قیمت‌گذاری پویا و بهینه‌سازی طول دوره گارانتی در طول چرخه عمر محصول (مطالعه موردی: شرکت صنام الکترونیک)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری مهندسی صنایع دانشگاه تربیت مدرس

2 دانشکده مهندسی صنایع و سیستمها دانشگاه تربیت مدرس

چکیده

گارانتی یکی از مؤلفه‌های مهم در بازاریابی به شمار می‌آید. بدیهی است که پیشنهاد گارانتی با مدت‌زمان طولانی‌تر از طرف تولیدکننده تأثیر مستقیم برافزایش فروش خواهد گذاشت ولی از طرف دیگر، هزینه­های شرکت با افزایش طول دوره گارانتی افزایش می‌یابد. مسئله این تحقیق بیشینه‌سازی سود تولیدکننده است که از اجزای درآمدی و هزینه‌ای تشکیل می‌شود. برخلاف اغلب مقالات در حوزه گارانتی که فقط تصمیمات مربوط به محصولات تحت گارانتی را بررسی می‌کنند، در این تحقیق نقشی که محصولات از گارانتی خارج‌شده بر تصمیمات تولیدکننده می‌تواند داشته باشد نیز در نظر گرفته شده است. بدین منظور، مدل ارائه‌شده قابلیت محاسبه تعداد خرابی‌های محصولات تحت گارانتی و از گارانتی خارج‌شده (بر اساس نرخ خرابی محصولات) در بازه‌های زمانی برنامه‌ریزی تولید قطعات یدکی را دارد. همچنین تقاضای محصول مطابق با چرخه عمر آن، تابعی از زمان، قیمت و طول دوره گارانتی است. قیمت فروش و طول دوره گارانتی به ترتیب اثر مستقیم و عکس بر تقاضای محصول دارند. بدین ترتیب، تصمیم‌گیری توأمان در رابطه باقیمت محصول در بازه‌های زمانی مختلف و طول دوره گارانتی از اهمیت بالایی برخوردار است. برای حل مسئله از الگوریتم بهینه‌سازی ازدحام ذرات و بهینه‌سازی مُلهَم از اپتیک استفاده ‌شده است. به‌منظور بررسی نحوه تأثیر متغیرها بر سودآوری تولیدکننده، مدل پیشنهادی با استفاده از داده‌های شرکت صنام الکترونیک حل ‌شده و مورد تحلیل قرارگرفته است.

کلیدواژه‌ها


عنوان مقاله [English]

Dynamic Pricing and warranty Length optimization in product’s life cycle (A Case study in SANAM Electronic Company)

نویسندگان [English]

  • Mohsen Afsahi 1
  • Ali Hosseinzadeh kashan 2
  • Bakhtiar Ostadi 2
  • Hessamedin Zegordi 2
1 Industrial Engineering- TMU
2 Department of Engineering Management, TMU, Tehran, IRAN
چکیده [English]

Warranty is an important element of new products’ marketing. It is obvious that longer warranty length has direct effect on product’s demand, but it can increase manufacturer's costs. The problem of this paper is maximizing the profit of the manufacturer, which consists of a set of revenue and cost elements. Although, most of researches in warranty area just examine the decisions for under-warranty products, in this research we proposed a model considering the role of both under- and out-of-warranty products on manufacturer’s profit. For this, we propose a model for calculating the number of under-warranty and out-of-warranty failures in each spare part production period. The demand itself is dependent upon the time, sales price, and length of warranty. Sales price and warranty length are, respectively, inversely and directly proportional to the market demand. Therefore, the simultaneous decision about sales price and length of warranty is of considerable significance in order to maximize the profit. We solve the problem with the Particle Swarm Optimization and Optics Inspired Optimization algorithms. Through a systematic analysis and comparison, some interesting and valuable managerial insights are derived which are applicable for companies such as SANAM Electronic Company for which we test our proposed model.

کلیدواژه‌ها [English]

