Developing a Performance Measurement System for Apparel Sector Lean Manufacturing Organizations in Sri Lanka

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2013 17: 293Vision: The Journal of Business PerspectiveP.S.T. Perera and H.S.C. PereraOrganizations in Sri Lanka

Developing a Performance Measurement System for Apparel Sector Lean Manufacturing

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Forster 293

Developing a Performance Measurement System for Apparel Sector Lean Manufacturing Organizations in Sri Lanka

P.S.T. PereraH.S.C. Perera

AbstractPerformance measurements systems were rarely adapted to changing manufacturing environments. Lean is one such practice aiming at productivity improvements. Non-cost performance improvements resulting from lean makes the inadequacy of traditional measurements for evaluating performance. Sri Lankan apparel manufacturers adapt lean manufacturing to meet global business challenges. However a study has not been done to develop an appropriate performance measurement system for lean environment in Sri Lankan apparel sector lean practicing companies. This study develops such a model from literature review and interview. Finally it is validated with the existing models.

Key WordsLean Manufacturing, Performance Measurement System, Apparel Industry

Vision17(4) 293–301

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IntroductionVarious management practices come into play in manufac-turing companies during past which improve operational performances in terms of quality, delivery, dependability, flexibility etc. As a result, only financial measures were not adequate to measure performance. Consequently numbers of Performance Measurement Systems (PMS) were developed which were capable of measuring both financial and non-financial performances. Even though the new approaches to PMS have been brought into adjust with respective organizational changes, it is noted that they are not best fit with the new practices and the changing organi-zational culture. However in this era of globalization and fierce competition amongst businesses there is a need to evaluate the real impact of operational philosophies on business performance.

Lean manufacturing is a practice that greatly influences organizational performance and culture. Bond (1999), Cua (2001) and Pont et al. (2008) have confirmed that the performance improvement in lean environment is not one-dimensional. Lean put more importance on non-financial measures than the financial measures. Therefore PMSs in lean manufacturing environment should provide measures and controls that support management decision making in terms of lean strategies.

None of the studies on a PMS for lean manufactuirng organizations considers stakeholder values, organizational

objectives and lean manfacturing principles together. There is a vacuum in the literature on how to integrate these in order to develop a proper PMS for an organization. Furthermore, a common PMS cannot be developed catering all types of lean practicing organizations as it is contingent on the national context and infrastructure practices in quality and workforce management (Phan and Matsui, 2010).

In Sri Lanka, lean is mostly practiced in apparel indus-try. The phenomenon discussed in the above section pre-vails among the PMSs adopted in Sri Lankan lean practitioners too. Moreover, tools and techniques in prac-tice vary from organization to organization, industry to industry, culture to culture, and country to country. PMS should be, therefore capable of identifying changes facili-tated by lean that are required for the particular organiza-tion. Therefore there is a void in the literature on a PMS suitable for lean manufacturing environment to Sri Lankan apparel companies. Therefore this article presents a PMS model which suits for lean manufacturing environment that captures operational performance and make a congruent between organizational goals, stakeholder requirements and lean concept for Sri Lankan apparel sector. It describes the background literature on PMSs in lean manufacturing setting and develops a conceptual model. Finally the PMS model is developed and validated for lean manufacturing apparel companies in Sri Lanka.

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294 Developing a Performance Measurement System

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Apparel Industry in Sri Lanka

Apparel industry is the strongest manufacturing sub- sector in terms of its contribution to the Gross Domestic Production, exports, foreign exchange earnings and employment generation in Sri Lanka. It employs 15 per cent of the country’s workforce, out of which the majority is women. It is a relatively low skilled, labour-intensive industry. As a result, country gets the comparative advan-tage of low cost of production. Majority of apparel exports are standard, low value added ready-made garments which primarily concentrate in casual wear. From recent past, considerable pressure is on the industry to reach higher standards of production and service due to significant changes in consumer demands, technology and competi-tion. Further, Sri Lankan apparel manufacturers face stiff competition from other developing countries of South and South East countries due to competitive priority is on low cost and catering to similar markets. The situation wors-ened with the phase out of quota regime in 2005 after which the companies were compelled to compete for its market share. In addition, China is emerging as a domi-nant force in the global apparel industry with its massive supply capability and low cost of production. Other chal-lenge faced by the industry is the Just-In-Time (JIT) production. Orders have become smaller, lead times have become shorter, and buyers demand complex products. Therefore the companies should respond quickly. To face this situation, the ‘stronger’ factories have introduced ‘supply management’ techniques, effectively networked with the others in the production process, and changed the manufacturing practices. Sri Lankan manufacturers should therefore develop strategies to face these challenges. Lean is one such strategy that can be used meeting these challenges.

