle revolves around metrics and data, and provides a framework for measuring the effectiveness of strategic and management processes. These performance metrics should be captured in a performance dashboard, which visually presents a system for users to measure, monitor, and manage the metrics, also referred to as KPIs. It is important to present the performance metrics in a meaningful and structured way, in order to enable the users to gain knowledge and insight from the measurements. The metrics should reflect and derive from the organizational goals, which is why the choice and the handling of these metrics are of great importance.

2.3 Key Performance Indicators In order to develop successful performance measurement and management systems, the purpose and objects of the measurement must be defined, i.e. why measure? and what to measure?. The literature is very confident about the importance of


implementing performance measurement and management systems, but the challenge is to implement and measure the right metrics. Most KPIs measure events in the past, and it is important to remember that information based on the past is only interesting, when it helps understand the potential for success in the future (Lebas 1995).

As stated by Amaratunga and Baldry (2002), measurement provides the basis for an organization to assess how well it is progressing towards its predetermined objectives, helps to identify areas of strengths and weaknesses, and decides on future initiatives, with the goal of improving performance. In empirical literature, there is strong consensus of the value of measuring performance, however, it is important to determine whether all measures are KPIs or merely other relevant measures of performance.

Parmenter (2010) suggests four categories of performance measures; Key Result Indicators (KRIs) state how a person, unit, or company has done in a perspective or critical success factor, Result Indicators (RIs) state what they have done, Performance Indicators (PIs) state what to do, and Key Performance Indicators (KPIs) state what to do to increase performance dramatically. Organizations must be aware of this distinction in order to be able to analyse and identify the core representative measures driving organizational objectives and goals. Furthermore, it is important to recognize that irrespective of the chosen indicator, a performance measure can at best pinpoint where to improve performance, but fails to provide information on the actions necessary for the improvement, and a qualitative analysis is therefore always suitable in combination with performance measurement.

The method for measuring performance has many different names, such as KPIs, PIs, performance measures, performance metrics, etc., but there is consensus of the importance of creating and monitoring the right measures. The challenge is to design measurement metrics, which can act as a tool for the management to monitor, control,

and improve the organization. The metrics are indicators that measure progress towards and achievements of certain goals. The measurements described will from here on be referred to as KPIs.

Parmenter (2010) defines KPIs based on seven characteristics. The metric must be non-financial, be measured frequently, be acted on by CEO or senior management, clearly indicate the actions that are required by staff, tie responsibility to a team, have significant impact, and encourage appropriate action. A KPI therefore focuses on those


aspects of organizational performance that are most critical for the current and future success of the organization.

Eckerson (2009) describes six important elements of a KPI, i.e. strategy, targets, ranges, encodings, time, and benchmarks. A KPI is characterized by the fact that it is measureable, encompasses a strategic objective, and measures performance towards a goal. The goal associated with a KPI is known as a target and is specified by a measurable outcome. This target may be a specific point or a range of values, within which the metric should lie. If the KPI falls outside this range and crosses a specific threshold, the visual display in the dashboard would change, and indicate a failure or success in reaching the target. Furthermore, it is common to specify certain timeframes

as to when the KPI should be measured, and as to when the target outcome should be achieved. Finally, KPIs become really interesting, when benchmarking is possible, as this is a direct point for improving performance.

Many authors have supported the concept of integrating the SMART (Specific, Measurable, Attainable, Realistic and Time-sensitive) criteria when implementing KPIs and pursuing goals in an organization. This concept ensures that each indicator is aligned with the strategy and appropriate for further actions and analysis (Shahin and Mahbod 2007).

KPIs can be divided into two fundamental types: output KPIs, which measure past activity, and driver KPIs, which measure activities with significant impact on output KPIs (Eckerson 2009). It can be challenging to define accurate KPIs that drive future performance, and it is important to differentiate between KPIs and operational measures, which are relevant to measure, but not necessarily central to the strategy. In a large organization with a complex structure, issues are raised as to how to utilize, align, and integrate these performance measures in the various parts of the organization. In this connection, it would be beneficial to compute different dashboards, representing the different levels in the organization, and recognize that these various levels need different information and amount of details. The strategic dashboard enables the senior executives to execute strategy, manage performance, and drive new or optimal behaviour across the enterprise. Managers can optimize the performance of the people and processes under their supervision by using the tactical dashboard, whereas the operational dashboard enables frontline workers to monitor and control core processes


on a daily or weekly basis. Therefore, in order to potentially achieve the full, successful implementation of performance systems, the goals must be completely aligned throughout the organization. If each level of the organization implements performance

measures, useful for fulfilment of the organization goals, the overall measurements of

KPIs are optimized at top level, and ownership of the initiative exists from frontline workers to top management (Eckerson 2009).

