Benchmarking SCM performance and empirical analysis: a case from paint industry

Mishra, Pratima; Sharma, Rajiv Kumar Article Benchmarking SCM performance and empirical analysis: a case from paint industry Logistics Research Provided in Cooperation with: Bundesvereinigung Logistik (BVL) e.V., Bremen Suggested Citation: Mishra, Pratima; Sharma, Rajiv Kumar (2014) : Benchmarking SCM performance and empirical analysis: a case from paint industry, Logistics Research, ISSN 1865-0368, Springer, Heidelberg, Vol. 7, Iss. 1, pp. 1-16, https://doi.org/10.1007/s12159-014-0113-0 This Version is available at: https://hdl.handle.net/10419/157711 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by/2.0/ ORIGINAL PAPER Benchmarking SCM performance and empirical analysis: a case from paint industry Pratima Mishra •Rajiv Kumar Sharma Received: 23 May 2013 / Accepted: 6 March 2014 / Published online: 26 March 2014 ÓThe Author(s) 2014. This article is published with open access at Springerlink.com Abstract The real challenge for managers is to develop and implement a suitable supply chain performance framework that not only helps in making right decisions but also facilitates the benchmarking of their internal supply chain. The main purpose of this study was to develop a framework based on the performance metrics such as (1) total length of the supply chain, (2) supply chain inefficiency ratio and (3) supply chain working capital productivity. Case study approach is used to benchmark the SCM performance of two paint companies. Further, in order to examine the relationship between SCM practices and SCM performance measures, an empirical analysis has been done by formulating research hypothesis. Results show strong support for linkage between SCM practices and selected performance metrics. Keywords Supply chain SCM performance  Benchmark SCM practices 1 Introduction In the recent years, a number of firms have realized the potential of SCM in day-to-day operation management but evaluating SCM performance is a complex task, because it involves several dimensions be it strategic, tactical or operational. According to the Shah [39], supply chain management encompasses all activities involved in the transformation of goods from the raw material stage to the final stage when the goods and services reach to the end customer. In order to attain competitive advantage over the rivals, various business houses are paying more attention towards the end consumer, i.e. customer. A key feature of present day business is the idea that it is the supply chain that competes, not companies, and the success or failure of supply chain is ultimately determined in the marketplace by the end consumer [14]. Brandenburg and Seuring [9] applied benchmarking methodology for quantifying value contributions in terms of cost of goods sold and working capital from ten leading fast-moving consumer goods companies. They further emphasized on cross-industry benchmarking with different or extended content which may include key supply chain partners, i.e. suppliers, service providers, retailers and distributors. Previous researches [4,19,23,26, 46] into supply chain benchmarking show that it may lead to increased productivity of the supply chain, as managers compare their practices to the best in the field. Many researchers have stressed the importance of using the right metrics to benchmark and manage supply chain efficiently and effectively [1,12,21,28,34]. Gunasekaran et al [21,22], Hudson et al. [24], Folan and Browne [18] identified key SCM metrics and proposed a framework to classify them as financial and non-financial metrics. In the literature, various models such as (1) analytic hierarchy process (AHP), (2) balanced scorecard (BSC) and (3) supply chain operations and reference model (SCOR) were developed for supply chain performance evaluation. According to Bhagwat and Sharma [7], AHP can be the best tool for prioritizing and choosing the best measure and metric for day-to-day business operations. However, it is argued that AHP is not stable in its theoretical foundation P. Mishra (&)R. K. Sharma Department of Management and Social Sciences, National Institute of Technology, Hamirpur, HP, India e-mail: [email protected] R. K. Sharma Mechanical Engineering Department, National Institute of Technology, Hamirpur, HP, India e-mail: [email protected] 123 Logist. Res. (2014) 7:113 DOI 10.1007/s12159-014-0113-0 and could cause revision in decision-maker’s preference because pairwise comparison metric fails to perfectly satisfy the consistency required by the AHP approach [11]. BSC was first introduced by Kaplan and Norton in year [25]. It consists of four perspectives: (1) financial, (2) customers, (3) internal business process, (4) innovation and learning. Bhagwat and Sharma [6] suggested putting different SCM metrics into different BSC perspectives to give a balanced picture of SCM performance evaluation. Further, they (Bhagwat and Sharma [7]) expanded the BSC framework to include AHP in determining which