However this concept is new to Sri Lankan industries. Only very few companies have implemented it, among which apparel companies are the pioneers. Moreover not all companies have implemented the same lean tools as well as the level of implementation differs. While some benefits through lean some claim that entire lean journey is a failure.

Lean Manufacturing

Lean manufacturing is a manufacturing paradigm that improves plant performance and enables meeting competi-tiveness. It encompasses numbers of tools such as Total Quality Management (TQM), 5S, JIT, Total Productive Maintenance (TPM), Kanban, pull production, value stream mapping, visual management and cellular manufacturing etc. Lean basically focuses on waste elimination.

It identifies seven key areas of such waste as overproduc-tion, waiting, transportation, motion, over processing, inventory and defects.

However, there is an absence of common definition for this concept. Generally lean manufacturing is described in terms of two points of view; from a philosophical perspective related to guiding principles and overarching goals or from the practical perspective of a set of management practices, tools, or techniques that can be observed directly. The aim of lean manufacturing is to produce finished products at the pace of customer demand with little or no waste through elimination of waste by concurrently reducing or minimizing supplier, customer, and internal variability (Shah and Ward, 2007). Further it lies on five basic principles of understanding customer value, value stream analysis, flow, pull and perfection (IFS Research and Development, 2004).

Performance Measures

Performance Measure (PM) is a metric that is used to quantify the efficiency and/or effectiveness of an action (Neely et al., 1995). They are lined to business perform-ance (Moss et al., 2007; Neely, 1999) and help productivity improvements. Therefore performance is frequently measured in terms of productivity. Business performance is multidimensional (Ketokivi and Schroeder, 2004; Neely, 1999) and can be classified into four distinct performance dimensions and so types of indicators as costs/productivity, time, flexibility and quality (Toni and Tonchia, 2001). They can be divided into two main categories as financial (cost) performances and non-financial (non-cost) perform-ances. Financial performance measurements are important for strategic decisions and external reporting while non-financial measures are vital for effective control of day-to-day operations (Maskell, 1991, citing from Gunasekaran et al., 2001). However strategic performance of a company does not consider this multi-dimensional nature of performance. Consequently, it is not always understandable which measure a firm should adopt. Moreover, measures that will be applicable mostly to the firm will change over time complicating how business per-formance is measured (Neely, 1999).

Financial measures are the most popular measurement of business performance although it suffers from numbers of drawbacks (Tangen, 2003). When management philosophy is changed, the way the company measures its business performance must change accordingly (Gama and Cavenaghi, 2009). Moreover, having a proper PMs would be a motivator for employees as it drives employee behaviour (Tangen, 2003).



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Performance Measurement Systems

Performance Measurement System (PMS) is the set of metrics used to quantify the efficiency and effectiveness of an action (Neely et al., 1995). They have been subjected to many changes and developments over the period due to inadequacy of traditional financial methods due to new management practices. Quality, delivery flexibility, delivery reliability and dependability have become the competitive priorities with the passage of time. As a result, the management accounting system was inconsistent and the concept of Activity Based Costing (ABC) was developed. Later many others were introduced such as Theory of Constraint (Goldratt, 1990), Sink and Tuttle model (Sink and Tuttle, 1989), Performance Pyramid (Cross and Lynch, 1992), Balance Scorecard (Kaplan and Norton, 1992) and Performance Prism (Neely et al., 2001). Among them performance pyramid considers the hierar-chical break down of the organizational objectives into operational performance measures. Performance prism focuses on five linked performance perspectives namely stakeholder satisfaction, strategies, processes, capabilities and stakeholder contribution. The most noteworthy change in PMS occurs with Kaplan’s and Norton’s Balance scorecard which develops measures with respect to four stakeholder aspects. In addition, the development of busi-ness models and frameworks such as Malcom Baldrige National Quality Award and other quality models provided multiple insights to PMSs.

