Read Microsoft Word - icls54_p text version

The 5th International Congress on Logistics and SCM Systems(ICLS2009)

A Study of Business Performance through Key Performance Indicators (KPIs) in Thai Garment Industry

Suthathip Suanmali*1A, Ekkprawatt Phong-arjarn *A, Chawalit Jeenanunta*A, Veeris Ammarapala*A, Kornthip Watcharapanyawong*B *A Sirindhorn International Institute of Technology, Thammasat University, Thailand *B Department of Textile Science, Kasetsart University, Thailand 1 Corresponding author, Tel: (662) 501-3507 Ext 2114, Fax: (662) 501-3507 Ext 2101, Email: [email protected] Abstract

The textile industry plays a very important role in the economy of Thailand as it is the second largest industry that contributes to the GDP. The garment industry is considered as a downstream segment in the textile industry, and it remains the largest exporter and employs the largest number of workforces. The garment industry has been losing its competitive advantages and facing high competition in global market. Manufacturers then seek to form strategic alliance among suppliers, distributors, warehousing companies, and customers in order to gain back their competitive advantages. After the supply chain alliance has been formed, enterprise resource planning (ERP), a business support system, has been established to aid garment manufacturers in handling manufacturing, logistics, distribution, inventory, shipping, invoicing, accounting, etc. In this paper, we discuss about establishing key performance indicators (KPIs) to measure business performance. The KPIs are developed according to the Supply Chain Operations Reference (SCOR) Model and divided into two levels. KPIs level one are aimed to provide a comprehensive view of business organizations whereas KPIs level two are specified operational performance such as plan, source, make, and delivery. These KPIs were tested by applying in the case study garment companies, and we study the results from measuring the supply chain performance of these companies. Keywords: Performance Measurement, Key Performance Indicators, Textile Industry. advantages. Supply chain management (SCM) is the operational strategy for achieving organizational competitiveness. Companies are looking for ways to improve their flexibility, responsiveness and competitiveness by changing their operational strategy, methods and technologies that include the implementation of SCM and information technology [1]. However, they often lack the insight on how to develop the effective performance measures and metrics in order to attain a fully integrated supply chain. Moreover, such measures and metrics are needed to be tested to reveal the feasibility of strategies [2]. Lee and Billington [3] argue that discrete measurement of the performance of supply chains is an important issue because it allows for "tracking and tracing" of efficiency and failures and eventually leads to more informed decision making. In order to identify the success of supply chain management, an adequate performance measurement system (PMS) needs to be developed.

2. Status of Garment Industry in Thailand

Textile and Apparel (T/A) industry has emerged in Thailand for more than 30 years. The export value of textile and clothing industry is approximately 6,000 million US$ per year, and GDP in textile industry is approximately 4% of the total GDP of Thailand. There are more than 4,000 establishments in Thai textile industry that employ over a million workers [4]. According to the information from Thailand Textile Institute (THTI), the export value of this sector in the year 2008 (Jan ­ Nov) is about $US 6.6 billion, which has been increased about 4.18 percent from the year 2007 as shown in Table 1. Thai T/A industry consist of 3 Stages; Up Stream stage - including fiber industry and spinning industry, Middle Stream stage - including weaving industry, knitting industry, dyeing, printing and finishing industry, and Down Stream stage ­ including garment industry and other fabrication industry. Most T/A companies are the Small-Medium Enterprise (SME) with high labor-intensive. Since garment industry is the labor-intensive industry, it is currently facing the high competition from other developing countries with lower labor cost,

1. Introduction

The textile industry provides not only the high export value, but also the employment opportunity of more than one million workers. The garment industry is considered as a downstream segment in the Textile industry, and it remains the largest exporter and employs the largest number of workforces. The garment industry has been losing its competitive advantages and facing high competition in global market. Strategic alliance among suppliers, distributors, warehousing companies, and customers are formed in order to gain back their competitive

The 5th International Congress on Logistics and SCM Systems(ICLS2009)

such as China, Bangladesh, and Vietnam. In order to increase competitiveness, Thai garment industry has to be improved both in the product development and business management. Thailand Textile Institute

(THTI) proposed that the major issues of improvement of Thai T/A industry (and also garment industry) is to increase the efficiency of supply chain management [5].