measures to include and at which level (strategic, tactical or operational) of the organization. Criticism of the BSC includes the exclusion of people, competitive environments, environmental and social aspects of industry [3,36]. Epstein and Weisner [17] argued that there is no rule for the right number of measures to include in a balanced scorecard, although including too many can distract from pursuing a focused strategy. In a review conducted by [20], they concluded that BSC is more like a strategic tool rather than a true complete performance measurement system. SCOR was developed by the Supply Chain Council. It defines supply chain as the integrated process of plan, source, make, deliver and return. It contains five level of analysis, i.e. (1) process type, (2) process categories, (3) process elements, (4) implementation and (5) performance metrics. However, measurement models for supply chain performance evaluation have their limitations. Firstly, there are too many individual measures being used in the supply chain context. For example, Shepherd and Gunter [40] have summarized single supply chain performance indicators related to cost (39), time (22), quality (or reliability) (35), flexibility (28) and innovativeness (8), respectively. Though these measures offer valuable information for decision making, selecting and trading off so many measures to obtain effective and crucial improvement strategies is a difficult task for different supply chain participants. Secondly, these models do not provide definite cause–effect relationships among numerous (and hierarchical) individual key performance indicators [10]. According to Persoon and Araldi [35], SCOR is basically used for the static operations of supply chain rather than the dynamic effects like changes in production rate, poor quality in raw materials and other effects related to the bullwhip behaviour of a supply chain. With all these problems highlighted with reference to AHP, BSC and SCOR models, there seems to be no universal consensus regarding suitable measures of SCM performance. Cai et al. [10] in their work found that many measurement systems lacked strategic alignment, a balanced approach and universal thinking; they have difficulty in systematically identifying the most appropriate metrics. According to Martin and Patterson [30], firms that were engaged in SCM found inventory and cycle time to be the most significant metrics, but the dimensions related to inefficiency and working capital productivity are not addressed adequately. There are many metrics considered by different authors like cost, quality, flexibility, innovation and responsiveness [5,12,21,22,40], but they lack consideration of inefficiency ratio, total length of supply chain and working capital productivity. Work by Wouters and Wilderom [47] and Wouters [48] clearly supported the need for a performance measurement system which should clearly define the purpose, data collection and calculation methods, and simple, easy to use, preferably in the form of ratios rather than absolute numbers. Studies in the past have shown that a well-planned and executed SCM will not only enable organizations to reduce their inventories, but also provide better customer services [13]. On one hand, SCM’s short-term objective is targeted to enhance productivity and reduce inventory and lead time, and on the other hand, the long-term objective is targeted to increase company’s market share and have external integration of the supply chain processes, which needs to be further investigated [27,29]. Also, Akyuz and Erkan [1] in their paper on ‘‘supply chain performance measurement: a literature review’’ concluded about immaturity of SCM frameworks and models in their survey and believed that future contribution to the area will come specifically from framework development efforts and validation of developed performance measures. To abridge this gap, authors in the present study developed a benchmarking framework based upon performance metrics given by Shah [39]. In the first part of work, SCM performance of two paint companies is benchmarked and DMAIC approach is explained which takes care of all entities, i.e. supplier, distributor and retailer in a supply chain network. In the second part, empirical testing of the selected performance metrics is done by formulating various research hypothesis. The organization of this paper is as follows. After introduction in Sect. 1, the Sect. 2briefly describes the theoretical background with research framework and formulas of metrics considered in the work. The Sect. 3presents the research objectives and methodology. The Sect. 4 discusses the analytical approach adopted in the study. Empirical analysis based on various research hypotheses formulated in the study is presented in the Sect. 5. The discussions and theoretical implications are presented in Sect. 6. Finally, concluding remarks and directions for future research are given in Sect. 7. 