PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
2017 | 27 | nr 1 | 105--124
Tytuł artykułu

On the Right Approach to Selecting a Quality Improvement Project in Manufacturing Industries

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Continuous improvement is the core of any successful firm. Talking about manufacturing industries, there is huge potential for continuous improvement to be made in various work areas. Such improvement can be made in any section of industry in any form such as quality improvement, waste minimization, system improvement, layout improvement, ergonomics, cost savings, etc. This case study considers an example of a manufacturing firm which wanted to start a quality improvement project (QIP) on its premises. Various products were available, but with dwindling quality levels. However, the real task was the choice of a product for upcoming QIP, as it is well known that success heavily depends upon the selection of a particular project. This is also because of the amount of effort in terms of time, money and manpower that is put into a project nowadays. The authors' objective was to compare three techniques, namely, cost of poor quality (COPQ), conditional probability and fuzzy TOPSIS for selecting the right project based on this specific firm. The pros and cons of these approaches have also been discussed. This study should prove to be instructive for the realization of QIPs in similar types of industry. (original abstract)
Rocznik
Tom
27
Numer
Strony
105--124
Opis fizyczny
Twórcy
autor
  • National Institute of Technology, Kurukshetra, India
  • National Institute of Technology, Kurukshetra, India
  • National Institute of Technology, Kurukshetra, India
Bibliografia
  • [1] HALL N.G., LONG D.Z., QI J., SIM M., Managing underperformance risk in project portfolio selection, Oper. Res., 2015, 63 (3), 660-675.
  • [2] GHORABAEE M.K., AMIRI M., SADAGHIANI J.S., ZAVADSKAS E.K., Multi-criteria project selection using an extended VIKOR method with interval type-2 fuzzy sets, Int. J. Inf. Technol. Dec. Making, 2015, 14 (5), 993-1016.
  • [3] MORTON A., KEISLER J.M., SALO A., Multicriteria Portfolio Decision Analysis for Project Selection, in Multiple Criteria Decision Analysis, Springer, New York 2016, 1269-1298.
  • [4] EDWARDS W., DETLOFF VON W., Decision analysis and behavioral research, Cambridge University Press, 604, 6-8, 1986.
  • [5] THOMAS F.S., Towards an understanding of supply chain quality management, J. Oper. Manage., 2008, 26 (4), 461-467.
  • [6] MITTAL K., KAUSHIK P., A general model for problem solving in manufacturing or service organizations, J. Eng. Technol., 2017, DOI: 10.4103/0976-8580.158566
  • [7] WIBIG T., KARBOWIAK M., JASZCZYK M., A Bayesian model of group decision making, Oper. Res. Dec., 2016, 26 (1), 95-110.
  • [8] HU H., BRADY M., A Bayesian approach to real-time obstacle avoidance for a mobile robot I Fsonar (X), Auton. Robots, 1994, 92 (1), 69-92.
  • [9] TORRES-TOLEDANO J.G., SUCAR L.E., Bayesian Networks for reliability analysis of complex systems, Lect. Notes Comput. Sci., 1998, 1484, 195-206.
  • [10] WEIDL G., MADSEN A.L., ISRAELSON S., Applications of object-oriented Bayesian networks for condition monitoring, root cause analysis and decision support on operation of complex continuous processes, Comput. Chem. Eng., 2005, 29, 1996-2009.
  • [11] MARTIN N.P.D., BISHOP J.D.K., CHOUDHARY R., BOIES A.M., Can UK passenger vehicles be designed to meet 2020 emissions targets? A novel methodology to forecast fuel consumption with uncertainty analysis, Appl. Energy, 2015, 157, 929-939.
  • [12] KEENEY R., Evaluation of proposed storage sites, Oper. Res., 1979, 27, 48-64.
  • [13] CRAWFORD D., HUNTZINGER B., KIRKWOOD C., Multiobjective decision analysis for transmission conductor selection, Manage. Sci., 1978, 24, 1700-1709.
  • [14] DYER J., LORBER H., The multiattribute evaluation of program-planning contractors, Omega, 1982, 10, 673-678.
  • [15] BELL D.E., BROWN E., Bidding for the S.S. Kuniang, Interfaces (Providence), 1984, 14 (2), 17-23.
  • [16] JANSSENS D., WETS G., BRIJS T., VANHOOF K., ARENTZE T., TIMMERMANS H., Integrating Bayesian networks and decision trees in a sequential rule-based transportation model, Eur. J. Oper. Res., 2006, 175 (1), 16-34.
  • [17] KARSAK E.E., KUZGUNKAYA O., A fuzzy multiple objective programming approach for the selection of a flexible manufacturing system, Int. J. Prod. Econ., 2002, 79, 101-111.
  • [18] AL-NAJJAR B., ALSYOUF I., Selecting the most efficient maintenance approach using fuzzy multiple criteria decision making, Int. J. Prod. Econ., 2003, 84, 85-100.
