A QUALITATIVE STUDY FOR MEASURING WAREHOUSE PERFORMANCE

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1 A QUALITATIVE STUD FOR MEASURIG WAREHOUSE PERFORMACE by Premporn Khemavuk School of Mechanical and Manufacturing Engineering, The University of ew South Wales, Australia and Maruf Hasan 1 School of Mechanical and Manufacturing Engineering, The University of ew South Wales, Australia pimmz@hotmail.com ABSTRACT This research aims to validate a SEM model for measuring warehouse performance using data from an international company in Australia (company G). Moreover, a methodological triangulation method was also adopted to test whether different methodological approaches produce convergent findings about warehouse performance measurement. These three different methods are the SEM model, the multiple case research study and validation of the SEM model using data from 80 companies in Thailand and a company in Australia. With the results from the triangulation method, it is obvious that the SEM model can be used to measure the performance of warehouses in Thailand and Australia. Since the SEM model consists only of significant indicators, it is more appropriate than company G s scorecard. Furthermore, the SEM model can overcome the limitations of traditional models by allowing companies to compare their performance over time. KEWORDS SEM, Warehouse Performance, Triangulation Method, Qualitative Study ITRODUCTIO The warehouse has been viewed in many different ways and has many definitions. Cavinato (1990) views the warehouse as the place to hold, move, sort, transfer and change the form of inventories. Whereas, Spencer (1993) argues that the warehouse is a production system. He states that the warehouse is a combination of single operations, culminating at the end as a whole process. Gunasekaran et al. (1999) believe that the warehouse is a combination of both physical processes of material handling and methodologies such as inventory control and production control. Warehouse performance has been measured with each indicator separately, however, this traditional measurement has some limitations that it cannot compare the performance over time regarding the unknown relative values between indicators (McGinnis et al., 2002). Such limitations have been overcome by the work of Khemavuk and Hasan (2010), a model for measuring warehouse performance was developed with Structural Equation Modelling (SEM) technique and analogy based approach. The results from the study show that the SEM model is able to measure the warehouse performance with a MMRE value of 2.99%. Furthermore, the SEM model was developed using an analogy based approach. This development allows the company to retrieve information about other warehouses from a dataset in order to compare it with the company s information. METHODOLOG This study consists of two parts. Firstly, multiple case studies were in-depth interviewed in order to be used in conjunction with the SEM model (from the work of Khemavuk and Hasan, 2010). Lastly, the SEM model was validated using data from an international company in Australia. Campbell and Fiske (1959) started using triangulation in social sciences. They recommend that more than one method should be used in the validation process to ensure that the variance reflected the trait not the method. Later, Denzin (1978) discusses methods of triangulation by outlining four types of triangulation: data triangulation, investigator triangulation, theory triangulation and methodological triangulation.

2 A methodological triangulation method was adopted to test whether different methodological approaches produce convergent findings about warehouse performance measurement. Research framework of multiple case studies In this step, a research framework was first constructed and the variables and questions for this study were identified. Voss et al. (2002) suggest that there needs to be consideration of the categories intended for study and their relationships to each other. Miles and Huberman (1994) also point out that a research framework should explain the categories needed for study in narrative form or in graphical form. The procedures in research frame work are described as follows. Defining research questions It is very important to have a well defined research question at the start of the process in order to guide the collection of data. Research Question 1: Is the SEM model generated able to measure warehouse performance? This research question was derived from the SEM model and the study of McGinnis et al. (2002) and Mentzer and Konrad (1991), where the aim is to determine if the SEM model is able to measure the performance of any warehouse. Research Question 2: What are the key variables for measuring warehouse performance? In this multiple case research study, the focus is on three themes: warehouse management (WM), warehouse functions (WF) and warehouse performance measurement (WPM). These three themes were derived from both the SEM model and the study of many researchers including Huq et al. (2006); McGinnis et al. (2002); and Mentzer and Konrad (1991). Therefore, this research question will determine the key variables that companies use to measure their warehouse performance, with a focus on these three themes. Choosing cases The ideal number of cases is hard to define; however, the fewer the case studies, the greater the opportunity for in-depth observation (Voss et al., 2002). Therefore, nine case studies of six companies