2 Theoretical background The section presents summary of some significant findings with reference to supply chain performance measurement 113 Page 2 of 16 Logist. Res. (2014) 7:113 123 which takes into account (1) supply chain length (2) supply chain inefficiency ratio and (3) working capital productivity with both theoretical and practical listing of related literature (Table 1). Having outlined the detailed interpretation of related literature and necessary research gaps, the framework (Fig. 1) and important formulas to measure the selected performance metrics, i.e. (1) total length of the supply chain, (2) supply chain inefficiency ratio and (3) supply chain working capital productivity, are discussed as under (Shah [39], Sriyogi [43]). Various indicators used in performance metrics are presented in Fig. 2. (1) Total length of the supply chain—The total length of the supply chain is [given by Eq. (4)] arrived at by adding up the days of inventory for raw materials (DRM), days of work in progress (DWIP) and days of finished goods (DFG). The firm that has the minimum total length of the chain is said to have best performance. Table 1 Summary of literature findings Author and year of publication Category Focus objectives/Research gaps Shah and Singh [38] Theoretical (performance measures) Authors proposed (1) supply chain length (2) supply chain inefficiency ratio (3) working capital productivity as benchmarking metrics for internal supply chain performance. Work can be extended by performing empirical investigations to validate the results. Elmuti [16] Theoretical (performance measures) Studied the impact of SCM on overall organizational effectiveness and identified problems affecting SCM success and found that the firms implement SCM are to reduce cost, inventory and cycle time, which needs to be further investigated by considering suitable SCM performance metrics in different supply chains. Basnet et al. [4] Practical (case study) This paper illustrated an empirical study of benchmarking on supply chain practices in New Zealand. Further work for identification and validation of SCM practices particularly suited to manufacturing industries is required. Dangayach and Deshmukh [15] Theoretical (survey study) Authors stressed that Indian industry is facing competition both from imports and from multinational companies in the domestic market because new competition is in terms of improved quality, products with higher performance, reduced cost, a wider range of products, and better service, all delivered simultaneously but they failed to deliver on time, so it needs to reduce the length of the supply chain by analysing the supply chain network. Gunasekaran et al. [22] Theoretical (performance measures metrics) Authors have concluded that many companies have not succeeded in maximizing their supply chain’s potential because they have often failed to develop the performance measures and metrics needed to fully integrate their supply chain to maximize effectiveness and efficiency. Sridharan et al. [44] Practical (case study) Authors conducted a practical study to investigate supply chain implementation issues that could have major impact on the value of firms which is otherwise the capital productivity of firms. The issue has been addressed along with length and inefficiency in a paint supply chain by us as one of the important SCM metrics. Singh et al. [41] Practical (performance measures) Authors utilized benchmarking and performance measurement to investigate SCM practices at a number Indian manufacturing organizations by considering different metrics such as improving on time delivery, reducing inventory costs, to secure supply of raw materials and components, lowest possible product cost, reducing order to delivery cycle time, integrating suppliers, etc. and stressed the need for further analysis. Wong and Wong [46] Theoretical (performance measures) Authors focused on the past literature of supply chain benchmarking and found that most of the past literature had not viewed supply chain as whole entity and there is a scarce of empirical studies. So, authors in the present study has focused on the benchmarking and empirical study by considering three metrics, i.e. length, inefficiency ratio and capital productivity. Soni and Kodali [42] Theoretical (performance measures and case study) Authors proposed a methodology for the internal benchmarking to reduce the variability in performance among supply chain of same focal firms by considering a case study and stressed the need to develop a framework which can measure length and inefficiency of a supply chain. Arlbjorn et al. [2] Conceptual (exploratory study) Authors presented exploratory studies that aim to provide a better understanding of supply chain innovation, mirroring leading edge practices, and providing a sound terminological and conceptual basis for advanced academic work in the field. Author emphasized that there is a lack of common terminology of agreement about the conceptual understanding of key performance metrics, and of related empirical work related to SCM