  • [19] KAHRAMAN C., YASIN ATEŞ N., ÇEVIK S., GÜLBAY M., AYÇA ERDOĞAN S., Hierarchical fuzzy TOPSIS model for selection among logistics information technologies, J. Enterp. Inf. Manage., 2007, 20 (2), 143-168.
  • [20] ÖNÜT S., KARA S.S., ISIK E., Long term supplier selection using a combined fuzzy MCDM approach. A case study for a telecommunication company, Expert Syst. Appl., 2009, 36, 3887-3895.
  • [21] RAO R.V., PATEL B.K., A subjective and objective integrated multiple attribute decision making method for material selection, Mater. Des., 2010, 31 (10), 4738-4747.
  • [22] VATS G., VAISH R., Selection of lead-free piezoelectric ceramic selection of lead-free piezoelectric ceramics, Int. J. Appl. Ceram. Technol., 2013, 11 (5) 883-893.
  • [23] RATHI R., KHANDUJA D., SHARMA S.K., Six Sigma project selection using fuzzy TOPSIS decision making approach, Manage. Sci. Lett., 2015, 5, 447-456.
  • [24] MITTAL K., TEWARI P.C., KHANDUJA D., KAUSHIK P., Application of fuzzy TOPSIS MADM approach in ranking and underlining the problems of plywood industry in India, Cogent Eng., 2016, 3, 1-11.
  • [25] ZADEH L., The concept of a linguistic variable and its application to approximate reasoning, Inf. Sci., 1975, 8, 199-249.
  • [26] DENGFENG L., CHUNTIAN C., New similarity measures of intuitionistic fuzzy sets and application to pattern recognitions, Pattern Recognit. Lett., 2002, 23, 221-225.
  • [27] LUCZAK H., NICOLAI H., KEES A., PPC-systems: Re-engineering or replacement? Venus. A fuzzy-decision-tool helps to evaluate outdated production planning and control systems, Prod. Plan. Control, 2010, 9 (5), 448-456.
  • [28] RIBEIRO R.A., Fuzzy multiple attribute decision making: a review and new preference elicitation techniques, Fuzzy Sets Syst., 1996, 78, 155-181.
  • [29] BEVILACQUA M., CIARAPICA F., GIACCHETTA G., A fuzzy-QFD approach to supplier selection, J. Purch. Supply Manage., 2006, 12 (1), 14-27.
  • [30] ZADEH L., Is there a need for fuzzy logic?, Inf. Sci. (NY), 2008, 178 (13), 2751-2779.
  • [31] BELLMAN R., ZADEH L., Decision-making in a fuzzy environment, Manage. Sci., 1970, 17 B, 64-141.
  • [32] XU X., HINDUJA S., Determination of finishing features in 2(1/2) D components, Proc. Inst. Mech. Eng. Part B, J. Eng. Manuf., 1997, 211 (2), 125-142.
  • [33] KAHRAMAN C., BESKESE A., KAYA I., Selection among ERP outsourcing alternatives using a fuzzy multi-criteria decision making methodology, Int. J. Prod. Res., 2010, 48 (2), 547-566.
  • [34] RAO R.V., PATEL B.K., Decision making in the manufacturing environment using an improved PROMETHEE method, Int. J. Prod. Res., 2010, 48 (6), 4665-4682.
  • [35] SAATY T.L., TRAN L.T., On the invalidity of fuzzifying numerical judgments in the analytic hierarchy process, Math. Comput. Model., 2007, 46 (7-8), 962-975.
  • [36] RATHI R., KHANDUJA D., SHARMA S.K., Six Sigma project selection using fuzzy TOPSIS decision making approach, Manage. Sci. Lett., 2015, 5 (5), 447-456.
  • [37] HWANG C.-L., PAIDY S., YOON K., MASUD A., Mathematical programming with multiple objectives. A tutorial, Comput. Oper. Res., 1980, 7, 5-31.
  • [38] KIM Y., CHUNG E.-S., JUN S.-M., KIM S., Prioritizing the best sites for treated wastewater in stream use in an urban watershed using fuzzy TOPSIS, Resour. Conserv. Recycl., 2013, 73, 23-32.
  • [39] YONG D., Plant location selection based on fuzzy TOPSIS, Int. J. Adv. Manuf. Technol., 2006, 28 (7-8), 839-844.
  • [40] WANG Y.-M., ELHAG T., Fuzzy TOPSIS method based on alpha level sets with an application to bridge risk assessment, Expert Sys. Appl., 2006, 31 (2), 309-319.
  • [41] AMIRI M., Project selection for oil-fields development by using the AHP and fuzzy TOPSIS methods, Expert Sys. Appl., 2010, 37 (9), 6218-6224.
  • [42] MAHMOODZADEH S., SHAHRABI J., PARIAZAR M., ZAERI M., Project selection by using fuzzy AHP and TOPSIS technique, Int. J. Soc. Behav. Educ. Econ. Manage. Eng., 2007, 1 (6), 270-275.
  • [43] KWONG C., BAI H., Determining the importance weights for the customer requirements in QFD using a fuzzy AHP with an extent analysis approach, IEEE Trans., 2003, 35, 619-626.
  • [44] LAUKKANEN S., KANGAS A., KANGAS J., Applying voting theory in natural resource management: a case of multiple-criteria group decision support, J. Environ. Manage., 2002, 64, 127-137.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.ekon-element-000171467967

Zgłoszenie zostało wysłane

Zgłoszenie zostało wysłane

Musisz być zalogowany aby pisać komentarze.
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.