Skip to main content

Advertisement

Log in

Development of a multi-level performance measurement model for manufacturing companies using a modified version of the fuzzy TOPSIS approach

  • Focus
  • Published:
Soft Computing Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Notes

  1. Teece et al. 1997; Teece (2007), (2009); Eisenhardt and Martin 2000; Bromiley and Rau 2016; Phan et al. (2011); Hayes and Wheelwright 1984; Schroeder and Flynn 2001; Phan 2011; Prybutok et al. 2011; Ezzabadi et al. 2015; De Fellice and Patrillo 2015; Chiarini and Vagnoni 2015; Petrillo et al. 2018.

References

  • Ahmad K, Zabri SM (2016) The application of non-financial performance measurement in malaysian manufacturing firms. Procedia Econ Financ 35(2016):476–484

    Article  Google Scholar 

  • Anderson JC, Rungtusanatham M, Schroeder RG, Devaraj S (1995) A path analytic model of a theory of quality management underlying the deming management method: preliminary empirical findings. Decis Sci 26(5):637–658

    Article  Google Scholar 

  • Babic Z, Plazibat N (1998) Ranking of enterprises based on multicriterial analysis. Int J Prod Econ 56–57:29–35

    Article  Google Scholar 

  • Banker RD, Potter G, Schroeder RG (1993) Reporting manufacturing performance measures to workers: an empirical study. J Manag Acc Res 5:33–53

    Google Scholar 

  • Banks RL, Wheelwright SC (1979) Operations versus strategy—trading tomorrow for today. Harvard Bus Rev 57(3):112–120

    Google Scholar 

  • Böhm E, Eggert A, Thiesbrummel C (2017) Service transition: a viable option for manufacturing companies with deteriorating financial performance? Ind Mark Manag 60:101–111

    Article  Google Scholar 

  • Braz GT, Scavarda LF, Martins RA (2011) Reviewing and improving performance measurement systems: an action research. Int J Prod Econ 133(2011):751–760

    Article  Google Scholar 

  • Bromiley P, Rau D (2016) Operations management and the resource based view: another view. J Oper Manag 41:95–106

    Article  Google Scholar 

  • Chamodrakas I, Leftheriotis I, Martakos D (2011) In-depth analysis and simulation study of an innovative fuzzy approach for ranking alternatives in multiple attribute decision making problems based on TOPSIS. Appl Soft Comput 11:900–907

    Article  Google Scholar 

  • Chen S-J, Hwang C-L (1992) Fuzzy Multiple Attribute Decision Making. Springer, Berlin

    Book  Google Scholar 

  • Cheng C-H, Lin Y (2002) Evaluating the best main battle tank using fuzzy decision theory with linguistic criteria evaluation. Eur J Oper Res 2002(142):174–186

    Article  Google Scholar 

  • Chiarini A, Vagnoni E (2015) World-class manufacturing by Fiat. Comparison with Toyota production system from a strategic management, management accounting, operations management and performance measurement dimension. Int J Prod Res 53(2):590–606

    Article  Google Scholar 

  • Choi TY, Liker JK (1995) Bringing Japanese continuous improvement approaches to US manufacturing: the roles of process orientation and communications. Decis Sci 26(5):589–616

    Article  Google Scholar 

  • Cua KO, McKone KE, Schroeder RG (2001) Relationships between implementation of TQM, JIT, and TPM and manufacturing performance. J Oper Manag 19(6):675–694

    Article  Google Scholar 

  • Das A, Handfield RB, Calantone RJ, Ghosh S (2000) A contingent view of quality management—the impact of international competition on quality. Decis Sci 31(3):649–690

    Article  Google Scholar 

  • De Felice F, Petrillo A (2015) Optimization of manufacturing system through world class manufacturing. IFAC Papers On-line 48(3):741–746

    Article  Google Scholar 

  • Dow D, Samson D, Ford S (1999) Exploding the myth: do all quality management practices contribute to superior quality performance? Prod Oper Manag 8(1):1–27

    Article  Google Scholar 

  • Eisenhardt KM, Martin JA (2000) “Dynamic Capabilities: what are they? Strateg Manag J 21:1105–1121

    Article  Google Scholar 

  • Epstein MJ, Manzoni J (1997) The balanced scorecard and tableau de board, Translating strategy into action. Manag Account 8:28–36