Implementation of KPIs in performance management and measurement systems is of great value to an organization, provided that the right measures and goals are chosen. Empirical literature suggests that goal setting, especially specific challenging goals,

results in improved performance and productivity (Shahin and Mahbod 2007). The identification of KPIs helps the entire organization to work in the same direction, to reach the same objectives, as well as to create transparency and clarity throughout the business. Implementation and monitoring of goals and measures must be adapted to the multiple layers of organization in order to create the necessary ownership of the project, as well as an understanding of the alignment of measurement and strategy.


3 Overall Equipment Effectiveness

Simultaneous to the introduction of performance management systems aligning measurement with strategy, new schools of thought appeared, which focused on increasing productivity in manufacturing companies.

3.1 Total Productive Maintenance Total Productive Maintenance (TPM) was developed in the 1980s. It originates partly from the tactical approach to machinery maintenance in the American military, which

emphasizes preventive maintenance throughout the lifetime of the asset, and partly from Total Quality Management philosophies in Japan, which integrates all company departments in the maintenance process. The American and Japanese practices have affected maintenance management during its evolution from catastrophic maintenance management, which had a reactive approach to maintenance, to TPM in which maintenance of equipment is regarded as a vital part of the business, and no longer considered a non-value adding activity (Pophaley 2010). TPM is a maintenance approach focusing on continuous improvement in terms of maximizing output and operating equipment ideally and effectively (Nakajima 1988). In order to achieve this objective, TPM strives to maximize equipment effectiveness, establish a preventive maintenance system throughout the lifetime of the asset, integrate all relevant business departments, involve all employees, and promote TPM through motivated and autonomous group activities (Rich 1999).

In order to achieve the objective of maximizing overall equipment effectiveness, it is necessary to identify the losses in effectiveness, so these can be eliminated. Nakajima (1988) categorized effectiveness losses into six key areas named the six big losses: breakdown loss, setup and adjustment time, idling and minor stoppages, speed loss, yield loss and quality loss. The first two losses constitute the loss in time, when the equipment is available for production, whereas idling, minor stoppages and speed loss reduce production speed, and yield and quality loss represent the loss in quality. The significance of the individual category of loss varies between production plants, but reductions in, and preferably eliminations of, each type of loss are key objectives of TPM, and a holistic approach to assess the full picture of the effectiveness losses is therefore deemed necessary.


3.2 Overall Equipment Effectiveness In 1988, Nakajima (1988) introduced Overall Equipment Effectiveness (OEE) as part of the concept of TPM. OEE was proposed as a metric for evaluating the progress of TPM and achieving a production site with zero loss. OEE identifies the six hidden effectiveness losses, which are activities that absorb resources but create no value, and which were not possible to detect previously, when only equipment availability was considered (Jeong and Phillips 2001). OEE was designed to measure total performance of individual equipment in terms of the availability and performance of the equipment and the quality of the output produced, and OEE thereby creates an awareness of how effectively the equipment is utilized and operated.

The original definition of OEE by Nakajima (1988) is the multiplication of the equipments availability, performance, and quality:

OEE = Availability Performance Quality (1)

where the components are calculated as follows:

Availability = Planned Operating Time DowntimePlanned Operating Time

(2)

Performance = Theoretical Cycle Time Produced QuantityActual Operating Time

(3)

Quality = Produced Quantity Defect QuantityProduced Quantity (4)

The starting point for the OEE calculation is the planned operating time, which is the scheduled downtime such as holidays and scheduled maintenance subtracted from the total time (24 hours a day). The availability rate of the equipment is the fraction of the actual operating time, which is the planned operating time minus the unforeseen downtime, and the planned operating time, as seen in equation (2). Equation (3) shows that the equipments performance rate is the fraction of the smallest production time theoretically possible to the actual production time. Finally, the quality component is calculated as the rate of conforming output to the total output produced, as seen in equation (4).


The availability component captures the breakdown losses and setup and adjustment time, whereas the performance component includes idling, minor stoppages and speed loss, and the quality component encapsulates the yield and quality loss.

Figure 2 OEE and the Six Big Losses

Source: Nakajima (1988)

Eliminating all effectiveness losses, and achieving the zero loss objective, will yield an OEE of 100%. However, as in many cases this is not a realistic target, Nakajima (1988) has defined the optimal values for the OEE components. Ideally, the availability rate exceeds 90%, the performance rate is above 95% and the quality rate surpasses 99%, which results in world class performance and an OEE of 85%.

3.3 Critique of Original OEE The original OEE definition, which is presented above, has been subject to various points of critique. The purpose of OEE was originally to compare the performance of an organizations equipment to world class performance across various types of equipment. However, this objective of OEE has been questioned, and it is argued that it is only reasonable to benchmark equipment performance, if the equipment compared is exactly identical and performs exactly the same functions. The primary purpose of OEE should therefore be a visualization of the utilization of the equipment and an indicator of improvement or decline in effectiveness, as internal as well as external benchmarking is restricted (Pophaley 2010).