Table 1 Thai Textile & Clothing Export Classified by Major Market, Jan. ­ Nov. 2008 Value (Million US$) Growth: % 2005 2006 2007 2007 2008 2005 2006 2007 2008 Major Market 1. USA 2,111.1 2,083.5 1,970.5 1,813.5 1,784.3 1.48 -1.31 -5.42 -1.61 2. EU 1,210.4 1,316.8 1,324.1 1,198.2 1,248.7 1.35 8.80 0.55 4.21 3. 770.1 803.2 924.8 845.6 947.8 17.23 4.29 15.14 12.09 ASEAN 4. Japan 412.1 395.4 378.5 343.4 429.8 -4.09 -4.04 -4.28 25.16 5. China 282.5 249.7 264.4 242.2 232.8 6.16 -11.61 5.88 -3.89 6. Others 1,907.3 1,986.0 2,104.9 1,933.3 1,999.2 7.82 4.13 5.99 3.41 7. World 6,693.5 6,834.6 6,967.2 6,376.2 6,642.5 4.66 2.11 1.94 4.18

3. Performance Measurement in Supply Chain Management

Performance Measurement (PM) can be defined as the process of quantifying the efficiency and effectiveness of an action [6]. A performance measure is a set of metrics used to quantify the efficiency and/or effectiveness of an action [7]. A Performance Measurement System (PMS) plays an important role in managing an organization because it provides the necessary information for a decision-making process. Gunasekaran [8] supports that performance measurement and metrics play a crucial part in setting objectives, evaluating performance, and determining future courses of actions. The main reason for poor performance of supply chains is the lack of a measurement system [9]. The real challenge for managements is to develop suitable performance measures and metrics to make appropriate decisions that eventually lead to an improved organizational competitiveness. Bagchi [10] determines the metrics of a supply chain system to be used in comparing the competitiveness of selected companies and placed each of the 28 metrics in one of the following four categories: Time, Quality, Cost, and Diagnostic Measure. Stewart [11] proposed that the metrics and measures are discussed in the context of the following supply chain activities/processes: · plan, · source, · make/assemble · delivery/customer The detail of the metrics in each process has been described in [8]. In addition, Gunasekaran [6] presents the key performance metrics in Logistic and SCM that are classified by phases in supply chain ­ Plan, Source, Make, and Delivery.

The Supply-Chain Operations Reference (SCOR) model was developed by the Supply-Chain Council (SCC) to assist firms in increasing the effectiveness of their supply chains, and to provide a process-based approach to SCM. It provides a unique framework that links business process, metrics, best practices and technology features into a unified structure to support communication among supply chain partners and to improve the effectiveness of supply chain management and related supply chain improvement activities [12]. The SCOR Model is based on five distinct management processes; Plan, Source, Make, Delivery, and Return [13]. It contains three levels of process detail; · top level (process type) · configuration level (process category) · process element level (decompose processes)

4.2 Balance Scorecards of Supply Chain

While SCOR provides a proven model to measure and benchmark supply chain performance, it does not include measures for the other business processes in the value chain such as product design, sales, etc [14]. Such key performance indicators of a business process can be found in Balanced Score Cards (BSC). The Balance Score Cards (BSC) is an approach to strategic management developed in the early 1990's by Robert Kaplan and David Norton. It contains a performance evaluation system that offers a comparative advantage over others, theoretically and in practice. The basic idea is that an organization must measure its performance from a balanced view against its goals as established in its vision and strategy. The Balanced Scorecard has four measurement categories: · Customer Perspective · Internal Process Perspective · Financial Perspective · Learning and Growth Perspective. Brewer [15] explores the application of BSC in

4. SCOR model and Balance Score Cards 4.1 SCOR Model

The 5th International Congress on Logistics and SCM Systems(ICLS2009)

supply chain performance measurement and presents a new tool for performance measurement of supply chain, "The BSC of Supply Chain." It links between SCM and BSC and provides the connection between Performance Indices and Enterprise Strategies. Because BSC focus only the activities in the enterprise, Zheng proposes the Dynamic 5D-BSC Model as shown on Fig. 1 by adding suppliers and customer parts [16].

management is responsible for the total financial results; and (2) a situation in which a management team is responsible for the overall performance of the whole supply chain. To choose KPIs for this study, we use the following concepts and factors as shown on Fig. 2: · SCOR Model and BSC · Satisfaction of users (management level and operational level), customers and suppliers

Fig. 1 The Dynamic 5D-BSC Model.

