Abstract
This paper aims to develop a comprehensive hierarchical performance measurement model. The proposed model not only determines a manufacturing company’s overall performance within its industry but also obtains its strengths and weaknesses in critical activities. It lets one to combine a company’s performance scores in seventeen critical activities with important industry-specific objectives to obtain a single overall performance score by using a 4-Point Fuzzy Scale and a modified fuzzy version of the Technique for Order Preference by Similarity to Ideal Solution approach. The calculated overall performance scores provide a ranking order among manufacturing companies within their industry. In addition, it also enables each company to compare its performance in critical activities with respect to other companies in its industry. Furthermore, the performance measurement model has the capability to determine what a company should do to improve its performance in critical activities. This paper provides an example to illustrate the application of the proposed model.
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Appendices
Appendix 1
Objectives:
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O1. Improve the products’ technological level and increase value added portions in their prices
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O2. Improve the manufacturing capability and competitiveness
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O3. Improve the customers’ profile and increase the percentage of the export revenues
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O4. Improve the personnel quality
Critical activities:
Activities | The list of statements |
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A1. Location selection | (1) The manufacturing company is close to several research institutes, technical universities and educational facilities (2) Various industrial zones and manufacturing companies exist near the company (3) The company can hire skilled labor and engineers easily (4) The company is close to multiple transportation ways. The company can reach to its customers using multiple transportation way (5) The location of the company is close to its customers. The delivery distance time to customers is short. Transportation occurs with low carrying costs (6) The personnel is happy with the location of the company. There is no relocation request from the company personnel |
A2. Effectiveness of the plant design and part flow in the plant | (1) The layout plan is developed using a proper methodology (2) The transportation distances of the parts are low (3) Keeping track of the parts on the material handling system is not considered a problem (4) There is no pile of materials waiting between machines, and the parts completed on the machines are moved on the planned time (5) Necessary areas are available for support activities such as maintenance and tooling placement (6) The layout can easily be expanded or modified when product variety and production volumes of product types change (7) The machines do not stay idle because of the delays in arrivals of parts, tooling and equipment |
A3. Effectiveness of the maintenance and repair activities | (1) There is a maintenance and repair department in the plant. Necessary personnel and financial resources are allocated to the department to perform its activities (2) The maintenance department prepares maintenance and repair plans of the resources in the plant (3) The maintenance and repair activities are performed according to the developed maintenance plans (4) Prevention of the machine and system failures is considered more important than other objectives by the plant management (5) Modifications and improvements are performed on the resources to improve effectiveness and useful lives of them in addition to the routine maintenance and repair activities 6) Breakdowns of the resources are rare and total repaid time is not considered as significant in the plant |
A4. Technological level of the plant | (1) An economic and technological evaluation is performed in the purchase of new resources (2) A comparison of the requirements of the customers and products are compared with the capabilities of new technologies are compared before their purchase (3) New technologies and machineries are preferred over conventional ones by plant management and widely used in the company (4) Implementation plans are prepared during the application of the new technologies to maximize their contribution to the competitiveness of the company (5) The company regularly attends exhibitions and visits builders and suppliers of the machine tools and other equipment (6) Benchmarking is routinely performed to observe and selectively adopt new technologies and practices being used in different industries’ best performing plants and competitors |
A5. Quality improvement activities | (1) The planning of the quality improvement studies starts with the strategic goals of the company and ends with the implementation activities on the shop floor (2) Measurement and feedback of the results of the programs are included and considered important in quality improvement studies (3) Personnel from all levels of the hierarchy contribute to the quality improvement studies (4) Personnel are aware of the importance of taking quality certifications to improve company’s reputation in the eyes of its customers (5) Several quality certificates are already taken, and their requirements are implemented throughout the plant (6) The personnel show no resistance to the new improved ways of doing things and see them as necessary for the company’s long-term survival |
Appendix 2: Calculation of the overall performance scores of the companies
Step 1 The members of the decision matrix (\( \tilde{x}_{ij} \)’s) and weights of the critical activities with respect to the overall goal can be expressed as \( \tilde{x}_{ij} = \left( {a_{ij} ,b_{ij} ,c_{ij} ,d_{ij} } \right) \) and \( \tilde{w}_{j} = \, (\alpha_{j} ,\beta_{j} ,\gamma_{j} ,\delta_{j} ) \), respectively. For normalization, the highest decision matrix member in each ‘critical activity’ column (denoted as \( \tilde{x}_{j}^{*} = \left( {a_{j}^{*} ,b_{j}^{*} ,c_{j}^{*} ,d_{j}^{*} } \right) \)) must first be determined using Eq. (8).
Then, the normalized decision matrix is constructed using Eq. (9) (Chen and Hwang 1992).
Step 2 The normalized decision matrix \( (\tilde{V}) \) is weighted next using Eq. (10).
where
Step 3 Each fuzzy component of the weighted normalized decision matrix is defuzzified using Eq. (12) (Cheng and Lin 2002; Chen and Hwang 1992). The obtained crisp value of a trapezoidal fuzzy number (\( v_{ij} = (a,b,c,d) \)) is denoted as vij.
Step 4 The ideal solution vector, A*, and the negative-ideal solution vector, \( A_{{}}^{ - } \), include the best and the worst performance scores, respectively, and are calculated using Eqs. (13–16).
Step 5: Calculation of distance measures The distances of company i to the ideal solution (d * i ) and from the negative-ideal solution (d − i ) are calculated using Eqs. 17 and 18, respectively.
Step 6 The overall performance score (C * i ) is calculated using Eq. 19. A higher score corresponds to a better performance (Chen and Hwang 1992).
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Yurdakul, M., İç, Y.T. Development of a multi-level performance measurement model for manufacturing companies using a modified version of the fuzzy TOPSIS approach. Soft Comput 22, 7491–7503 (2018). https://doi.org/10.1007/s00500-018-3449-6
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DOI: https://doi.org/10.1007/s00500-018-3449-6