  • warranty
  • dynamic pricing
  • life cycle
  • Particle Swarm Optimization
  • optics-inspired optimization
[1]    Murthy .D, Solem .o, and Roren.t, "product warranty logistics: issues and challenges," European Journal Of Operational Research, vol. 156, 2004, pp. 110-126.
[2]    Murthy .D. N. P, and Blischke .w. R, "strategic warranty management: a life-cycle approach," IEEE Transactions On Engineering Management, vol. 47, 2000, pp. 40-54.
[3]    Khodadad .H. S. H, Osanlou, Moshabaki .A, and Kordnaij .A, "designing customer profitability model for organizations:(case study of electronic industry in iran)," Management Research In Iran (Modares Human Sciences)", vol. 17, 2013, pp. 73-94.
[4]    Murthy .D. N. P, "product warranty and reliability," Annals of Operations Research, vol. 143, 2006, pp. 133-146.
[5]    Murthy .D, and Djamaludin .I, "new product warranty: a literature review," International Journal of Production Economics, vol. 79, 2002, pp. 231-260.
[6]    Vahdani .H, Chukova .S, and Mahlooji ,H. "on optimal replacement-repair policy for multi-state deteriorating products under renewing free replacement warranty", Computers & Mathematics with Applications, vol. 61, 2011, pp. 840-850.
[7]    Manna .D. K, "price-warranty length decision with glickman-berger model", International Journal of Reliability and Safety, vol. 2, 2008, pp. 221-233.
[8]    F. Mansoori, T. Abbasnejad, and h. R. Askarpour, "designing an agile supply chain network in terms of demand dependence on price," Modern Researches in Decision Making, vol. 2, pp. 179-206, 2017.
[9]    Glickman .T. S, and Berger .P. D, "optimal price and protection period decisions for a product under warranty", Management Science, vol. 22, 1976, pp. 1381-1390.
[10] Mitra .A, and Patankar .J. G, "a multi-objective model for warranty estimation", European Journal of Operational Research, vol. 45, 1990, pp. 347-355.
[11] Lin .P.-C, and Shue .l.-Y, "application of optimal control theory to product pricing and warranty with free replacement under the influence of basic lifetime distributions", Computers & Industrial Engineering, vol. 48, 2005, pp. 69-82.
[12] Huang .H.-Z, Liu .z.-J, and Murthy .D, "optimal reliability, warranty and price for new products," IIE Transactions, vol. 39, pp. 819-827, 2007.
[13] Wu .C.-C, Lin .P.-C, and Chou .C.-Y, "determination of price and warranty length for a gamma lifetime distributed product", Journal of Information and Optimization Sciences, vol. 28, 2007, pp. 335-355.
[14] Lin .P.-C., Wang .J, and Chin .S.-S, "dynamic optimisation of price, warranty length and production rate", International Journal of Systems Science, vol. 40, 2009, pp. 411-420.
[15] Kim .B, and Park .S, "optimal pricing, eol (end of life) warranty, and spare parts manufacturing strategy amid product transition", European Journal of Operational Research, vol. 188, 2008, pp. 723-745.
[16] Tsao .Y.-C, Teng .W.-G, Chen .R.-S, and Chou .W.-Y, "pricing and inventory policies for hi-tech products under replacement warranty", International Journal of Systems Science, vol. 45, 2014, pp. 1255-1267.
[17] Yazdian .S. A, Shahanaghi .K, and Makui .A, "joint optimisation of price, warranty and recovery planning in remanufacturing of used products under linear and non-linear demand, return and cost functions" International Journal of Systems Science, vol. 47, 2016, pp. 1155-1175.
[18] Darghouth .M. N, Ait-kadi .D, and Chelbi .A, "joint optimization of design, warranty and price for products sold with maintenance service contracts", Reliability Engineering & System Safety, vol. 165, 2017, pp. 197-208.
[19] Chien .Y.-H, and Chiang .C.-P, "optimal warranty length and selling price to maximize the profit", Advances in Technology Innovation, vol. 2, 2017, pp. 18-21.
[20] Chen .C.-K, Lo .C.-C, and Weng .T.-C, "optimal production run length and warranty period for an imperfect production system under selling price dependent on warranty period," European Journal of Operational Research, vol. 259, pp. 401-412, 2017.
[21] Lei .Y, Liu .Q, and Shum .S, "warranty pricing with consumer learning", European Journal of Operational Research, vol. 263, 2017, pp. 596-610.
[22] Nasrollahi .M, and Asgharizadeh .E, "pro-rata warranty pricing model with risk-averse buyers", Management Research in Iran, vol. 20, 2016, pp. 131-154.
[23] Eberhart .R. C, and Kennedy .J, "a new optimizer using particle swarm theory", in proceedings of the sixth international symposium on micro machine and human science, 1995, pp. 39-43.
[24] Kennedy .J, "particle swarm optimization", in encyclopedia of machine learning, ed: springer, 2011, pp. 760-766.
[25] Poli .R, Kennedy .J, and Blackwell .T, "particle swarm optimization," Swarm Intelligence, vol. 1, 2007, pp. 33-57.
[26] Du .K.-l. and Swamy .m, "particle swarm optimization," in search and optimization by metaheuristics, ed: springer, 2016, pp. 153-173.
[27] Kashan .A. H., "a new metaheuristic for optimization: optics inspired optimization (OIO)," Computers & Operations Research, vol. 55, 2015, pp. 99-125.