Fig. 2 Conceptual framework of KPIs 5.1 KPIs Level I The KPIs level I are aimed to provide a comprehensive view of business organizations. They consist of SCOR level 1 metrics, which are reliability, responsiveness, flexibility, cost, and asset management, added with satisfaction of stakeholder. Reliability - this metric category contains value chain metrics focused on quality of product and service. In the case of service, it could pertain to delivery, post sales service, or warranty. Responsiveness - this metric category focuses on speed or velocity in responding to demand events such as a customer order, new product introduction, service order, etc. Flexibility - this category of metrics attempt to measure the adaptiveness of a value chain to meet demand variation both in the near and long terms. Cost - this metric category attempts to measure the process performance of both direct and indirect aspects of the value chain including customer chain, supply chain, design chain, and aggregate measures. Asset management - this category of metrics focuses on measuring the efficient use of assets including both fixed and working capital. Satisfaction - this category of metrics focuses on satisfaction of the supply chain partners.

5. Development of KPIs

Globerson [17] suggests that the performance criteria should be based on company objectives. It should be comparable to other performance criteria used by similar organizations. The adequate performance criteria ought to be clearly define its purpose, data collection and calculation methods, Maskell [18] proposes seven principles of PMS design; · the performance measurement should be directly related to firm's strategy · nonfinancial measures should be adopted · measures should vary between locations (departments or companies) · measures should change as circumstances do · measures should be simple and easy to use · measures should provide fast feedback and · measures should stimulate continuous improvement. Lohman describes that many companies seem to be facing serious difficulties in implementing such supply chain-wide PMS that capture various dimensions of performance at various levels in a consistent way [19]. In order to support managing operations across supply chains, it may be useful to think first of two extremes: (1) several functional or regional departments are each responsible for one aspect or one part of the supply chain and only top

The 5th International Congress on Logistics and SCM Systems(ICLS2009)

No. 1 2 3 4

Attributes Reliability Reliability Responsiveness Flexibility

5 6

Cost Assets

Table 2 The List of KPIs Level I KPIs Description Perfect Order The percentage of products delivered on-time to customers Fulfillment with complete documentations and no defects. The percentage of the orders from old customers compare Repeated Order to all orders received. Order Fulfillment The total elapsed time from customer authorization of a Cycle Time sales order until the product received. The amount of time that a supply chain takes to respond to Upside Supply Chain an unplanned 20% increase in demand without service or Flexibility cost penalty. The percentage of both direct and indirect costs incurred to Cost of Goods Sold produce finished goods in a company compare to total sales. Return on Supply The ratio of annual sales to total net assets. Chain Fixed Assets


Additional KPI The satisfaction of management level , operational level, suppliers, and customers of a firm, such as, marketing, purchasing, production planning, production, inventory, and distribution. 5.2 KPIs Level II KPIs level II are associated with supply These KPIs are adapted from BSC concept and chain processes (Plan, Source, Make, Deliver, associated with business process of garment Return). Each process may be in different department manufacturing. 7 Satisfaction

No. 1 2 3 4 5 6 7 8 9 10 11 12 13 14

Process Plan Plan Plan Source Source Make Make Make Make Deliver Delivery Delivery Delivery Return

Table 3 The List of KPIs Level II KPIs Description Accuracy of production queuing The accuracy of production queuing. The average total time spent on actual Accuracy of production planning production process comparing with total time forecasted in planning. Accuracy of material planning The accuracy of material planning. The average time spent for sourcing raw Sourcing time material. The value of product sold comparing with Inventory turnover inventory cost. The percentage of approved the product Approved sample product samples. Sample cycle time The time spent on making sample product. Production cycle time The total time spent on production process. Rework production The percentage of product rework. Packaging cycle time The average time spent in packaging process. Delivery cycle time The average time spent in delivery process. Cost of Delivery The average delivery cost per year. The value of inventory that stocked more than Amount of dead stock two years. The percentage of defective products that are Return of Defective Product returned from customers. sportswear products. The company B is larger and has more budgets for IT development than the company A. While the company B achieved in implementation of IT system and quality management system, the company A is just in the initial stage of their business improvement and IT implementation.