All of these PMSs have some kind of limitations of their own. As such the models should be adjusted with the requirements of the organization and develop a one that suits the organizational objectives. This situation was much more aggravated by various management practices came into play such as lean manufacturing or world class manufacturing which had different objectives. For example, lean focuses on waste elimination. Hence PMS should reflect the objectives of these practices as well. As a result, many other PMSs were developed to adjust to lean environment.

PMSs for Lean Manufacturing Organizations

Lean Accounting is a PMS driven by the principles of lean thinking: value, value stream, flow, pull and perfection. It mainly focuses on the value stream which is important for continuous improvement. Elements of lean management accounting include eliminating operational transactions, lean performance measurement, cell performance meas-urement and value stream performance measurement.

A set of metrics were developed to reach the balance between financial and non-financial measures in lean environment. These were categorized into three groups as strategic, value stream and cell/process measures based on the decision making levels involved (Maskell, 1991).

A model was developed summarizing the important principles in lean manufacturing to assess changes result-ing from lean. It highlights the determinants that reflect changes happening becoming lean. They are the elimina-tion of waste, continuous improvement, zero defects, JIT, pull, multi-functional teams, decentralized responsibilities, integrated functions and vertical information systems. The determinants were presented in measurable format (Karlsson and Åhlström, 1996).

Sanches and Perez (2001) developed a set of indicators to assess manufacturing changes towards lean manufactur-ing. It was found that quality was the most influential indi-cator and the development of indicators in relation to organizational competitiveness was an important. Thirty six indicators were derived under six broader categories that cover main features of lean manufacturing. They were elimination of zero value activities, continuous improve-ment, team work, JIT production and delivery, suppliers’ integration and flexible information system. These groups therefore determine the changing areas of an organization due to lean manufacturing as mentioned by Karlsson and Åhlström (1996).

Mahidhar (2005) developed a conceptual framework for PMS for a lean enterprise based on a simple structure consists of three levels of performance measures: individual metric, metric sets and metric clusters. The interconnections in-between levels represent causal links. The stakeholder value analysis was used for the selection of PMs at the top metric cluster level.

Krichbaum (2007) suggested an approach to develop lean metrics that support lean scorecard. This starts with estabilishing measurement categories to cover balance scorecard perspectives. They are safety, people, quality, responsiveness and financial performance. Then vision statements are derived for each category followed by estabilishing goals, defining metric and a plan for strategy.

Khadem et al. (2008) addressed the issue of inconsist-ency in performance measurement criterion and lack of structured approach deriving manufacturing PMs for lean environment. He proposed a set of metrics which are the base line measurement for lean implementation. They are two fold as primary metrics and secondary metrics. Primary metrics are called lean metrics which includes Dock-To-Dock time, First-Time-Through capability, Overall Equipment Efficeincy and Build-to-Schedule Ratio. The secondary metrics includes on-hand-inventory, value adding ratio, manufacturing cycle time, 5S diagnos-tic rating and square footage required. These metrics are




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used to evaluate the performance of lean manufacturing system in terms of production rate, quality, machine utili-zation, tardiness, lead time and inventory levels.

Gama and Cavenaghi (2009) presented vital character-istics for a PMS of a lean manufacturing organization and developed a PMS using A3 reports which was a tool for process improvement. The model presents organizational strategies and critical areas of performance that need more attention. Further it provides indicators that support follow-up of the performance in strategic, tactical and operating areas, while providing consensus between parties involved and facilitating communication on organizational goals. There should be a set of A3 reports contemplating a strategy, describing the vision and desired results in the long term.

Lean focuses on flow and pull from the customer, value streams, system quality and team empowerment. Therefore a PMS should supports improvements resulting from lean such as shorter cycle times, less inventory, higher quality, on-time delivery, visual management, and pull systems. Further it should be strategically focused and aligned, primarily non-financial, simple and easy-to-use visual and obvious, provides immediate and timely feedback, and fosters continuous improvement (Purdue University, 2011).