from Thailand and other three companies from Australia were carried out. Managers from the nine companies were interviewed in-depth on the three themes selected (warehouse management, warehouse functions and warehouse performance measurement). Later, the pattern matching technique was adopted to evaluate and discuss the findings. In addition, one of three companies from Australia was chosen for validating the SEM model. Pattern matching was defined by Campbell (1975) as a useful technique for linking data to the propositions. He adds that pattern matching is a situation where several pieces of information from the same case may be related to some theoretical proposition. In case study research, the pattern matching technique has been adopted by many researchers such as Santos et al. (2001). They used pattern matching in their benchmarking study between UK and Brazilian construction sites. Developing research instruments In case study research, research instruments used for collecting data include interviews, personal observation, informal conversations, or even attendance at meetings and events (Voss et al., 2002). Therefore, in this study in-depth interviews for collecting data were conducted using four steps. The first step was the design of the question set to be used in the interviews. Three themes to be covered during the interviews were outlined: warehouse management (WM), warehouse functions (WF) and warehouse performance measurement (WPM). In the next step, access to the company was organised, highlighting the benefits of having their warehouses analysed in a systematic way. Then an agenda for the research meetings was set up, providing an outline of areas of warehouse management being investigated in this study and the type of data wanted for the analysis. The last step was to conduct interviews. It is important to keep in mind that the interviewer should be able to ask good questions, be flexible and be unbiased (in, 1994). The taking notes method was used to transcribe the data in this

3 study. This method was selected because this method is less likely to inhibit interviewees from answering the questions during the interviews. This may occur with the tape recording method, which is also very time-consuming when transcribing tapes (Voss et al., 2002). However, the taking notes method also has its weaknesses. In particular, it is not possible to record all the information provided by the interviewees. Therefore, field observation was used to follow up the interviews by using observation and analysis. This field observation method pays an important role in case study research because it is useful to record the ideas or incidents as soon as they occur (Voss et al., 2002). Data documentation and coding After collecting data through the interviews and field observation, the next step was to document and code the data. All data was transcribed to a Microsoft Excel spreadsheet. The documentation included ideas and insights during the field visit. When coding the data, the pattern matching method was used to limit the numbers of categories. The summary of the findings across the case studies is presented in Table 1. TABLE 1 SUMMAR OF THE FIDIGS ACROSS THE CASE STUDIES Key indicators for warehouse performance measurement A B C D E F G H I 1. Labour measures 1. Labour hour 2. Pallets/labour hour for receiving function 3. Pallets/labour hour for put away function 4. Pallets/labour hour for order picking function 5. Pallets/labour hour for shipping function 2. Cost measures 6. Labour cost 7. Transportation cost 3. Time measures 8. Downtime of storing equipment 9. Downtime of order picking equipment 10. Downtime of shipping equipment 4. Utilisation measures 11. Degree of space utilisation 12. Degree of equipment utilisation 13. Degree of labour utilisation 5. Administration measures 14. Degree of error-free orders shipped Indicators 1, 6, 7, 8, 9, 10, 11, 12, 13, and 14 are indicators from the SEM model = Case studies use indicators from the SEM model = Case studies do not use indicators from the SEM model; however, these indicators were found not to be significant to warehouse performance at a level of significance of 0.05 From Table 1, it is interesting that case studies in Thailand have very similar patterns of performance indicator usage. As expected, all of them use the same five indicators from the SEM model. These indicators are labour hour, labour cost, transportation cost, degree of space utilisation, and degree of error-free orders shipped. Only company A from the hard disk drive industry use downtime of storing equipment and degree of equipment utilisation whereas only company C and E use downtime of order picking equipment. Moreover, only company D uses degree of labour utilisation. The case studies in Australia, namely company G, H, and I, have a different indicator pattern. Company H uses nearly all the indicators from the SEM model; the exception being transportation cost, while company I uses five indicators (labour hour, labour cost, transportation cost, degree of labour utilisation and degree of error-free orders shipped). Interestingly, company G uses four indicators that were taken into account when building up the SEM model; however, they were found not to be significant to warehouse performance. These indicators are pallets/labour hour for receiving function, pallets/labour hour for put away function, pallets/labour hour for order picking function, and pallets/labour hour for shipping function.