6. Case Studies in Garment Companies The developed KPIs were tested by applying in the case study of Thai garment companies; A and B. The detail of these garment companies are described in Table 4. These garment companies were founded for more than 10 years, and they both manufacture

The 5th International Congress on Logistics and SCM Systems(ICLS2009)

Table 4 The Detail of the Case Study Companies Thai garment companies Attributes A B SME OEM OEM Type of manufacturing Less than 50 million bath More than 200 million bath Fixed asset (not included land) 200-500 More than 1000 Number of employees More than 10 years More than 10 years Length of operation Domestic and international International only Market N/A ISO 9001:2000 Quality management system Most KPIs data was collected from the departments of both companies; some was collected by interviewing managers or related officers. However, some data could not be collected because they were not recorded by the companies or there is no such criteria in garment business process, such as upside supply chain flexibility. A customer has to make a new purchase order if there is any change in quantities or style of a product. The data collection are shown in Tables 5 and 6.

Table 5 Result from data collection of KPIs Level I KPIs Level I Company A Perfect Order Fulfillment 90% Repeated Order 95% Order Fulfillment Cycle Time 90-120 days Upside Supply Chain Flexibility N/A Cost of Goods Sold 22.6 % Return on Supply Chain Fixed Assets 0.17 Satisfaction N/A

Company B 99.82% 98% 75-90 days N/A 94% 1.66 N/A

Table 6 Result from data collection of KPIs Level II KPIs Level I Company A Company B Accuracy of production queuing 50.0% 94.7% Accuracy of production planning 89.5% 88.0% Accuracy of material planning 98.5% 96.0% Sourcing time 37-70 days 21-47 days Inventory turnover 92.5% N/A Approved sample product 80% 100% Sample cycle time 50-90 days 15 days Production cycle time N/A N/A Rework production 20.2% N/A Packaging cycle time 1-6 days N/A Delivery cycle time N/A N/A Cost of Delivery 2,242,177 Bath/Month 554,670 Bath/Month Amount of dead stock N/A 5 Million Bath Return of Defective Products 3.75% 0.01 % For KPIs Level I, the KPIs data of the companies A and B are not much different except the order fulfillment cycle time and the cost of goods sold as company B's are much better than company A's. For KPIs Level II, the sourcing time and the sample cycle time of company B is much shorter than company A. The return of defective products of company B also less than company A. The accuracy of production queuing of company A is higher that company B for 44.7 %. The cost of delivery of company A is half of company B. Other KPIs of both companies are not much different. The results have shown that the overall performance of company B is higher than company A. 7. Conclusion and Future Plan In this paper, we present the development of a set of KPIs for measuring supply chain performance of garment companies. The KPIs are developed according to the Supply Chain Operations Reference (SCOR) Model and Balance Score Cards (BSC). There are two levels of KPIs, level I are aimed to provide a comprehensive view of the companies, and level II are specified operational performance of supply chain processes such as plan, source, make, and delivery. These KPIs were tested by applying in the case study garment companies and study the results from measuring the supply chain performance of these companies. There are several problems found in the data collection process such as · The case study companies were not familiar with the new developed KPIs, and there were some

The 5th International Congress on Logistics and SCM Systems(ICLS2009)