Lean scorecard was developed by performance man- gement solution providers to integrate lean performance with the balance scorecard approach. It utilizes both financial and non-financial peformance measures in the areas of leadership, transparency, lean product development, process focus, JIT, process control, standard work, and continuous improvement (www.ifsworld.com, n.d).

Sandanayake (2009) developed a model for a PMS that suits for JIT enabled manufacturing setting. It captures how JIT practices and extented balance scorecard perspec-tives relate to overall performance of the organization while quantifying the interactions. However, the study limits to JIT practices where as JIT was a part of lean. It proposes Key Performance Indicators (KPIs) based on current JIT practices that best fit to measure the overall performance with respect to different balance scorecard perspectives.

PMSs used by Lean Practicing Apparel Companies in Sri LankaSri Lankan apparel companies have changed the PMSs once lean is implemented. Following is a brief description of PMSs preacticed by them.

Performance objectives are set by the top management considering organizational objectives. Although stake-holder requirements are also considered, they are not fully captured. These performance objectives are cascaded to define performance measures at the operational level. As a

result, stakeholder requirements are not directly considered when developing PMs at the operational level. The purpose of operational level performance measures is to maintain a suitable environment for manufacturing performance. They do not reveal the importance of each measure for enhancing manufacturing performance. The present mech-anisms do not consider important manufacturing areas ena-bling through lean when developing performance measures. Also there is no mechanism to diagnose the importance of stakeholder requirements and the important manufacturing areas on manufacturing performance.

MethodologyInterviews were conducted to study the present PMSs and to reveal industry views. Judgmental sampling was adopted under non-probability sampling method for sampling for interview as statistical inferences were not going to be made. Qualitative nature of research questions further vali-dates the appropriateness of judgmental sampling. This is suitable since sample size is small (Saunders et al., 2003). Six interviews were conducted following convenience sampling strategy under judgmental sampling.

Findings of the interviews in conjunction with literature review and documented materials were used to formulate a PMS model. Interviews are applicable due to specific and culture bound nature of lean. These were held with mana-gerial level person who involves with lean implementation and performance measurement development in each organ-ization. Each interview may last one hour.

Face validity of interview data was checked since only content validity is applicable to interviewing technique. This was obtained by peer debriefing the questionnaire prepared for the interview with the academic expertise. Companies come under a particular group follow the same PMS. Conducting interviews with several people come under same determines the accuracy of the answers to the questions. Therefore data triangulation of this nature provided the evidence of validity of interviews.

Measuring reliability of the interviews becomes diffi-cult. It is highlighted in the literature that validity does not exist without reliability and demonstrating validity is therefore sufficient for qualitative research. Confirming the validity as mentioned in the above section therefore proves the reliability as well.

Problem ConceptualizationConceptual design of the study was directly drawn from Mahidhar (2005). This was selected as the ground since it represented most of the characteristics of a good PMS. It consists of three levels as metric cluster, metric set, and individual metrics. The links between levels show the




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interconnections. Individual metrics capture task-level, activity-level, or employee-level performance contributing to overall performance of processes. Metric set captures the performance across a group of activities or for the overall process, evaluated by measuring end-to-end performance or by aggregation of individual metrics. Each set directs an individual’s activities in support of strategic objectives. Metric cluster aggregates the individual metric and metric set in order to link with strategic objectives and stakeholder values (Mahidhar, 2005).

Conceptual Design

Variables for the constructs of conceptual design discussed above were drawn from literature. Importance of stakeholders and strategic alignment are highlighted in the performance prism model, Measurement System Development Process (MSDP) and Atkinson et al. (1997). In lean manufacturing, KPA is a changing area or performance improvement area due to lean application. Therefore these KPAs should be aligned with the stakeholder requirements. Rentes, Carpinetti and Van Aken (2002) introduced Key Performance Areas (KPA) where KPA is a particular business process, a functional area or a infrastrucutre aspect, etc. These were synthesized with the hierarchy proposed by Mahidhar (2005) to develop the conceptual model. This is shown in Figure 1. The top level represents the overall manufacturing performance of the organization. The next level (metric cluster) represents the stakeholder values. The next level (metric set) is the KPAs which capture the performance of a group of activities. Under each KPA individual metrics should be defined to measure the activity level or employee level performance.