Moreover, it has been debated whether the chosen time base, from which the availability component is calculated, is the most appropriate. In the original definition of OEE, the planned operating time is the starting point, which means that planned downtime, such as holidays, lunch breaks, preventive maintenance etc., is not included


in the time base, and availability is therefore not measured against 24 hours a day. From one viewpoint this seems appropriate, as it is misleading to consider any planned downtime as a loss in equipment efficiency, when the equipment is not scheduled to operate, and therefore only effectiveness losses caused by the equipment should be accounted for by the equipment (De Ron and Rooda 2006). However, when aiming at visualizing and maximizing the utilization of the equipment, it is favourable to include the scheduled downtime in the time base (Ljungberg 1998). It is particularly important to modify the definition of OEE when operating in capital-intensive environments with high-capital cost equipment, as the utilization of the equipment is even more important

under these circumstances (Jeong and Phillips 2001). It is suggested by several authors that the time base is extended to 24 hours a day, as it is theoretically feasible to constantly utilize the equipment (Muchiri and Pintelon 2008). It is important to notice that the size of the OEE measure depends on the time base, and the larger the time base, the lower the OEE. It is therefore not possible to compare OEE results with different time bases.

Another point of debate is the difference between integrated and stand-alone equipment. OEE was originally intended to estimate the effectiveness of the individual, stand-alone equipment, but as the metric is influenced by various external factors such as the operator, input material etc., OEE actually measures the effectiveness of equipment integrated in a production line, i.e. the effectiveness of the equipment, including the effects from the equipment before and after the given equipment as well as other external effects. It can be argued that it is only appropriate to attribute effectiveness losses that are actually caused by the equipment to OEE. If the equipment is idle due to lack of feed or lack of operator, this is per se not attributable to the equipment, even though it decreases the utilization of the equipment (De Ron and Rooda 2006).

Muchiri and Pintelon (2008) substantiate the belief that OEE is unsuitable, because it is limited to individual equipment and no machine is in fact isolated. On the other hand, it can be argued that there is a general risk that productivity measures are influenced by external factors such as skills of operator, type of product etc., and OEE is therefore not particularly disadvantaged.

Furthermore, the OEE target levels have been heavily debated, and empirical literature does not agree with Nakajima on the target levels defined in 1988. Some authors argue


that an OEE target of 50% is more realistic, whereas others believe that OEE figures are acceptable between 60% and 75% or even between 30% and 60% (Dal, Tugwell and Greatbanks 2000). It must be kept in mind that the appropriate target level is of course dependent on the time base, so if OEE is modified and the time base extended, a lower OEE target is acceptable.

Another interesting point of critique is the lack of cost perspective, which would be relevant to incorporate, since a tradeoff between performance and quality is likely to occur, and it is therefore necessary to be able to estimate the price of this tradeoff. E.g. if the equipment operates at a higher speed, which increases performance, there is a risk that the quality is reduced. Moreover, it could be interesting to consider the tradeoff between maintenance and breakdowns, as it must be assessed whether the costs related to preventive maintenance are less than the costs related to breakdowns. This critique of OEE is supported by the empirical literature, in which there is consensus that performance measures, such as OEE, must be based on a thorough understanding of cost relationship and behaviour (Keegan, Eiler and Jones 1989).

Due to the before mentioned points of critique, it is essential for successful use of OEE that it is not applied in isolation, but in connection with other performance and operational measures, so an overview of the context can be obtained and the reality approximated.

Finally, it is assumed that the three components of availability, performance and quality are equally important to OEE, however, this may not be the case (Pophaley 2010). It is questioned whether the multiplication of the three components with equal weights is the appropriate choice, and it is suggested that the three components carry different weights (Raouf 1994). In this thesis, a study of the components and the weights will be performed, as it is essential that the weights are correctly specified to avoid a wrongful and misguiding OEE measure, and research question iv) is therefore constructed to shed light on this issue.

3.4 OEE as KPI Even though a significant part of the literature discusses the downsides of OEE, the measure is also supported as a meaningful and immediate expression of productivity or effectiveness of equipment. Harris (2009) sees OEE as an important measure, as it combines three KPIs into a single calculation. The interpretation of availability,


performance, and quality as individual KPIs may be an exaggeration, however, OEE is a concise indicator because it includes more aspects in the calculation of productivity or effectiveness of equipment.

