Benchmarking : An essential Quality tool
- 1. Benchmarking : An essential Quality tool Case Study : Benchmarking the manufacturing process of Sri Lankan garment manufacturing companies by Ajay Gayakwar, Bittu Singh, Radhe Kumar, Shubham Singh â DFT â V NIFT, Gandhinagar
- 2. 1 Table of Contents 1. What is Benchmarking ? .................................................................................................................2 1.1 Types of Benchmarking :...............................................................................................................2 2. Steps in the benchmarking process................................................................................................3 3. Key Objectives and Results expected from Benchmarking.................................................................4 4.What is Performance Measurement ?.................................................................................................5 5. What are Key Performance Indicators(KPI) ? .....................................................................................5 6. Case Study : Benchmarking the manufacturing process of Sri Lankan garment manufacturing companies...............................................................................................................................................5 7. Conclusion........................................................................................................................................12 8. References ........................................................................................................................................13
- 3. 2 1. What is Benchmarking ? Benchmarking is the procedure of comparing products, services and processes with organizations considered to be the best in one or more aspects of their operations. Benchmarking can be of great help to an organization by providing necessary insights to understand how one organization compares with similar organizations. It helps an organization identify areas, systems or processes for improvements. The improvements may be incremental or in the form of Business Process Re-engineering. 1.1 Types of Benchmarking : a) Technical Benchmarking helps to determine how well both an organization and the competition are fulfilling customer needs in terms of design requirements. This evaluation is expressed as a score plotted on the vertical axis. The design requirements can also be scored on a scale of 1-4, with four being the best. It is performed by the design staff to ascertain the capabilities of products or services of leading competitors. b) Competitive Benchmarkingreflects how well an organization and the competition are satisfying the customer requirements. It is identified on the vertical axis on the left side of the matrix. It compares two organizations with respect to critically important attributes, functions or values associated with the organizationâs products or services. Benchmarking : House of Quality. Citation : Reference No. 2
- 4. 3 2. Steps in the benchmarking process Planning ⢠Identify what to benchmark ⢠Determine benchmark measurements ⢠Develop a data collection plan ⢠Select internal locations/departments Data Collection & Analysis â˘Collect data â˘Research data elements â˘Interview/Surveys â˘Form a corporate benchmarking committee â˘Location visits â˘Select & collect data â˘Data analysis Process Improvement ⢠Process change plan ⢠Implement and measure changes ⢠Updating the database ⢠Ongoing reporting Citation : Reference No. 2
- 5. 4 With Benchmarking Defining customer rerquirements Market reality Objective Evaluation High conformance Establishing effective goals & objectives Credible, unarguable Pro-active Industry-leading Developing true measures of productivity Solving real problems Understanding outputs Based on best industry practices Becoming competitive Concrete understanding of competition New ideas of proven practices & technology High commitment Industry best practices Pro-active search for change Many options Business practice break-through Superior performance Without Benchmarking Based on history or gut-feel Perception Low fit Lacking external focus Reactive Lagging industry Pursuing pet projects Strengths and weaknesses not understood Route of least resistance Internally focused Evolutionary change Low commitment Not invented here Few solutions Average of industry progress Frantic catch-up activity 3. Key Objectives and Results expected from Benchmarking Citation : Reference No. 2