performance which needs to be abridged. Logist. Res. (2014) 7:113 Page 3 of 16 113 123 DRMi¼RMi365 CRMi ð1Þ where i=index for time period which is taken as a year (i.e. 365 days) DRM i =days of raw material inventory for time period iRM i =raw material inventory for time period iCRM i =cost of raw material for time period i DWIPi¼SFGi365 CPi ð2Þ where DWIP i =days of work in process inventory for time period iSFG i =semi-finished goods inventory for time period iCP i =cost of production for time period i DFGi¼FGi365 CSi ð3Þ where DFG i =days of finished goods inventory for time period iFG i =finished goods inventory for time period iCS i =cost of sales for time period i Total length of the chain (in days) ¼DRMiþDWIPiþDFGi:ð4Þ Cost of raw material is the total cost of raw material consumed during the accounting period. It also includes incidental expenses for procuring raw materials. In this paper, cost of raw material value is directly considered from financial statement and is represented by CRM. (2) Supply chain inefficiency ratio—This ratio measures the relative efficiency of internal supply chain management. The ratio will be low for the firms with better performance. SCIi¼SCCi NSi ð5Þ SCCi¼DCiþINViICCið6Þ where (1) SCI i =supply chain inefficiency ratio for the time period i(2) SCC i =supply chain management costs for the time period i(3) DC i =distribution cost for the time period i(4) ICC i =inventory carrying cost percentage for the time period i(5) INV i =inventory for the time period i(6) NS i =net sales for the time period i. This measure is known as the internal supply chain inefficiency ratio since the internal supply chain management cost would be higher if the operations are not optimal and there is inefficiency in the system. Distribution cost includes the expenses incurred in transportation and material handling. To have an efficient and flexible distribution, firms try to achieve optimization in activities related to transportation, loading, unloading and warehousing. The firms that manage their internal supply chain processes in an efficient manner will have lower levels of inventory. The lower level inventory is achieved by better purchasing, planning, manufacturing and distribution processes [43]. (3) Supply chain working capital productivity—The analysis of firms on these metrics will also be based on the level of inventory, accounts receivable and accounts SCM Performance metrics Total length of the supply chain Supply chain inefficiency ratio Supply chain working capital productivity Firm performance Fig. 1 Research framework Total length of the supply chain Indicators Indicates that DRM Days of raw material RM Raw material inventory CRM Cost of raw material DWIP Days of work in process SFG Semi finished goods Inventory CP Cost of production DFG Days of finished goods Inventory FG Finished goods inventory CS Cost of sales Supply chain inefficiency ratio Indicators Indicates that SCC Supply chain Management cost DC Distribution cost INV Inventories level ICC Inventory carrying Cost NS Net sales Supply chain working capital productivity Indicators Indicates that SWC Supply chain Working capital INV Inventories level AR Account receivables AP Account payables SWCP Supply chain working Capital productivity NS Net sales Fig. 2 Indicators of performance measurement metrics 113 Page 4 of 16 Logist. Res. (2014) 7:113 123 payable. Firms with efficient supply chains will have high supply chain working capital productivity. SWCi¼INViþARiAPið7Þ where SWC i =supply chain working capital for the time period iINV i =inventory for the time period iAR i =accounts receivable from the dealers/distributors for the time period iAP i =accounts payable to the suppliers from the time period i SWCPi¼NSi SWCi ð8Þ where SWCP i =supply chain working capital productivity for the time period iNS i =net sales for the time period i. Accounts receivable is termed as sundry creditors in the public databases. These are basically the distributors and the dealers who buy the products and owe payment to the firm. Inventory is a composite of raw materials, semi-finished goods and finished goods inventories. Accounts payable is termed as sundry debtors in the public data bases. These are basically the suppliers of raw materials to whom the firm owes payment. 