There are several benefits of implementing OEE as a KPI. First of all, it is important to acknowledge that what is not measured, cannot be improved. No process can be improved, nor any goal achieved, if the performance is not measured and monitored. Furthermore, monitoring the effectiveness of equipment gives a good indication of the utilization of the equipment, and this visualization helps detect potential bottlenecks in a production site, as the different equipment may be dependent on the completion of the products in earlier parts of the production. In determining status quo of the effectiveness, the OEE measure creates a potential breeding ground for internal benchmarking across the production site or against previous years performance. The monitoring and benchmarking of performance create an incentive to perform better, and target and goal setting is known to have a positive effect on productivity (Shahin and Mahbod 2007).

It is relevant to evaluate OEE as a KPI in the light of the different theories defining a KPI, and thereby determine whether OEE serves as a well-defined and useful KPI. When evaluating the use of OEE as a KPI in a strategic context, the SMART criteria are used in order to gain an understanding of the overall aspects of OEE as a metric used to measure progress towards the achievement of a specific goal. However, when discussing a more practical application of OEE, it seems relevant to incorporate various KPI strategies and definitions, and thereby provide a more practical view on how to define a KPI.

When establishing whether OEE is more than an operational metric, focus must be held on the strategic aspect of OEE. A KPI must have a strategic objective and measure performance against a goal, whereas an operational measure merely refers to the

measurement of business activity (Eckerson 2009). Even though the components and variables, which constitute OEE, are identified and measured at an operational level, and activities to improve production are initiated at a tactical level, the aggregated OEE measure serves as a part of the foundation for top management decision making, and it is therefore considered a strategic KPI.


The SMART criteria take strategic and organizational goals as its starting point, which is why this is not a specific criterion in the framework. The goal of improving

productivity through OEE must be specific and detailed, in order not to lose track of the measurement and to be able to hold someone responsible for the execution and continuous improvement of OEE. As OEE is a measure used to monitor and increase the effectiveness of the equipment, which is derived from the objective of increasing productivity, OEE is a specific measure, which supports the goals of the organization.

A key criterion is that the KPI is measurable, either in a qualitative or quantitative form, against a specific target or standard of expectation, which is fulfilled by the OEE measure, even though it is questioned whether OEE measures the effectiveness of stand-alone equipment, as it is intended to. It can be hard to establish precisely where to measure OEE, as it can be complicated to determine the exact starting and finishing point for various products moving through a chain of production processes.

Any goal should be attainable and reasonable, but it should also be challenging and demand an effort in order to be successful. It is important for a KPI to measure performance against a target, so the success can easily be established. Originally, Nakajima (1988) stated that a reasonable target for an OEE calculation is 85%, as seen in section 3.2. However, any organization should determine an OEE target internally, as the OEE measure can be dependent on different factors not visible in the OEE metric, e.g. number of product shifts, age of the equipment, time base of the calculation, etc. Furthermore, it is important that the targets are set, so that they are demanding, but still realistic in the given organization, and so the targets are achievable provided that the equipment operates effectively.

Finally, it is important that any goal has a time frame for completion. It is important to establish the time frame, so it can be determined whether the targets have been met in time. OEE can be calculated on a daily basis, so an OEE measure could possibly have a daily target. However, this makes little sense, since certain parts of the production activities, e.g. cleaning and maintenance, are related to the production over a longer period, and should not be attributed to only a few days. It would therefore be more reasonable to aggregate the OEE measures on a weekly or monthly basis.

When evaluating OEE in terms of the SMART goal setting framework, it makes sense to consider OEE a KPI. There are, however, certain pitfalls for using OEE as a KPI and


thereby being a measure for strategic goals in the organization. These pitfalls will probably not occur until the implementation, and they are therefore more practical considerations associated with using OEE as a KPI. One potential problem is the point stated by Parmenter (2010), namely that a KPI should be sufficiently embedded in an organization to be tied to a single team. The OEE measure concerns and engages staff from many departments, because the measure is built upon three different components, which may have to be measured in different parts of the organization.

Eckerson (2009) identifies a potential issue in terms of benchmarking. As previously stated, it can be risky to compare targets of OEE across industries and organizations, as OEE is not an isolated measure unaffected by environmental factors. Benchmarking therefore primarily becomes relevant in terms of internal benchmarking for the same equipment across a period of time.

Furthermore, De Ron and Rooda (2006) criticizes OEE as a diagnostic measure explaining the causes behind the effectiveness losses, because OEE only indicates how effective the equipment is, but not why it is more or less effective. This conflicts with the initial definition of a KPI by Parmenter (2010), as a KPI should give directions as to how to increase performance dramatically. Even though, OEE may not explain direct causes, it still visualizes which parts of the production that are problematic and identifies potential bottlenecks. Therefore, the indirect cause of the effectiveness of the equipment can be identified through a more transparent production.