4 VALIDATIO OF THE SEM MODEL At this step, the validation of the SEM model using an international company in Australia was performed. The company used for the validation was company G. The company is an international company operating in warehousing and transportation. This company is a provider of logistics solutions where the facility is owned by their customer and the company is responsible for the management of the facility. This company implements their own scorecard system to measure their warehouse performance. They use pallets per man hour and cartons per hour in their warehouse functions including receiving, handling, order picking and shipping functions. An example of this scorecard system is illustrated in Table 2. TABLE 2 SCORECARD SSTEM OF COMPA G Task Score UOM (Unit of Measurement) Target Achieved Actual (Weighting) Actual (Score) Check-in 5.00 Pallets per man hour Put-away Pallets per man hour Replenishment 5.00 Pallets per man hour Full picks Pallets per man hour Part picks Cartons per hour Truck-loading Pallets per man hour Turnaround times %TA less than 1.5 hrs 95% 92.32% Credits %ational Invoices 1.00% 0.70% Total From Table 2, the company measures their performance monthly and in the month indicated in the scorecard they scored out of 100. Therefore, in validating the SEM model all the necessary data for nine months were collected. These data are presented in Appendix A. The data collected were labour hour (L1), labour cost (C4), transportation cost (C5), downtime of storing equipment (T3), downtime of order picking equipment (T4), downtime of shipping equipment (T5), degree of space utilisation (U1), degree of equipment utilisation (U2), degree of labour utilisation (U3), and degree of error-free orders shipped (A5). The results show that the SEM model provided a rating for their warehouse performance that was similar to their scorecard system. For example, the company measures their performance at 97.56% while the SEM model measures their performance at 95.32% for the same month. A comparison of a second month, found a company score of 90.21% while the SEM model measures their performance at 91.35%. A full comparison of all the months tested (February to ovember 2009) is presented in Table 3. In addition, the SEM model is able to retrieve the information of other warehouses from a dataset of warehouses that perform in a similar manner to a particular company, so that the company can use the information in order to compare their warehouse with other warehouses. Month (2009) TABLE 3 WAREHOUSE PERFORMACE MEASURED FROM COMPA G S SCORECARD AD THE SEM MODEL Warehouse performance from company G s Scorecard (%) Warehouse performance from the SEM model (%) MMRE February March April May June July August September ovember

5 From Table 3, the company rating is generally higher than the score generated by the SEM model. The only exception was in April and May when the SEM model measured performance at 91.35% in April and 95.12% in May while the company measured their performance at 90.21% and 94.54% respectively. The Mean Magnitude of Relative Error (MMRE) of the SEM model was calculated. The results show that the SEM model measured the performance of company G differs from their own scorecard with the value of MMRE at (0.318/9) x 100% = 3.53%. In this study two sets of validation testing were performed. The SEM model was tested using data from 80 companies in Thailand as well as the validation using the Australian data. In both instance, it was found that the SEM model generated in this study can measure the performance of warehouses in a comparable manner to that used by the companies themselves, with only slightly different performance values from their own performance measurement systems. DATA AALSIS As stated earlier, the methodological triangulation was used to triangulate three different methods for warehouse performance measurement. These three methods are the SEM model, the multiple case research study and validation of the SEM model using data from 80 companies in Thailand and a company in Australia. From the SEM model, there are ten indicators that are significant to warehouse performance. These indicators are, for example, transportation cost, labour cost, labour hour, downtime of storing equipment, degree of labour utilisation, and degree of error-free orders shipped. The results from the multiple case study show that all ten indicators from the SEM model are used by the Thai and Australian companies to