Portfolio selection of new product projects: a product reliability perspective

• Autorzy: Relich M.

Identyfikatory

DOI

10.17531/ein.2016.4.17

Warianty tytułu

PL

Wybór portfela projektów nowych produktów z uwzględnieniem niezawodności produktu

• Języki publikacji: EN PL

Abstrakty

EN

Portfolio selection of new product development projects is one of the most important decisions in an enterprise that impact future business profits, competitiveness and survival. Ensuring reliability in a new product is costly but it increases customer satisfaction and reduces the potential warranty cost, contributing to product success. This paper aims to develop an approach for designing decision support system of selecting portfolio of new product development projects, taking into account the aspect of ensuring the desired reliability of products. A portfolio selection problem is formulated in terms of a constraint satisfaction problem that is a pertinent framework for designing a knowledge base. A set of admissible solutions referring to the new product alternatives is obtained with the use of constraint logic programming. The proposed approach is dedicated for enterprises that modernise existing products to develop new products.

PL

Wybór portfela projektów nowych produktów jest jedną z najistotniejszych decyzji podejmowanych w przedsiębiorstwie, wpływającą na przyszłą wartość zysków oraz konkurencyjność i rozwój przedsiębiorstwa. Zapewnienie niezawodności produktu jest kosztowne, ale zwiększa satysfakcję klienta z używanego produktu i redukuje koszty potencjalnych napraw gwarancyjnych, przyczyniając się do sukcesu rynkowego produktu. Celem artykułu jest opracowanie podejścia umożliwiającego budowę systemu wspomagania decyzji dotyczących wyboru portfela projektów nowych produktów do rozwinięcia, z uwzględnieniem aspektu zapewnienia wymaganej niezawodności produktu. Problem wyboru portfela projektów nowych produktów został wyrażony w postaci problemu spełniania ograniczeń, co umożliwia zaprojektowanie systemu opartego na bazie wiedzy. Zbiór rozwiązań dopuszczalnych dotyczący alternatywnych projektów rozwoju nowych produktów jest otrzymywany z wykorzystaniem technik programowania w logice z ograniczeniami. Opracowane podejście jest dedykowane dla przedsiębiorstw, które realizują strategię modernizacji wytwarzanego produktu.

Słowa kluczowe

EN

cost estimation; project alternatives; constraint satisfaction problem; constraint logic programming; decision support system

PL

estymacja kosztów; warianty alternatywne projektu; problem spełniania ograniczeń; programowanie w logice z ograniczeniami; system wspomagania decyzji

• Wydawca: Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne
• Czasopismo: Eksploatacja i Niezawodność
• Rocznik: 2016
• Tom: Vol. 18, no. 4
• Strony: 613--620
• Opis fizyczny: Bibliogr. 35 poz., rys., tab.

Twórcy

autor

Relich M.

• Faculty of Economics and Management University of Zielona Góra ul. Licealna 9, 65-417 Zielona Góra, Poland