Different authors have different views on the definition of a KPI, which criteria it must fulfil to be useful, and how it should be implemented and integrated in the organization. Referring to the discussion above, OEE fulfils the majority of the criteria for a useful KPI, which can help an organization achieve its objective of increased productivity and effectiveness of the equipment. However, since OEE can depend on various both internal and external factors, such as production structure, equipment diversity across the organization, etc., it is important to compare this evaluation of OEE as a KPI with

the overall critique of OEE, in order to conclude whether OEE is a relevant KPI to implement in the organization in question.

3.5 Modification of OEE into OEE* With origin in the OEE definition by Nakajima (1988) and considering the above critique and discussion of this metric, as well as the constraints imposed by the data


collection at Arla Foods GIN, an attempt is made to create a modified OEE measure. As an important objective of OEE is to visualize the utilization of the equipment and eliminate all effectiveness losses, the original time base is extended to include scheduled downtime, as recommended by e.g. Muchiri and Pintelon (2008) and Dal, Tugweel & Greatbanks (2000). Equipment availability is calculated based on 24 hours a day, as this gives the full picture of the utilization of the equipment. Furthermore, the inclusion of planned downtime in the time base hinders the manipulation of the OEE measure in terms of unrealistic downtime by placing an emphasis on preventive maintenance as a means of minimizing unforeseen downtime (Dal, Tugwell and Greatbanks 2000). Thus, effectiveness losses unrelated to the equipment itself are also included in the modified OEE calculation, and it is therefore only reasonable to consider other external factors affecting the effectiveness of the equipment too. In continuation

hereof, it is therefore decided not to consider the individual equipment in complete isolation, but to determine individual equipment effectiveness while accepting the equipment as an integrated part of the production site. The modification contradicts the original idea of a metric measuring the effectiveness of individual, isolated equipment, however, the modified OEE metric ensures a more useful and beneficial measure, as it enhances transparency and better explains the reasons for losses in effectiveness.

Inspired by Jeong and Phillips (2001), it is furthermore decided to make alterations as to the categorization of the six big losses in OEE. Nakajima (1988) defined quality loss as quality and yield loss, however, it is chosen to disregard the yield loss in this component and instead include the yield loss in the performance rate, as the yield loss is in essence the loss in output resulting from start-up of the equipment. In addition, idling and minor stoppages are not included in the performance rate, but represented by the availability loss, as it is considered unplanned downtime.

The modified version of OEE is termed OEE*, as seen in Figure 3, and forms the basis of the analysis performed in the present thesis. This modification is substantiated by the construction of the equipment-level productivity measure Total Equipment Effectiveness Performance, which includes planned downtime in the total time horizon, so the time base is extended to 24 hours a day (Muchiri and Pintelon 2008).


Figure 3 OEE*

Source: Based on Nakajima (1988) and modified in collaboration with Arla Foods

The three overall components of OEE* remain the same as for OEE, however, the underlying reasons for effectiveness losses are scrutinized to ensure that all effectiveness losses are covered sufficiently. Also, it is attempted to break OEE* down to as many types of effectiveness losses as possible, in order to increase the transparency and make OEE* a better diagnostic measure.

Based on the modification of OEE into OEE*, the following sections aim at answering research question i), by determining the variables which best depict the effectiveness losses when measuring OEE*.

3.5.1 Availability The empirical literature suggests that availability losses can be broken down to a greater extent than proposed in the original definition of OEE. Availability losses encompass both non-production time, which is scheduled, and unforeseen downtime, and it is relevant to distinguish between these two categories (Smith and Hinchcliffe 2006).

This tendency is substantiated by Turkcan (1999), who distinguishes between deterministic and stochastic reasons for equipment unavailability. Deterministic unavailability is downtime anticipated on beforehand, whereas the stochastic unavailability is the non-productive time not known a priori. Availability of equipment is constrained by several factors, but in relation to equipment effectiveness it is


necessary to distinguish between factors, which cause the equipment to be non-productive, and factors making the equipment unavailable for a particular product, as the latter do not constitute a loss in effectiveness. As discussed by Turkcan (1999), facility unavailability, lack of operators, and preventive maintenance are the availability constraints necessary to consider in relation to the above criteria. However, as it is assumed that the facility is available 24 hours a day as seen in Figure 3, the unavailability of the facility becomes obsolete.

In addition to preventive maintenance and lack of operator, it is furthermore relevant to measure the time for setup/adjustment and the changeover time when shifting from one product to another, separately (Muchiri and Pintelon 2008). In addition, standstill, idling, cleaning, and lack of feed constrain availability, and moreover, it is possible to specify breakdowns in detail by measuring people failure, machine failure, and line failure. By including the people failure and line failure, it is evident that the equipment is integrated and not considered in isolation.

Thus, the availability component in OEE* is the total time (24 hours a day) minus the time lost seen in Figure 3. The nature of these availability constraints is likely to vary depending on the production plant in question, however, it must be recognized that some of the time losses are deterministic, whereas others are stochastic.