3 Research methodology The section provides details regarding the research objectives/solution methodology along with description of case settings/environment. The following research objectives have been deduced for the study: (1) To benchmark the SCM performance based upon metrics, i.e. (a) total length of the supply chain, (b) supply chain inefficiency ratio and (c) supply chain working capital productivity. (2) To find out correlation between performance measures and SCM practices. (3) To empirically test the relationship between SCM practices and performance measures by formulating various hypothesis. (4) To see whether significant difference with respect to selected metrics exists between SCM firms and non-SCM firms. A framework to measure SCM performance based upon two independent approaches, i.e. (1) analytical approach to benchmark and compute SCM performance metrics based on company data (2) empirical approach based upon hypothesis formulation and statistical validation. In the first approach, two paint companies (detail discussed in Sect. 4) have been considered and data related to performance metrics, i.e. (1) total length of the supply chain; (2) supply chain inefficiency ratio; and (3) supply chain working capital productivity is used to benchmark the performance of supply chain. DMAIC (Define–Measure–Analyse– Improve and Control), a six sigma process is used to identify and analyse the problems for improving SCM performance metrics. Further, in order to examine the relationship between best SCM practices and SCM performance measures, empirical analysis has been done by formulating various research hypotheses, i.e. (H1–H6). H1 The use of good SCM practices results in improving the total length of the supply chain. H2 SCM practices helps to decrease the supply chain inefficiency ratio. H3 SCM practices have positive effect on the supply chain working capital productivity. H4 Firms those employing SCM practices will perceive that their total length of the chain is better than those do not employing SCM practices. H5 Firms those employing SCM practices will perceive that their inefficiency ratio is better than those do not employing SCM practices. H6 Firms those employing SCM practices will perceive that their supply chain working capital productivity is better than those do not employing SCM practices. The case settings are described in the paragraph as under: A structured questionnaire (‘‘Appendix 2’’) was designed based on the initial feedback received against a pilot questionnaire and subsequent personal interactions held with academicians, and people from paint companies in the Northern region of the country. In order to measure the variables of firm performance metrics, the questionnaire used a six-point Likert scale for supply chain practices in general and specifically mentioned the terms related to total length of the chain, supply chain inefficiency ratio and supply chain working capital productivity. In order to define the firms that are using SCM or not, a dichotomous variable was used that expressed either the existence of SCM (1) or without SCM (2). The sample was selected randomly. Firms with less than 250 employees were considered as SMEs. A total of 150 questionnaires were mailed and sent out to the companies. The respondents were followed up by phone and mail to increase the response rate. A total of 60 usable surveys were received representing a response rate of 40 %. From the 60 responses, 45 were employing SCM practices and the remaining 15 were not employing SCM practices. Table 2 presents the distribution of the respondents by firm size. Logist. Res. (2014) 7:113 Page 5 of 16 113 123 4 Case study analytical approach A case from paint industry situated in Northern part of India is under taken to describe the framework and validate the performance measures. The cost data of two paint companies is collected from the annual report of the paint companies for financial year 2005–2006 to financial year 2009–2010 (shown in ‘‘Appendix 1’’), and benchmarking exercise between two companies is done by measuring the metrics, i.e. total length of the supply chain, inefficiency ratio and working capital productivity metrics. Based upon cost data extracted from the financial report of companies, length of the supply chain, supply chain inefficiency ratio and supply chain working capital productivity for both companies are calculated using the formulas listed in Sect. 2for financial year (2005–2006) as presented in the Table 3. The yearwise comparison of financial year from 2005–2006 to 2009–2010 for all the three metrics is shown in Figs. 3,4,5. As shown in the Fig. 3, company A is having the minimum length as compared to company B. The company A has maintained the total length, but the company B is having more length during period 2005–2006 which was about 160 days which increased 170 days in year 2006–2007 and further dropped to 132 days in year 2008–2009. This implies that supply chain length for process of raw material, semi-finished goods and finished goods is not balanced properly. The company A following good SCM practices is having less total length of the chain throughout the years. So, according to the trend in Fig. 3, company A is performing better. As shown in the Fig. 4, the company A is having less inefficiency ratio than company B. But after that, company B somehow manages to reduce his ratio. It was about 0.088 in year 2005–2006 and then it decreased to 0.087 in year 2006–2007, but after that, it dropped to between 0.07 and 0.06 in year 2008–2009 and year 2009–2010. It shows that they have tried to manage inventory cost as well as distribution cost. Figure 5shows the comparison of the supply chain working capital productivity of the