- 6. 5 4.What is Performance Measurement ? Performance Measurement is defined as the set of metrics used to quantify both the efficiency and effectiveness of actions. It is the language of progress for any organization. It functions as a guide to whether the organization is in the correct path to achieve its goals. Performance Measurement Models are comprehensive models which highlight the relation among various Performance Measures used by an organization. 5. What are Key Performance Indicators(KPI) ? KPIs are defined as a number of key characteristics that help an organization to identify an appropriate set of measures to assess their performance. They help an organization to measure their current performance, identify their improvement areas and prepare plans for the overall business. 6. Case Study : Benchmarking the manufacturing process of Sri Lankan garment manufacturing companies ďś Introduction : The apparel and garments manufacturing industry is the prime source of export earnings and employment in Sri Lanka. The industry employs more than 2,80,000 people and contributes up to 40% of total industrial production. In this age of cut-throat competition, any company must continuously improve its products, processes and methods to improve their performance. The study was conducted on 25 apparel manufacturing factories to develop a Benchmarking Model including a Performance Measurement Model.This was done so that any two companies can be benchmarked against each other easily. The means of comparing used in the study were two Multi Criteria Decision Making(MCDM) techniques : Technique for Order Preference by Similarity to the Ideal Solution(TOPSIS) & Analytical Hierarchy Process(AHP). ďś Data Collection was carried out with the purpose to identify the most relevant KPIs in the Sri Lankan garments industry and develop a Performance Measurement Model. A list of 76 such KPIs was finalised through industry visits. Using these KPIs, a questionnaire was constructed and divided in segments on the basis of 10 sub- functional areas present in any factory. Final data collection was carried out through on-site visits and the factory representatives were requested to fill the questionnaires. The interviews were well- structured. Since the identity of the representatives were kept anonymous, it was possible to extract the exact and sensitive data about the factories. A scale of 1-5 was used for all the questions, with 1 âbeing not related at allâ & 5 being âvery highâ. Responses from 27 factories were used and the population size covered was 300. The population sample size was very diverse with respect to product portfolio, customers, production capacity, geographical location and the technology being used. The demography of apparel manufacturers used for the sample size is as below:
- 7. 6 Type of Manufacturer Number of Factories Percent of Sample Size Intimate wear 5 20 Active wear 5 20 Casual wear 5 20 Kids wear 4 16 Others 8 24 ďś Development of the Conceptual Models was done with keeping in mind the 3 key functional areas in a garment factory : Operations, Plan & Policy and Merchandising/Marketing. The Performance Measurement Model was adapted by the model proposed by a faculty at the University of Mauritius, namely Dinesh Kumar Hurreeram.The mentioned model was preferred because : ď It fitted with the manufacturing process of Sri Lankan garment factories. ď The layout best fitted the organizational structure. ďś Identification of the most effective KPIs : Importance Index was calculated by the following formula : Importance Index(I) = 20*R1 + 40*R2 + 60*R3 + 80*R4 + 100*R5 R1+R2+R3+R4+R5 Where ; R1= number of responses âNot Relatedâ R2= number of responses âLowâ R3= number of responses âMediumâ R4= number of responses âHighâ R5= number of responses âVery Highâ ďś Calculation of Relative Importance Weights : Relative Importance Weight = Importance index of the sub-functional area Total index of all sub-functional areas in the key functional area Citation : Reference No. 3
- 8. 7 Benchmarking Model.Citation : Reference No. 3
- 9. 8 ďś Obtaining the Performance Ranking within Sub-functional area on the basis of KPIs by applying Technique for Order Preference by Similarity to the Ideal Solution(TOPSIS) The 10 sub-functional areas identified were : Inventory Management, Product Development, Quality Assurance, Maintenance, Recruitment, Wages & Welfare, Compliance, Marketing, Merchandising and Cut, Make, Trim & Delivery. For every sub-functional area, having ânâ number of decision criteria(KPIs) and âmâ number of alternatives(manufacturers), a n*m matrix is formed. All the values in the KPIs are normalized through a statistical formula. Two types of solutions are derived at through this process. ď For positive ideal solutions, all the best values of KPIs were selected. If the KPI is a benefit attribute, the maximum value was selected. If the KPI is a cost attribute, the minimum value was selected. ď For negative ideal solutions, all the worst values of KPIs were selected. If the KPI is a benefit attribute, the minimum value was selected. If the KPI is a cost attribute, the maximum value was selected. Performance Measurement Model.Citation : Reference No. 3