Model DevelopmentThe model was developed by determining elements for the variables of the conceptual framework discussed in

previous section. The proposed model is shown in Figure 2 and a description of deriving elements/metrics for each variable in the model are given below.

Defining Metrics for Stakeholder Value Level

Stakeholder value analysis method adopted by Mahidhar (2005) was followed here with minor modifications. This includes four steps of identifying stakeholders, identifying values or performance attributes that are of importance to each stakeholder, group related values or performance attributes identified by each stakeholder into metric clus-ters, and final cluster formation. Clusters were formed converting the identified stakeholder requirements into manufacturing terminology. Stakeholders were identified as of Atkinson et al. (1997), Mahidhar (2005) and balance scorecard approaches. Manufactuting cost, manufacturing capability, manufacturing best practices, employee satis-faction and external resource development were the final elements defined at this level.

Defining Metrics for Key Performance Areas (KPAs)

These were discovered from the findings of the literature review and interviews. Indicators developed by Sanches and Perez (2001) to identify changing areas of an organization from lean manufacturing were selected for this study. The main categories to which the indicators belong were considered as the metrics for the KPA level. These categories are the elimination of zero value activities, continuous improvement, team work, JIT production and delivery, supplier integration and flexible information system.

According to interviews, lean manufacturing apparel companies in Sri Lanka develop manufacturing Key Performance Indicators (KPIs) based on lean metrics that cover Safety, Quality, Delivery, Manufacturability and Cost (SQDMC). Krichbaum (2007) identified five catego-ries to estabilish measures for manufacturing performance covering the four perspectives defined by Kaplan and Norton in balance scorecard. These measurement areas are safety, people, deliver to the customer, quality, responsive-ness and financial performance. These areas can be mapped to SQDMC model. Indicators developed by Sanches and Perez (2001) can be mapped into SQDMC model too. Even though quality, delivery and cost aspects are covered from the model safety and morale are not tapped. Therefore safety and morale were introduced to the metrics devel-oped at this level. The final metrics include KPAs of elimi-nation of zero value activites, continuous improvement, team work, JIT production and delivery, suppliers’ integra-tion, flexible information system, safety and morale.

Figure 1. Conceptual Design of the Performance Measurement System

Source: Developed by the authors.




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Figure 2. Proposed PMS Model for Lean Manufacturing Apparel Company in Sri Lanka


Model JustificationThe proposed model possesses attributes suggested by previous scholars for a good PMS. This section describes how proposed PMS is in line with such recommendations.

According to European Foundation for Quality Mana- gement, there is a cause and effect relationship among enabler and results. The main elements of the organization are an ‘enabler’ criterion which includes leadership, policy and strategy, people, partnership and resources and proc-esses. KPAs of the proposed model covers these areas except leadership, policy and strategy. Since the model is limited to operational level performance, elimination of these aspects would not harm the model. Improvements in ‘enable’ criteria would be reflected in organizational results in customer, people, society and key performance results (Najmi et al., 2005). These are reflected by stakeholder value level parameters of the model. Therefore the pro-posed model complies with recommendations of Najmi et al. (2005) for a PMS.

The proposed model can be further explained by the generic PMS design approach of Najmi et al. (2005) which

consists of three basic elements of direction, processes and measures. The adoptability of the model to the above is shown in Figure 3.

In proposed model, the way of measuring manufacturing performance is defined by the organizational objectives. Therefore it sets direction to the manufacturing function. Manufacturing performance is guideded by the processes required to satisfy stakeholders which are reflected by stakeholder value level metrics. Lean KPAs indicate the measures derived from strategies that are attached to the processes.

Figure 3. Applicability of Generic PMS Design Approach for the Proposed Model





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Figure 4. Adopting the Proposed PMS Model for Muchiri et al. (2011) PMS


Moreover the proposed model can be explained by performance measurement framework of Muchiri et al. (2011). The model can be fitted to their model as shown in Figure 4.