3.5.2 Performance The performance rate expresses the actual performance of the equipment relative to its

optimum performance. The actual performance can be measured in terms of output per time unit, whereas the theoretical optimum performance is harder to determine. The best estimation of the ideal performance will vary depending on the company implementing OEE*. Possible measures of maximum capacity are the maximum output produced in the last x number of batches, maximum output produced in the last month, the output guaranteed by the supplier of the equipment etc., however, the product type must be considered when comparing actual output against maximum capacity. Regardless of the decision, it is essential for the performance rate that the optimum performance is a good proxy for the maximum output possible to produce, as the performance rate otherwise risks exceeding 100%. If actual capacity exceeds maximum capacity, it is evident that the proxy for maximum capacity is misspecified, since the actual output produced can only be tangent to the real maximum capacity and never surpass it.


3.5.3 Quality The quality component is defined as the ratio of conforming products to the total output produced. There are various viewpoints on quality, and depending on the product, the production site, and the company, there are different requirements for conformance. In some cases, there is a range in which the product is in conformance with the product standards, however, according to Taguchi and Clausing (1990), all deviations of a product from the specific target constitute non-conformance.

When turning to the non-conformance procedure of ISO 9000, which is a family of standards for quality management systems, there are four kinds of non-conforming products, i.e. rework, scrap, products used for alternative use, and products accepted by customers as is or with concession, as seen in Figure 3.


4 OEE* Initiative in Arla Foods

4.1 Profile of Arla Foods Arla Foods is a global dairy concern owned by Danish and Swedish milk producers. Its roots can be traced back to 1881, where independent milk producers in Sweden formed the first dairy cooperative, Arla Mejerifrening. In 1882, Danish milk producers followed and from there, the dairy industries in Denmark and Sweden experienced the same development towards a large, national dairy cooperative. In 1995, Swedish Arla and Danish MD Foods started cooperating, and in 2000 the two cooperatives merged into Arla Foods.

Today, Arla Foods operate on a global scale with production facilities in 13 countries around the world, sales subsidiaries in another 20 locations and more than 16.000 employees. In 2009, revenue reached DKK 46 bill. and resulted in a profit of DKK 971 mill. The company carries a wide range of well-known brands, which are sold primarily in Scandinavia, the United Kingdom, Germany, the Netherlands, and Poland, but also in the rest of the world. The product portfolio includes an extensive assortment of milk, butter, yoghurt, cheese, sauces, desserts and ingredients. Furthermore, Arla Foods is the largest producer of organic dairy products, and the focus on natural dairy products without artificial flavours, aromas and additives is emphasized by the company concept Closer to Nature, introduced in 2008.

Being a cooperative owned by 7625 milk producers in Denmark and Sweden, Arla Foods works to ensure that these milk producers can dispose of all their raw milk at the highest possible price. As one of the leading dairy companies in the world, Arla Foods has a strong position on the global market, however, the company has implemented an aggressive growth strategy to become the obvious choice for consumers in Northern Europe and Great Britain, and ensure that the milk producers continue to obtain the highest possible price for their raw milk. In order to achieve these objectives, Arla Foods focuses on developing the three key brands, Lurpak, Arla and Castello, and

prioritizes the refined whey protein products.

Arla Foods is divided into four different business groups, i.e. Consumer International focusing on cheese, Consumer Nordic handling milk, yoghurt, soups, sauces etc., Consumer UK, which is a separate business group in the UK, and Global Ingredients


producing and selling milk powder and whey protein. These milk-based ingredients can be applied in connection with fermented dairy products, ice cream, meat, bakery and infant, health and performance nutrition.2

The business group Global Ingredients (Arla Foods GIN) can furthermore be split into four business units, i.e. BU Milk Powder and BU Whey Protein, handling marketing, management and retail of milk powder and whey protein, respectively, BU Trading selling excess ingredients to industrial buyers, and Supply Chain, producing whey protein and milk powder and handling logistics. The ingredients are produced by six Arla Foods sites (HOCO, AKAFA, ARINCO, Denmark Protein (DP), Vimmerby and Visby) in Denmark and Sweden, and by a number of joint ventures and partnerships.