companies. It is expected that if the company is improving the total length as well as the inefficiency ratio, then the productivity of the Table 2 The distribution of the respondents by firm size Number of employee Number of respondent Percent Cumulative percent 0B50 6 10 10 [50 B100 9 15 25 [100 B150 13 21.67 46.67 [150 B200 12 20 66.67 [200 B250 15 25 91.67 [250 B300 5 8.33 100.00 Table 3 SCM performance metrics calculation for year 2005–2006 Total length of supply chain (in days) Company A 106.09 Company B 160.54 Supply chain inefficiency ratio Company A 0.0648 Company B 0.088 Working capital productivity of SCM Company A 7.65 Company B 4.57 Fig. 3 Yearwise comparison of total length of supply chain Fig. 4 Yearwise comparison of supply chain inefficiency ratio Fig. 5 Yearwise comparison of supply chain working capital productivity 113 Page 6 of 16 Logist. Res. (2014) 7:113 123 firm would increase. The company A is having more capital productivity than company B. But somehow, the company B is improving the performance by practicing SCM measures as evident from Fig. 5which shows it is possible only because of reduction in inefficiency ratio. The details of improved plan with necessary definitions and tools (based on DMAIC six sigma methodology) which the company B has adopted to improve SCM performance metrics in their supply chain network are presented in Table 4. To initiate the improvement process, a supply chain network model is developed for measuring performance of various entities as shown in Fig. 6. The network consists of entities such as suppliers, manufacturer, warehouse, distributors and retailers through which raw materials are acquired, transformed and distributed to the customers. The objective of each entity is to make easy the scheduling of materials from upstream to downstream and, in turn, deliver products to the customers. Various phases under DMAIC process are discussed as under: 4.1 Define phase The aim of this phase is to determine the customer and process requirements that helps to define the scope of different metrics, i.e. total length of the supply chain, supply chain inefficiency ratio and supply chain working capital productivity for improving SCM performance. During define phase SIPOC (Supplier, Input, Process, Output and Customers), diagram (Fig. 7) was constructed to investigate the potential causes with respect to different metrics. As shown in the SIPOC diagram, manufacturing process starts from mixing of raw material and finishes with packaging and storage. 4.2 Measure and analyse phase Data were collected with respect to different performance indices, i.e. total length of the supply chain, supply chain inefficiency ratio and supply chain working capital productivity to find out the problems in the network related to different entities. The data of different process defect such as (1) shrinkage (2) blending (3) grinding (4) thinning and dilution (5) filtration and finishing (6) packaging and storage were also collected to analyse the process performance. In the analyse phase, brainstorming sessions were undertaken by team members in order to identify potential factors that could result in increasing the total length of the supply chain, inefficiency ratio and working capital productivity. Further, a cause analysis and validation plan to measure and analyse the potential causes related to different metrics with reference to different entities involved in supply chain network diagram are made. Based upon cause analysis and validation plan, the role of various entities involved in the network is discussed in the following paragraphs. Table 4 Definition and tools used in DMAIC process Phases Definitions Tools used Define What is the problem? Does it exist? What type of defects exists? SIPOC diagram, data collection, brainstorming, voice of customer, Pareto chart Measure How is the process measured? How is it performing? Data collection, brainstorming, histogram/ frequency plot, Analyse What are the most important causes of defects? Cause and effect analysis, SPC run chart, ttest, ANOVA test Improve How do we remove the causes of defects? Brainstorming, improvement plan, process sigma, material flow synchronization (flow diagram) Control How can we maintain the improvements? SPC run chart, box plot, histogram/frequency plots Supplier 1 Entity 1 Supplier 1 Entity 2 Manufacturer Entity3 Warehouse Entity 4 Distributor Entity 5 Customers Entity 7 Retailer Entity 6 Orders Flow of product SCM performance 1. Total length of the supply chain 2. Supply chain inefficiency ratio 3. Supply chain working capital productivity Fig. 6 Company supply chain network Logist. Res. (2014) 7:113 Page 7 of 16 113 123 4.2.1 Supplier to supplier variation Suppliers are the person or groups providing key materials, or other resources to the process. In our case, suppliers (entity 1 and 2) provide all the raw materials which are needed for paint manufacturing. To study the variation on the part of suppliers, the data of the number of orders not delivered to the manufacturer from supplier on specified time were collected and ttest was performed to compare the supplier to supplier variation. The details of descriptive statistics (ttest) are given in Table 5. The Levene’s Ftest for equality of variances equals 0.987 and is statistically not significant at the 0.325 level. From the results, it is observed that supplier to supplier variation is significant and it is root cause, as significant value indicates that p\0.05, and therefore, it is significant at less than the 0.05 level for a two-tailed test, i.e. t(58) =5.246, p=0.000. So, a significant difference exists in the performance of suppliers in providing raw material and information to the manufacturer. 