    Google Scholar 

  • Eraslan E, İç YT (2011) A multi-criteria approach for determination of investment regions: Turkish case. Ind Manag Data Syst 111(6):890–909

    Article  Google Scholar 

  • Ertugrul I, Karakasoglu N (2009) Performance evaluation of Turkish cement firms with fuzzy analytic hierarchy process and TOPSIS methods. Expert Syst Appl 36:702–715

    Article  Google Scholar 

  • Esmaeel RI, Zakuan N, Jamal NM, Taherdoost H (2018) Understanding of business performance from the perspective of manufacturing strategies: fit manufacturing and overall equipment effectiveness. Procedia Manuf 22:998–1006

    Article  Google Scholar 

  • Ezzabadi JH, Saryazdi MD, Mostafaeipour A (2015) Implementing Fuzzy Logic and AHP into the EFQM model for performance improvement: a case study. Appl Soft Comput 36:165–176

    Article  Google Scholar 

  • Fitzgerald L, Johnston R, Brignall TJ, Silvestro R, Voss C (1991) Performance measurement in service businesses. The Chartered Institute of Management Accountants, London

    Google Scholar 

  • Flynn BB, Schroeder RG, Sakakibara S (1995) The impact of quality management practices on performance and competitive advantage. Decis Sci 26(5):659–691

    Article  Google Scholar 

  • Forza C, Flippini R (1998) TQM impact on quality conformance and customer satisfaction: a causal model. Int J Prod Econ 55(1):1–20

    Article  Google Scholar 

  • Fry TD, Cox JF (1989) Manufacturing performance; local versus global measures. Prod Inven Manag J 30(2):52–56

    Google Scholar 

  • Hall RW (1983) Zero inventories. Dow, Jones-Irwin, Homewood

    Google Scholar 

  • Hayes RH, Abernathy WJ (1980) Managing our way to economic decline. Harvard Bus Rev 62:95–101

    Google Scholar 

  • Hayes RH, Garvin DA (1982) Managing as if tomorrow mattered. Harvard Bus Rev 60(3):70–79

    Google Scholar 

  • Hayes RH, Wheelwright SC (1984) Restoring our competitive edge: competing through manufacturing. Wiley, New York

    Google Scholar 

  • Ic YT (2012) Development of a credit limit allocation model for banks using an integrated fuzzy TOPSIS and linear programming. Expert Syst Appl 39(2012):5309–5316

    Google Scholar 

  • Ic YT (2014) A TOPSIS based design of experiment approach to assess company ranking. Appl Math Comput 227:630–647

    MathSciNet  MATH  Google Scholar 

  • Ic YT, Yurdakul M (2010) Development of a quick credibility scoring decision support system using fuzzy TOPSIS. Expert Syst Appl 37:567–574

    Article  Google Scholar 

  • IFAC (1998) International management accounting practice statement: management accounting concepts. International Federation of Accountants, New York

    Google Scholar 

  • Ittner C, Larcker D, Randall T (2003) Performance implications of strategic performance measurement in financial service firms. Acc Organ Soc 28(7/8):715–741

    Article  Google Scholar 

  • Kagioglou M, Cooper R, Aouad G (2001) Performance management in construction: a conceptual framework. Constr Manag Econ 19(85):95

    Google Scholar 

  • Kaplan RS (1983) Measuring performance: a new challenge for managerial accounting research. Acc Rev 18(4):686–705

    Google Scholar 

  • Kaplan RS, Norton DP (1992) The balanced scorecard—measures that drive performance. Harvard Bus Rev 70(1):71–79

    Google Scholar 

  • Kaynak H (2003) The relationship between total quality management practices and their effects on firm performance. J Oper Manag 21(4):405–435

    Article  Google Scholar 

  • Kennerley M, Neely A (2002) A framework of the factors affecting the evolution of performance measurement systems. Int J Oper Prod Manag 22(11):1222–1245

    Article  Google Scholar 

  • Kundu P, Kar S, Maiti M (2014) A fuzzy MCDM method and an application to solid transportation problem with mode preference. Soft Comput 18:1853–1864

    Article  Google Scholar 

  • Lynch RL, Cross KF (1991) Measure up – the essential guide to measuring business performance. Mandarin, London