The vision of the supply chain of Arla Foods GIN is to be a world-class supply chain of dairy based ingredients, and this entails the best logistics and supply chain structure, the best spray drying and ingredients technology competencies, the highest quality standards, cost efficiency in all aspects of the supply chain and setting the technology level for value-added ingredients. The strategic ambition for the business unit is to become the global leader for whey production, achieve a leading position in milk based nutrition products and establish a leading cost position for milk based bulk products in Europe. In order to meet this objective, a number of initiatives have been formulated to be implemented in 2010 along with a dashboard of performance measures indicating how the strategy implementation is progressing.3

4.1.1 OEE Initiative One of the initiatives in the 2010 dashboard is the implementation of the equipment effectiveness measure, OEE, in the production of whey protein and milk powder. The aim of this project it to identify the bottlenecks in the production, visualize the utilization of the high capital cost equipment, and benchmark the effectiveness of the equipment over time and across production sites. The OEE activities are initiated on the spray towers, as Arla Foods GIN suspects this equipment to be the bottlenecks, and an increase in the effectiveness of the spray towers is therefore expected to result in substantial gain.

2 The authors are familiar with the organizational structure implemented in Arla Foods per 1st of May

2010, however, due to the terminal date set in the present thesis, discussion hereof is omitted. 3 This section is based on information from www.arla.com, annual reports and information provided by

the OEE project manager regarding organization structure and business units.


The OEE initiative is managed by the OEE Project Manager, and the roll-out and continuous data collection are performed in close cooperation with the PTD managers at the respective production sites. Since Arla Foods GIN wishes to visualize the utilization of the equipment 24 hours a day, and determine the effectiveness of the equipment as an integrated part of the production line, the OEE initiative is based on the modified OEE* measure presented in section 3.5.

4.2 Optimal Measurement of OEE* in Arla Foods GIN In March 2010, an OEE Workshop was held at Arla Foods headquarters in Viby. The OEE Project Manager, representatives from the LEAN Office, the PTD Managers from the six Arla Foods GIN production sites, and the authors of this thesis were present, and the objectives of the workshop were to define the optimum measurements for OEE* in Arla Foods GIN and to agree on a battle plan for the OEE* project going forward.

In order to define the ideal measurements, it is first of all necessary to determine the purpose of an OEE* implementation in Arla Foods. The OEE* implementation is initiated to visualize the utilization of the high-capital cost equipment, remove bottlenecks, and make it possible to benchmark effectiveness across time and across identical equipment. It is the intention that over time OEE* is rolled-out on all equipment on all production sites, and it is therefore important that OEE* remains a general measure, which can be applied on various equipment and that it is not modified into a very equipment-specific measure. It is chosen to start the OEE* roll-out on the

sprays4 on all Arla Foods GIN plants, as the sprays are the bottlenecks in the production of milk powder and whey protein, and a potential increase in the effectiveness of the sprays will be important for the productivity of the production of ingredients.

In addition, it is important to assess the possibilities and constraints as to the technological and operational feasibility on the various production sites in relation to continuous OEE measurement. The ideal measurements of availability, performance, and quality are determined by using OEE* as the starting point and role model, and taking the purpose of the OEE implementation and the operational constraints into account. The effectiveness losses of the equipment in Arla Foods can be expressed

4 The sprays at Arla Foods GIN production sites are used to extract the milk powder or whey protein from

the milk or whey. Liquid products are sent through spray towers under extreme pressure and high temperature, so only the powder and protein are left after the water has evaporated.


generally by means of the measures in bold, whereas the measures specific for the sprays in Arla Foods GIN are the sub-measures written in italics, as seen in Appendix A.

4.2.1 Availability in Arla Foods GIN The measures defined as being ideal for OEE* in section 3, can be applied directly to OEE* in Arla Foods without further explanation or modification. However, there are some effectiveness losses, which require alterations to be suitable for the sprays in Arla Foods GIN. When considering the planned downtime, it must be noticed that the time, when an operator is not present, is not measured on the sprays in Arla Foods GIN, as the sprays are the bottlenecks and operators therefore are assumed to be available at all times. The downtime due to lack of feed, which for the sprays entail no milk or whey, is split into scheduled and unforeseen lack of feed, as both these scenarios are plausible. Standstill is included as an effectiveness loss, however, standstill will often be caused by lack of feed and therefore included under this measure. Preventive maintenance is only recorded as a loss in effectiveness, if maintenance is performed when the equipment could be operating. As preventive maintenance on the sprays is typically carried out, when the spray is unavailable all the same, maintenance rarely causes losses in effectiveness. It is chosen to distinguish between cleaning in place (CIP) of the tower and CIP of the miscellaneous parts of the spray, as these are not necessarily performed at the same time. In order to make the unplanned availability loss of the OEE* measure as diagnostic as possible, it is favourable to distinguish between the different types of people failures, machine failures, and line failures. In relation to people failures, it is relevant to distinguish between a breakdown caused by the operator and planning errors resulting in a filled silo or sudden lack of feed. The machine errors can be analyzed in terms of which part of the spray is causing the breakdown, and the line failures will represent a power blackout, problems with the steam or a miscellaneous breakdown. Repair time is not included as a measure, as the time necessary to fix a breakdown is included in the breakdown.