4.2.2 Manufacturer Based on the historical records and observations, the different types of defects found in manufacturing process were categorized as (1) shrinkage (2) blending (3) grinding (4) thinning and dilution (5) filtration and finishing and (6) packaging and storage. In order to investigate the role of manufacturing, entity data related to the process defects were collected. Pareto diagram in Fig. 8shows the overall distribution of defects with shrinkage as the major one which contributes to 59.76 % of total defects. Other defects with considerable percentage were blending (14 %), grinding (9.5 %), thinning and dilution (7.5 %), filtration and finishing (5.0 %) and packaging and storage (4.3 %) [32]. This prioritization of defects using Pareto helps the team to focus on improvement actions that are linked with defects. To determine the overall distribution of percentage defective and the frequency of a range of defects, histogram plots are obtained, as shown in Fig. 9a–d, and statistical details such as mean, standard deviation are presented in Table 6, respectively. From the results, it is observed that process defects result in inefficient supply chain, increase in supply chain length (by not meeting the target at time) and reduction in capital productivity (loss due to rework, wastage and overhead costs). 4.2.3 Warehouse Warehouse is the most important entity in the supply chain network. The efficiency and effectiveness in any distribution network in turn are largely determined by the functions of the nodes in such a network, i.e. warehouses. The functions performed by a warehouse include (1) receiving the goods from a source, (2) Storing the goods until they are required, (3) picking the goods when they are required and (4) shipping the goods to the appropriate user. It was suspected that there was a possibility of variation with Suppliers Raw Material Process Outputs * Quality * Process capability * DPMO * Cost Inputs Pigments Binders Solvents Fillers Driers Plasticizers Customers *Wholesalers *Offices *Houses *Factories *Furniture shops *Industries *Architectural Dept. Requirements Right product Right time Right quality Right quantity Mixing Grinding Homogeneity Checking Thinning/ Dilution Filtration & Finishing Packaging & Storage Fig. 7 SIPOC diagram Table 5 Independent sample ttest of suppliers Group Mean NSD Fvalue tvalue df Sig. (twotailed) Supplier 1 22.10 30 3.8716 0.987 5.246 58 0.000 Supplier 2 26.66 30 2.7833 113 Page 8 of 16 Logist. Res. (2014) 7:113 123 Appendix 2 QUESTIONNAIRE General Information Name of organization Please indicate the number of employees in your organization. 0≤≤ ≤150 ≤200 >200≤ 100 >100 250 >250≤300 For empirical analysis Q.1 How much day’s raw material takes to arrive in Inventory from vendor? Q.2 How much days work in process takes in Inventory? Q.3 How much days finished goods takes in Inventory? Q.4 How much is your Inventory carrying cost per annum? Q.5 How much is your distribution cost per annum? Q.6 How much is your net sales per annum? Q.7 How much is your supply chain inefficiency ratio? Q.8 How much is your a/c receivable in your current asset per annum? Q.9 How much is you’re a/c payable in your current liability per annum? Q.10How much is your working capital per annum? Q.11How much is your supply chain working capital productivity 50 >50 >150 10 days 20 days 30 days 40 days 50 days above 50 days 10 days 20 days 30 days 40 days 50 days above 50 days 10 days 20 days 30 days 40 days 50 days above 50 days 0 .05 0 .10 0.15 0 .20 0 .25 above .25 50cr 100cr 150cr 200cr 250cr above 250cr Below 1000cr 2000cr 3000cr 4000cr 5000cr above 5000cr 0.05 0.06 0.07 0.08 0.09 above .09 100cr 150cr 200cr 250cr 300cr above 300cr 100cr 150cr 200cr 250cr 300cr above 300cr 100cr 150cr 200cr 250cr 300cr above 300cr 478 91011 References 1. Akyuz GA, Erkan TE (2010) Supply chain performance measurement: a literature review. Int J Prod Res 48(17):5137–5155 2. Arlbjorn JS, de Haas H, Munksgaard KB (2011) Exploring supply chain innovation. Logist Res 3(1):3–18 3. Barber E (2008) How to measure the value in value chains. Int J Phys Distrib Logist Manag 38(9):685–698 4. Basnet C, Corner L, Wiense J, Tan K (2003) Benchmarking supply chain management practice in New Zealand. 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