misunderstanding of a definition of a KPI. Hence, some collected data were not applicable to determine the value of the KPI. · Because of a large number of product types in each purchase order, it is often difficult to measure some of the KPIs data; for instance, sourcing time, production cycle time. · In many cases, some data are not available because a company does not keep the record, or they are recorded in different formats. Therefore, comparing the KPIs data between the two companies is difficult. From the interviews with the managers of these companies, the set of KPIs is accepted that it can indicate the supply chain performance of the companies. However, the garment industry has the unique characteristic, so the KPIs that can be used in another industry may not be suitable with this industry. For the further research, these KPIs shall be adjusted accordingly to the business process to make them more suitable for garment companies. More empirical study is needed to be carried out. Furthermore, we plan to develop a handbook on how to improve each KPI for garment companies. References [1] A. Gunasekaran, and E.W.T. Ngai, "Information systems in supply chain integration and management", European Journal of Operational Research, Vol. 159, pp.269­295, 2004. [2] A. Gunasekaran, C. Patel, and E. Tirtiroglu, "Performance measures and metrics in a supply chain environment", International Journal of Operations & Production Management, Vol. 21 No. 1/2, pp. 71-87, 2001. [3] H.L. Lee and C. Billington (1992), "Managing supply chain inventory: pitfalls and opportunities'', Sloan Management Review, Spring, pp. 65-73, 1992. [4] Thailand Textile Institute (THTI), "Thai Textile statistics 2006/2007", THTI, 2007. [5] Sirisoponsilp, S., Kritchanchai, D., Wasusri, T. and Watcharapanyawong, K. , "Basic Concept of Supply Chain Management in Textile Industry", 1st Ed., Thailand Textile Institute (THTI), 2007. [6] A. Gunasekaran and B. Kobu, "Performance measures and metrics in logistics and supply chain management: a review of recent literature (1995-2004) for research and applications," International Journal of Production Research, vol. 45, pp. 2819 - 2840, 2007. [7] A. Neely, M. Gregory, and K. Platts, "Performance measurement system design: A literature review and research agenda," International Journal of Operations & Production Management, vol. 15, pp. 80-116, 1995. [8] A. Gunasekaran, C. Patel, and R. E. McGaughey, "A framework for supply chain performance measurement," International Journal of Production Economics, vol. 87, pp. 333-347, 2004. [9] E. Morphy, ``Measuring up'', Export Today, Vol. 15 No. 6, pp. 52-7, 1999.

[10] P. K. Bagchi, "Role of benchmarking as a competitive strategy: the logistics experience," International Journal of Physical Distribution & Logistics Management, vol. 26, pp. 4-22, 1996. [11] G. Stewart, "Supply chain performance benchmarking study reveals keys to supply chain excellence," Logistics Information Management, vol. 8, pp. 38-44, 1995. [12] G. Stewart, "Supply-chain operations reference model (SCOR): the first cross-industry framework for integrated supply-chain management", Logistics Information Management, Vol. 10 No. 2, pp. 62-7, 1997. [13] The Supply-Chain Council (2008), SCOR 9.0 Booklet, The Supply-Chain Council, [Online] Available: [Access: Nov. 2008] [14] P. Bolstorff, "Balancing Your Value Chain Metrics , Using The Balanced Scorecard to manage value chain performance", SCM Limited, 2006, Available: [Accessed: Feb., 2009]. [15] P. C. Brewer and T. W. Speh, "Using the Balanced Scorecard to Measure Supply Chain Performance," Journal of Business Logistics, vol. 21 No.1, pp. 75-93, 2000. [16] P. Zheng, K. K. Lai, and Y. Zhang, "Research on Supply Chain Dynamic Balanced Scorecard Based on Fuzzy Evaluation," 2006. [17] Globerson, S., "Issues in developing a performance criteria system for an organization," Int. J. Prod. Res., Vol.23, pp.639­646, 1985. [18] Maskell, B., "Performance measures of world class manufacturing", Management Accounting, Vol.67, pp.32­33, 1989. [19] C. Lohman, L. Fortuin, and M. Wouters, "Designing a performance measurement system: A case study," European Journal of Operational Research, vol. 156, pp. 267-286, 2004.

Suthathip Suanmali is a lecturer of School of

Management Technology, Sirindhorn International Institute of Technology, Thammasat University. She received her Ph.D. in Mathematics from North Carolina State University in 2007. Her research interests lies in the area of applied linear algebra, input-output model and supply chain management.


Microsoft Word - icls54_p

6 pages

Report File (DMCA)

Our content is added by our users. We aim to remove reported files within 1 working day. Please use this link to notify us:

Report this file as copyright or inappropriate


You might also be interested in

An agile supply chain for a project-oriented steel product network
Plaquette Lub Gal 2004 GB v33.indd
Report cover title