measure their warehouse performance. However, there are four indicators that only company G used to measure their performance. Such indicators are pallets/labour hour for receiving function, pallets/labour hour for put away function, pallets/labour hour for order picking function, and pallets/labour hour for shipping function. Interestingly, these four indicators were considered when building up the SEM model but they were found not to be significant to warehouse performance at a level of significance of The results from the model validation using 80 companies in Thailand and an international company in Australia (company G) show that the SEM model is able to measure the performance of their warehouses with MMRE values of 2.99% and 3.53% respectively. Therefore, these results show that there is high agreement among these three methods; the indicators from the SEM model are used in practice for measuring warehouse performance by companies from both Thailand and Australia. COCLUSIO When building up the SEM model, 25 indicators from five measurement areas were taken into account. However, only ten of them were found to be significant to warehouse performance. The findings from case studies using six companies in Thailand and three companies in Australia strongly agree that the SEM model is able to measure the performance of warehouse. However, company G in Australia argues that their own scorecard with different indicators from the SEM model is also able to measure their warehouse performance effectively. Interestingly, the indicators they used were the indicators found not to be significant to warehouse performance when building up the SEM model. With the results from the triangulation method, it is obvious that the SEM model can be used to measure the performance of warehouses in Thailand and Australia. Since the SEM model consists only of significant indicators, it is more appropriate than company G s scorecard. Furthermore, the SEM model can overcome the limitations of traditional models by allowing companies to compare their performance over time. REFERECES Campbell, D. (1975), Degrees of Freedom and the Case Study, Comparative Political Studies, Vol. 8, pp Campbell, D.T. and Fiske, D.W. (1959), Convergent and Discriminant Validation by the Multitrait-Multimethod Matrix, Psychological Bulletin, Vol. 56, o. 2, pp

6 Cavinato, J. (1990), Managing Different Types of Inventory, Distribution, Vol. 89, o. 3, pp Denzin,.K. (1978), The Research Act: A Theoretical Introduction to Sociological Methods, ew ork: McGraw-Hill. Gunasekaran, A., Marri, H.B. and Menci, F. (1999), Improving the Effectiveness of Warehousing Operations: A Case Study, Industrial Management & Data Systems, Vol. 99, o. 8, pp Huq, F., Cutright, K., Jones, V. and Hensler, D.A. (2006), Simulation Study of a Two-Level Warehouse Inventory Replenishment System, International Journal of Physical Distribution & Logistics Management, Vol. 36, o. 1, pp Khemavuk, P. and Hasan, M. (2010), Warehouse Performance Measurement: Structural Equation Modeling Technique and Analogy Based Approach, International Journal of Logistics and Transport, Vol. 3, o. 2, pp McGinnis, L.F., Chen, W.-C., Griffin P., Sharp G., Govindaraj T., and Bodner D. (2002), Benchmarking Warehouse Performance, accessed on ovember Mentzer, J.T. and Konrad, B.P. (1991), An Efficiency/Effectiveness Approach to Logistics Performance Analysis, Journal of Business Logistics, Vol. 12, o. 1, pp Miles, H. and Huberman, M. (1994), Qualitative Data Analysis: A Source Book, CA: Sage Publications. Santos, A., Powell, J.A. and Hinks, J. (2001) Using Pattern Matching for the International Benchmarking of Production Practices, Benchmarking: An International Journal, Vol. 8, o. 1, pp Spencer, M.S. (1993), Warehouse Management Using V-A-T Logical Structure Analysis, The International Journal of Logistics Management, Vol. 4, o. 1, pp Voss, C., Tsikriktsis,., and Frohlich, M. (2002), Case Research in Operations Management, International Journal of Operations & Production Management, Vol. 22, o. 2, pp in, R. (1994), Case Study Research, CA: Sage Publications. APPEDIX A DATA COLLECTED FROM COMPA G FOR IE MOTHS Month L1 C4 C5 T3 T4 T5 U1 U2 U3 A Feb 4,800 97,000 50, % 85% 80% 95% Mar 4,000 81,000 30, % 88% 85% 97% Apr 4, ,000 45, % 82% 83% 95% May 5, ,000 55, % 75% 80% 98% Jun 4,640 93,000 42, % 82% 82% 97% Jul 4,800 95,000 47, % 80% 89% 92% Aug 5, ,000 52, % 77% 90% 95% Sep 4,800 98,000 46, % 79% 88% 96% ov 4,320 90,000 40, % 80% 85% 91%