4.2.2 Performance in Arla Foods GIN In relation to the performance loss, it is decided to measure the actual output produced per minute against the maximum output produced per minute in the same quarter the previous year, taking the product type into consideration. In order to reflect the maximum output possible to produce, it is necessary to consider the seasonal


fluctuations, as temperature and humidity have significant impact on the productivity of the equipment, and it is therefore chosen to apply the maximum output produced from the same quarter the previous year. Furthermore, it is relevant to take the product type into account, as the productivity is dependent on the type of whey protein or milk powder.

4.2.3 Quality in Arla Foods GIN The quality control of the output produced by the spray must be performed by measuring the quality of the milk and whey before entering the spray, as well as of the ingredients when leaving the spray, as this will provide the best estimate of the quality loss attributable to the spray as stand-alone equipment. Furthermore, it is essential to ensure quality control before the products enter the spray, as it is very ineffective to risk wasting the resources of the spray on milk affected by bacteria, when the spray is the bottleneck in the production of ingredients. The quality loss is the rate of non-conforming products to the total output produced, and ideally the non-conforming products are divided into scrap, rework, and products sold for alternative use or/and at concession.


5 Design of OEE* Study

The collection of data for OEE* was administered by the PTD managers on the five individual Arla Foods GIN production sites. The authors were invited to HOCO to participate in the data collection process and to get insight in the production of milk-based ingredients and in the importance of the sprays as the constraining factor in the production process, whereas the data from the four other production sites was collected and sent by the PTD managers to the authors.

5.1 Data Collection for OEE* The data collection process was challenged by the fact that the respective production plants have different product portfolios, production processes, and equipment. The sites therefore have different needs and challenges and are managed in different ways.

The data is collected for selected sprays5 in the fourth quarter of 2009 on five out of six Arla Foods GIN production sites, and a list of the definitions of the spray towers can be found in Appendix B. It would have been ideal to use the optimum measures defined at the OEE Workshop in March and collect the data accordingly. However, due to the time constraints on the thesis, it is decided to collect the data retroactively, and the complete dataset collected can be found in Appendix E. This implies that data for all ideal measures is not available for the fourth quarter of 2009, and this has particular consequences for the availability component of OEE*, which is less detailed and nuanced than originally intended.

Furthermore, the data possible to retrieve and the level of detail vary significantly from site to site, and even from spray to spray. It is therefore necessary to create a common ground of data, which can be retrieved for all sprays. The planned availability losses must therefore be expressed in terms of standstill, CIP of tower, miscellaneous CIP and setup time, whereas idling and breakdowns must explain the unplanned non-productive time. It is not possible to decompose breakdowns into more detailed measures, as this decomposition is not recorded and would be based entirely on memory. Moreover, it is not possible to distinguish between setup time and time for product shifts, so this is all recorded as setup time, and the frequency of product shifts will be indicated as a separate operational measure in connection with OEE*.

5 The sprays were selected by the PTD on the production plant.


The performance component is defined as the rate of the actual performance to the maximum performance possible. Since data is only collected retroactively for the fourth quarter 2009, it is decided to apply the maximum performance for each product on each spray as the measure against which the actual performance is rated. Going forward, the maximum performance in the same quarter, but previous year, will be applied.

The quality loss caused by the spray as stand-alone equipment is not possible to determine, as the quality is currently not controlled until the product is bagged. The quality loss actually measured does not represent the quality loss from the spray as stand-alone equipment, but the quality loss in the entire production of whey protein and milk powder. Quality problems attributable to the spray mostly arise when the water content is higher than acceptable, however, when the quality loss is not isolated to the spray, the loss will therefore capture e.g. bacterial problems not caused by the spray, and the quality loss will be greater than if only measured on the spray.

The underlying assumptions for the classification of the data retrieved from the Arla Foods GIN production plants can be found in Appendix C. These assumptions have been necessary to make in order to obtain a similar data basis for each spray tower, which is detailed enough to figure in the desired statistical analysis. If these assumptions were not made, the data bases would be very different, and the number of variables possible to obtain, only based on the least common denominator, would be insufficient. The assumptions entail that the results from the analyses are less valid, however, since they are based on expert assessments from Arla Foods, they are considered the best estimates.

5.1.1 Background Variables As previously discussed, successful use of OEE* as a KPI requires that OEE* is considered in connection with other operational measures or performance indicators, as OEE* should not be viewed in isolation. It is therefore chosen to collect data that can enhance the diagnostic power of OEE* and contribute to the explanation of the reasons behind the size and development of OEE*. The choice of background variables has been verified by PTD managers as well as the OEE Project Manager at Arla Foods GIN. For the purpose of background variables, data is retrieved on the number of tower CIPs and product shifts pe

Structural Equation Modeling Approach to Overall Equipment Effectiveness in Arla Foods

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