    Google Scholar 

  • Maksoud A, Dugdale D, Luther R (2005) Non-financial performance measurement in manufacturing companies. Br Acc Rev 37:261–297

    Article  Google Scholar 

  • Matsui Y(2002) An empirical analysis of quality management in Japanese manufacturing companies. In: Proceedings of the seventh annual meeting of the asia-pacific decision sciences institute, APDSI, pp 1–18

  • Melnyk SA, Davis EW, Spekman RE, Sandor J (2010) Outcome-driven supply chains. MIT Sloan Manag Rev 51(2):33–38

    Google Scholar 

  • Moullin M (2003) Defining performance measurement. Perspect Perform 2(2):3

    Google Scholar 

  • Neely A (2002) Business Performance Measurement: Theory and Practice. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Parast MM, Adam SG, Jones EC, Rao SS, Raghu-Nathan TS (2006) Comparing quality management practices between the United States and Mexico. Qual Manag J 13(4):36–49

    Article  Google Scholar 

  • Parkan C, Wu M-L (1999) Decision making and performance measurement models with applications to robot selection. Comput Ind Eng 36:503–523

    Article  Google Scholar 

  • Petrillo A, De Felice F, Zomparelli F (2018) Performance measurement for world-class manufacturing: a model for the Italian automotive industry. Total Qual Manag Bus Excell. https://doi.org/10.1080/14783363.2017.1408402

    Article  Google Scholar 

  • Phan AC, Abdallah AB, Matsui Y (2011) Quality management practices and competitive performance: empirical evidence from Japanese manufacturing companies. Int J Prod Econ 133:518–529

    Article  Google Scholar 

  • Prybutok V, Zhang X, Peak D (2011) Assessing the effectiveness of the Malcolm Baldrige National Quality Award model with municipal government. Socio-Econ Plan Sci 45:118–129

    Article  Google Scholar 

  • Rao M, Chhabria R, Gunasekaran A, Mandal P (2018) Improving competitiveness through performance evaluation using the APC model: a case in micro-irrigation. Int J Prod Econ 195(2018):1–11

    Article  Google Scholar 

  • Rawat GS, Gupta A, Juneja C (2018) Productivity measurement of manufacturing system. Mater Today Proc 5:1483–1489

    Article  Google Scholar 

  • Samson D, Terziovski M (1999) The relationship between total quality management practices and operational performance. J Oper Manag 17(4):393–409

    Article  Google Scholar 

  • Sangwa NR, Sangwan KS (2018) Development of an integrated performance measurement framework for lean organizations. J Manuf Technol Manag 29(1):41–84

    Article  Google Scholar 

  • Sardana D, Terziovski M, Gupta N (2016) The impact of strategic alignment and responsiveness to market on manufacturing firm’s performance. Int J Prod Econ 177:131–138

    Article  Google Scholar 

  • Schroeder RG, Flynn BB (2001) High performance manufacturing: global perspectives. Wiley, New York

    Google Scholar 

  • Skinner W (1974) The decline, fall and renewal of manufacturing. Indus Eng 52(5):32–38

    Google Scholar 

  • Striteska M (2012) Key features of strategic performance management systems in manufacturing companies. Procedia Social Behav Sci 58:1103–1110

    Article  Google Scholar 

  • Taylor A, Taylor M (2013) Antecedents of effective performance measurement system implementation: an empirical study of UK manufacturing firms. Int J Prod Res 51(18):5485–5498

    Article  Google Scholar 

  • Teece DJ (2007) Explicating dynamic capabilities: the nature and micro foundations of (sustainable) enterprise performance. Strateg Manag J 28(13):1319–1350

    Article  Google Scholar 

  • Teece DJ (2009) Dynamic capabilities and strategic management: organizing for innovation and growth. Oxford University Press, Oxford

    Google Scholar 

  • Teece DJ, Pisano G, Shuen A (1997) Dynamic capabilities and strategic management. Strateg Manag J 18(7):509–533

    Article  Google Scholar 

  • Toklu MC, Taskin H (2017) Performance evaluation of small-medium enterprises based on management and organization. Acta Phys Pol A 132(3):994–998

    Article  Google Scholar 

  • Yeo B, Grant D (2017) Exploring the factors affecting global manufacturing performance. Inf Technol Dev. https://doi.org/10.1080/02681102.2017.1315354