- 10. 9 Separation Measures(S)& Relative Closeness Coefficients were calculated through thespecific formulae. ďś Obtaining ranking for Overall Performance by applying Analytical Hierarchy Process(AHP) Input to the AHP was the set of performance scores achieved in key functional area level. There were âmâ number of such values for each key functional area from rivals. Hence, a 3*m matrix was formed. ďś Obtaining Performance Ranking in Sub Functional Area by applying AHP The output coefficients were considered as input to the AHP. Coefficients for all manufacturers were multiplied by relative importance weights of sub-functional Criteria (KPIs) â Alternatives (Manufacturers) Matrix.Citation : Reference No. 3 Criteria (Key Functional Areas) â Alternatives (Manufacturers) Matrix.Citation : Reference No. 3
- 11. 10 areas. The multiplied values were summed up for all manufacturers to achieve performance rankings in key functional areas. Criteria (Sub Functional Areas) â Alternatives (Manufacturers) Matrix.Citation : Reference No. 3 Radar Diagram â Sub Functional Areas.Citation : Reference No. 3
- 12. 11 Final Performance Rankings.Citation : Reference No. 3
- 13. 12 ďś The final performance Rankings are highlighted in the above table. The results can also be analysed through Radar Diagrams. They are helpful in identifying performance gaps and weakest areas that require immediate attention. ďś Recommendations The project was intended to present a solution to fill up the void due to absence of a benchmarking tool in the Sri Lankan garment industry. The proposed model uses extensive mathematical operations in finding the performance rankings. A total of 14 matrices were solved by TOPSIS and AHP. To solve such complex mathematical operations, computer softwares can be used. 7. Conclusion ďś Benchmarking is an essential quality tool for any company to survive in todayâs customer-centric market and cut-throat competition. In the given case study, we see how benchmarking can be done step by step. First of all, industry visits were organised to all the concerned factories to extract the information about the KPIs being used there. A final list of 76 KPIs was derived at. An extensive questionnaire was prepared on the basis of the identified KPIs. The questionnaire was segmented on the basis of 10 sub-functional areas present in any factory. The questionnaires were asked to be filled by a representative from each of the factory, with the identity of the representative being kept anonymous to get the exact and true responses. Responses were rated from 1 to 5 with 1 being ânot related at allâ and 5 being âvery highâ. After data aggregation, a Performance Measurement Model was chosen through literature review. The list of most effective KPIs was arrived at through simple equations from the responses gathered in the questionnaires. Similarly, relative importance weights of all the sub-functional areas were calculated. After this, the performance ranks of different factories were calculated on the basis of KPIs through TOPSIS method. The performance rankings among different factories on the basis of 3 key functional areas and 10 sub-functional areas were also calculated separately through the AHP method. At the end of the process, we can point out as to which key functional areas and sub-functional areas are performing badly in different factories. Further, certain KPIs can be identified which need immediate and special emphasis to improve overall quality in the factories. After this, action plans can be developed to address the issues concerned. The action plans can be implemented and monitored from now onwards. With time, the whole benchmarking process can be carried out again according to the latest industry standards.
- 14. 13 8. References 1. ReVelle J.B. Quality Essentials: A reference guide from A to Z. Page No. 8. ASQ Quality Press. Retrieved from : https://books.google.co.in/books?id=B0GxBQfkZ9YC&printsec=frontcover&dq=Quality+Esse ntials:+A+Reference+Guide+from+A+to+Z&hl=en&sa=X&ved=0ahUKEwjzjZWbiarXAhVKCsAK He6pCSIQ6AEIJjAA#v=onepage&q=Quality%20Essentials%3A%20A%20Reference%20Guide% 20from%20A%20to%20Z&f=false 2. Graham N.O. Quality: Theory, Application & Evolution. Page No. 210. Jones & Bartlett Learning. Retrieved from : https://books.google.co.in/books?id=vBuX7X9wRx4C&pg=PA210&dq=benchmarking+quality &hl=en&sa=X&ved=0ahUKEwj- qIe8iarXAhWCHsAKHRa3Dl4Q6AEITDAH#v=onepage&q=benchmarking%20quality&f=false 3. Ratnayake R.M.V.S., Samarasekara A.V.L., Samarasinghe A.A.D.G., Dunukara D.M.P.G., Sampath U.H.T. University of Moratuwa. Benchmarking the manufacturing process of Sri Lankan garment manufacturing companies. International Journal of Engineering and Applied Sciences. Volume 4, No.5. November 2013. Retrieved from : http://eaas- journal.org/survey/userfiles/files/v4i502%20Manufacturing%20engineering.pdf If you're not benchmarking your performance against your competitors, you're just playing with yourself. - Albert Pisano