The first phase seeks aligning manufacturing performance with corporate and manufacturing strategy. Therefore overall manufacturing performance is determined by those. Further performance requirements of the manufacturing system can be defined by reviewing composite requirements of various stakeholders. Important manufacturing processes can therefore be determined from that. These processes equate to critical success factors which drive manufacturing performance. Indi- cators developed for these processes are referred to as leading indicators as they drive performance. Manufac- turing performances result in lean environment are monitored in terms of lean KPAs. Indicators developed for KPAs therefore measure the outcome. Consequently these indcators are referred to as lagging performance indicators.

Further the model captures balance scorecard aspects. It covers the extented balance scorecard perspectives considered by Sandanayake (2009). Table 1 shows how perspectives of extented balance scorecard are covered by the model.

ConclusionEven though the PMSs are subjected to change with changing organizational practices, there is an absence of PMS developed for lean manufacturing environment in Sri Lankan context. Lean implementation is different from organization to organization. As a result, each has to develop a one that suits for their setting. Therefore development of a PMS model is more suitable for such an environment.

Table 1. Coverage of Balance Scorecard Perspectives by the Proposed Model

Extented Balance Scorecard Perspective Stakeholder Value

Financial Manufactuting costCustomer Manufacturing capabilityInternal business processes Manufacturing best practices

Manufacturing capabilitySupplier External resource development

Manufacturing capabilityEmployee Employee satisfactionInnovation and growth Manufacturing capabilityExternal social environmental

groupsExternal resource development





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This study proposes a PMS model for lean manufacturing apparel companies in Sri Lanka by considering the impact of lean philosophy on manufacturing performance in developing the PMS. The top most level represents the manufacturing performance which denotes the top management expectation of the manufacturing function. Therefore it reflects strategic level expectations and hence the strategic objectives. The second level represents the stakeholder values. This captures the expectations of each stakeholder from the manufacturing area of the company. Then stakeholder expectations are translated into manufacturing requirements that affect the manufacturing performance. The bottom level identifies maufacturing areas affected by lean. It was assumed that categories belong to stakeholder values and manufacturing areas were not dependent. Model therefore investigates how KPAs of lean and manufacturing requirements defined by stakeholders drive the overall manufacturing performance.

This is a robust, comprehensive PMS model capable of assessing the impact of multidimensional changes due to lean practices in manufacturing performance. Such a study has not been done in lean manufacturing environ-ment for the apparel sector. It focuses on manufacturing areas changed by lean practices. Therefore the model indi-cates the important changing areas for manufacturing performance based on stakeholder requirements. It is therefore, not limited to the existing lean practices and reveals avenues suggesting key areas to be addressed by lean practices. Overall performance can be improved by adopting lean on vital areas. Further this reveals the types of KPIs to be set by the organization so as to monitor performance. Organizations can compete effectively by identifying essential areas in which KPIs are set. From the above discussions it is apparent that the manufacturing requirements identified from stakeholder values impact on overall manufacturing performance as well as KPAs impact on manufacturing requirements defined by stakeholder values. Therefore diagnosing this relationship and the level of impact are important. As a result, this study can be extended to quantify the importance of each child level on parent level of the model.

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P.S.T. Perera ([email protected]) completed Masters in Manufacturing Management at University of Colombo in 2011 and obtained the Bachelor of Science degree specialized in Industrial Management from University of Kelaniya, Sri Lanka in 2005. Presently she is a lecturer in the Department of Management of Technology, University of Moratuwa, Sri Lanka.

H.S.C. Perera ([email protected]) is a Professor and the Head of the Department of Management of Technology, University of Moratuwa, Sri Lanka. He obtained the Bachelor of Science in Engineering from University of Moratuwa, Sri Lanka, Master of Engineering and Doctor of Engineering from Asian Institute of Technology, Thailand. He has authored several book chapters and books, and contributed to numerous articles in international journals. He is a Chartered Engineer and a member of the Institute of Engineers in Sri Lanka.



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Developing a Performance Measurement System for Apparel Sector Lean Manufacturing Organizations in Sri Lanka