    Article  Google Scholar 

  • Yeung ACL, Cheng TCE, Lai KH (2005) An empirical model for managing quality in the electronic industry. Prod Oper Manag 14(2):189–204

    Article  Google Scholar 

  • Yurdakul M, Ic YT (2005) Development of a performance measurement model for manufacturing companies using the AHP and TOPSIS approaches. Int J Prod Res 43(21):4609–4641

    Article  Google Scholar 

  • Yurdakul M, Ic YT (2009a) Analysis of the benefit generated by using fuzzy numbers in a TOPSIS model developed for machine tool selection problems. J Mater Process Technol 209:310–317

    Article  Google Scholar 

  • Yurdakul M, Ic YT (2009b) Application of correlation test to criteria selection for multi criteria decision making (MCDM) models. Int J Adv Manuf Technol 40:403–412

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yusuf Tansel İç.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Additional information

Communicated by A. Genovese, G. Bruno.

Appendices

Appendix 1

Objectives:

  • O1. Improve the products’ technological level and increase value added portions in their prices

  • O2. Improve the manufacturing capability and competitiveness

  • O3. Improve the customers’ profile and increase the percentage of the export revenues

  • O4. Improve the personnel quality

Critical activities:

Activities

The list of statements

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).

$$ \left( {a_{j}^{*} ,b_{j}^{*} ,c_{j}^{*} ,d_{j}^{*} } \right)_{{}}^{{}} = \mathop {\hbox{max} }\limits_{1 \le i \le n} \left( {a_{ij} ,b_{ij} ,c_{ij} ,d_{ij} } \right), j = 1,2, \ldots ,17 $$
(8)

Then, the normalized decision matrix is constructed using Eq. (9) (Chen and Hwang 1992).

$$ \tilde{r}_{ij} = \tilde{x}_{ij} ( \div )\tilde{x}^{*}_{j} = \left( {\frac{{a_{ij} }}{{a_{j}^{*} }},\frac{{b_{ij} }}{{b_{j}^{*} }},\frac{{c_{ij} }}{{c_{j}^{*} }},\frac{{d_{ij} }}{{d_{j}^{*} }}} \right),\quad i = 1,2, \ldots ,n;j = 1,2, \ldots ,17 $$
(9)

Step 2 The normalized decision matrix \( (\tilde{V}) \) is weighted next using Eq. (10).

$$ \tilde{V} = \left[ {\tilde{v}_{ij} } \right]_{nx17} ,\quad i = 1,2, \ldots ,n;j = 1,2, \ldots ,17 $$
(10)

where

$$ \tilde{v}_{ij} = \tilde{r}_{ij} \otimes \tilde{w}_{j} $$
(11)

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.

$$ \mathop v\nolimits_{ij} = \frac{a + b + c + d}{4} $$
(12)

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. (1316).

$$ A* = \left( {v_{1}^{*} ,v_{2}^{*} , \ldots ,v_{j}^{*} } \right) $$
(13)
$$ v_{j}^{*} = \left\{ {(\mathop {best}\limits_{i} X_{ij} );i = 1,2, \ldots ,n} \right\};j = 1,2, \ldots ,17 $$
(14)
$$ A - = \left( {v_{1}^{ - } ,v_{2}^{ - } , \ldots ,v_{j}^{ - } } \right) $$
(15)
$$ v_{j}^{ - } = \left\{ {(\mathop {worst}\limits_{i} X_{ij} ;i = 1,2, \ldots ,n} \right\};j = 1,2, \ldots ,17 $$
(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.

$$ d_{i}^{*} = \sqrt {\sum\limits_{j = 1}^{17} {(v_{ij} } - v_{j}^{*} )^{2} } \quad i = 1,2, \ldots n; $$
(17)
$$ d_{i}^{ - } = \sqrt {\sum\limits_{j = 1}^{17} {(v_{ij} } - v_{j}^{ - } )^{2} } \quad i = 1,2, \ldots n; $$
(18)

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).

$$ C_{i}^{*} = d_{i}^{ - } /(d_{i}^{ - } + d_{i}^{*} );\,0 \le C_{i}^{*} \le 1\,i = \, 1, \, 2, \ldots ,n_{mc} $$
(19)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-018-3449-6

Keywords

Navigation