Bibliografia

• 1. Ahmed H, Chateauneuf A. Optimal number of tests to achieve and validate product reliability. Reliability Engineering and System Safety 2014; 131: 242-250, http://dx.doi.org/10.1016/j.ress.2014.04.014.
• 2. Anderson D M. Design for manufacturability. London: CRC Press, 2014.
• 3. Bach I, Bocewicz G, Banaszak Z, Muszyński W. Knowledge based and CP-driven approach applied to multi product small-size production flow. Control and Cybernetics 2010; 39 (1): 69-95.
• 4. Banaszak Z, Zaremba M, Muszyński W. Constraint programming for project-driven manufacturing. International Journal of Production Economics 2009; 120: 463-475, http://dx.doi.org/10.1016/j.ijpe.2008.12.016.
• 5. Ben-Arieh D, Qian L. Activity-based cost management for design and development stage. International Journal of Production Economics 2003; 83: 169-183, http://dx.doi.org/10.1016/S0925-5273(02)00323-7.
• 6. Bocewicz G. Robustness of multimodal transportation networks. Eksploatacja i Niezawodnosc – Maintenance and Reliability 2014; 16 (2):259-269.
• 7. Booker J D, Rainer M, Swift K G. Designing capable and reliable products. Oxford: Butterworth-Heinemann, 2001.
• 8. Chan S L, Ip W H. A dynamic decision support system to predict the value of customer for new product development. Decision Support Systems 2011; 52: 178-188, http://dx.doi.org/10.1016/j.dss.2011.07.002.
• 9. Chatterjee S, Bandopadhyay S. Reliability estimation using a genetic algorithm-based artificial neural network: an application to a load-hauldump machine. Expert Systems with Applications 2012; 39: 10943-10951, http://dx.doi.org/10.1016/j.eswa.2012.03.030.
• 10. Chin K S, Chan A, Yang J B. Development of a fuzzy FMEA based product design system. International Journal of Advanced Manufacturing Technology 2008; 36: 633-649, http://dx.doi.org/10.1007/s00170-006-0898-3.
• 11. Cooper R, Edgett S. Maximizing productivity in product innovation. Research Technology Management 2008; 51 (2): 47-58.
• 12. Dieter G E. Engineering design: a material and processing approach. Boston: McGraw-Hill, 2000.
• 13. Du X, Jiao J, Tseng M M. Understanding customer satisfaction in product customization. The International Journal of Advanced Manufacturing Technology 2006; 31: 396-406, http://dx.doi.org/10.1007/s00170-005-0177-8.
• 14. Foussier P. From product description to cost: a practical approach. London: Springer, 2006.
• 15. Hvam L, Mortensen N H, Riis J. Product customization. Berlin Heidelberg: Springer, 2008.
• 16. Juran J M, Gryna F M. Quality planning and analysis: from product development through use. Mcgrow-Hill Science, 2000.
• 17. Kłosowski G, Gola A, Świć A. Application of fuzzy logic controller for machine load balancing in discrete manufacturing system. Lecture Notes in Computer Science 2015; 9375: 256-263, http://dx.doi.org/10.1007/978-3-319-24834-9_31.
• 18. Kłosowski G, Gola A, Świć A. Application of fuzzy logic in assigning workers to production tasks. Advances in Intelligent Systems and Computing 2016; 474: 505-513, http://dx.doi.org/10.1007/978-3-319-40162-1_54.
• 19. Kumar S. A knowledge based reliability engineering approach to manage product safety and recalls. Expert Systems with Applications 2014; 41: 5323-5339, http://dx.doi.org/10.1016/j.eswa.2014.03.007.
• 20. Levin M, Kalal T. Improving product reliability: strategies and implementation. Chichester: John Wiley & Sons, 2003, http://dx.doi.org/10.1002/0470014024.
• 21. Lolas S, Olatunbosun O A. Prediction of vehicle reliability performance using artificial neural networks. Expert Systems with Applications 2008; 34: 2360-2369, http://dx.doi.org/10.1016/j.eswa.2007.03.014.
• 22. Murthy D N, Rausand M, Osteras T. Product reliability: specification and performance. London: Springer, 2008.
• 23. Niazi A, Dai J, Balabani S, Seneviratne L. Product cost estimation: technique classification and methodology review. Journal of Manufacturing Science and Engineering 2005; 128: 563-575, http://dx.doi.org/10.1115/1.2137750.
• 24. Niederliński A. A gentle guide to constraint logic programming via ECLiPSe. Gliwice: PKJS, 2011.
• 25. Peng W, Huang H Z, Li Y, Zuo M, Xie M. Life cycle reliability assessment of new product – a Bayesian model updating approach. Reliability Engineering and System Safety 2013; 112: 109-119, http://dx.doi.org/10.1016/j.ress.2012.12.002.
• 26. Relich M. A computational intelligence approach to predicting new product success. Proceedings of the 11th International Conference on Strategic Management and its Support by Information Systems, 2015, 142-150.
• 27. Relich M, Pawlewski P. A multi-agent system for selecting portfolio of new product development project. Communications in Computer and Information Science 2015; 524: 102-114, http://dx.doi.org/10.1007/978-3-319-19033-4_9.
• 28. Relich M. Identifying relationships between eco-innovation and product success. Technology Management for Sustainable Production and Logistics. Berlin Heidelberg: Springer, 2015, 173-192, http://dx.doi.org/10.1007/978-3-642-33935-6_9.
• 29. Rossi F, van Beek P, Walsh T. Handbook of constraint programming. Elsevier, 2006.
• 30. Salazar D, Rocco C M, Galvan B J. Optimization of constrained multiple-objective reliability problems using evolutionary algorithms. Reliability Engineering and System Safety 2006; 91: 1057-1070, http://dx.doi.org/10.1016/j.ress.2005.11.040.
• 31. Trott P. Innovation management and new product development. Essex: Prentice Hall, 2005.
• 32. Ulrich K, Eppinger S. Product design and development. Boston: McGraw-Hill, 2011.
• 33. Xu K, Xie M, Tang L C, Ho S L. Application of neural networks in forecasting engine systems reliability. Applied Soft Computing 2003; 2: 255-268, http://dx.doi.org/10.1016/S1568-4946(02)00059-5.
• 34. Yadav O P, Singh N, Chinnam R B, Goel P S. A fuzzy logic based approach to reliability improvement estimation during product development. Reliability Engineering and System Safety 2003; 80: 63-74, http://dx.doi.org/10.1016/S0951-8320(02)00268-5.
• 35. Zafiropoulos E P, Dialynas E N. Reliability prediction and failure mode effects and criticality analysis (FMECA) of electronic devices using fuzzy logic. International Journal of Quality & Reliability Management 2005; 22: 183-200, http://dx.doi.org/10.1108/026567105105772