مقاله انگلیسی رایگان در مورد سیاست بهینه برای برنامه ریزی تولید و بازاریابی در بازار بی تو بی ( الزویر )
|عنوان مقاله||Optimal dynamic policies for integrated production and marketing planning in business-to-business marketplaces|
|ترجمه عنوان مقاله||سیاست های پویای بهینه برای برنامه ریزی تولید و بازاریابی یکپارچه در بازار های کسب و کار به کسب و کار|
|سال انتشار||مقاله سال ۲۰۱۴|
|تعداد صفحات مقاله||۸ صفحه|
|رشته های مرتبط||مدیریت|
|گرایش های مرتبط||بازاریابی|
|مجله||مجله بین المللی اقتصاد تولید – International Journal of Production Economics|
|دانشگاه||Department of Commerce Automation and Management, Taiwan|
|کلمات کلیدی||ادغام تولید و بازاریابی، کسب و کار به کسب و کار، بازارهای الکترونیکی|
|تعداد کلمات||۴۹۶۵ کلمه|
|لینک مقاله در سایت مرجع||لینک این مقاله در سایت الزویر (ساینس دایرکت) Sciencedirect – Elsevier|
|وضعیت ترجمه مقاله||ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید.|
|دانلود رایگان مقاله||دانلود رایگان مقاله انگلیسی|
|سفارش ترجمه این مقاله||سفارش ترجمه این مقاله|
|بخشی از متن مقاله:|
Information technology (IT) has become increasingly important in enhancing supply chain performance in highly competitive global markets. Electronically enabled supply chains have the potential to improve supply chain performance, including increasing coordination effectiveness and transaction efficiency, by altering the quantity and velocity of information flows among supply chain partners (Chopra and Meindl, 2001; McAfee, 2002; Liu et al., 2005; Sanders, 2008). Swaminathan and Tayur (2003) demonstrated that an important link exists between an IT strategy, business processes, and supply chain performance. Indeed, effective supply chain management (SCM) requires collaboration among business processes and the integration of IT systems. Numerous studies have shown that enterprises applying business processes and IT systems outperform their competitors (SimchiLevi et al., 2008; Qrunfleh and Tarafdar, 2014). In addition to increasing operational efficiency, a highly integrated system can strengthen strategic advantages and generate related benefits (Stefanou, 2001; Mandal and Gunasekaran, 2002; Fin, 2006; Wong et al., 2014).
An important building block in effective supply chain strategies is strategic partnerships between suppliers and buyers. The benefits of inter-organizational collaboration and cooperation between upstream and downstream supply chain entities include reduced costs, reduced capital investment, reduced pooling risk, increased agility and adaptability, improved customer service, enhanced profit margins, and a focus on core competencies (Stank et al., 1999; Lee, 2000; Quinn, 2000; Vickery et al., 2003; Arshinder et al., 2008; Chou and Chang, 2008; Chen and Wei, 2012; Chen, 2013; Sarker, 2013; Wu et al., 2014). One primary coordination task is to streamline business flows of goods and information and decision-making processes among channel partners using vendor-managed inventory (VMI) for a vertically decentralized channel (Disney and Towill, 2003). Strategic partnerships alter the ways in which information is shared and inventory is managed in a supply chain, potentially eliminating the bullwhip effect (Moinzadeh, 2002; Reddy and Rajendran, 2005; Yu et al., 2008, 2010). For example, Milliken & Company works with several clothing suppliers and retailers, all of which agree to use point-of-sale data from department stores to synchronize ordering and manufacturing activities. The lead time from ordering fabric to receipt of finished goods by the department stores was reduced from 18 to 3 weeks (Schonberger, 1996). The most important requirement for an effective supply chain partnership, especially one moving toward the VMI system with consignment-based revenue-sharing contracts, is an advanced information system, including electronic data interchangeand Internet-based exchange, which can decrease data transfer time and number of entry mistakes (Lee et al., 1999; Dejonckheere et al., 2004). This work fills the gap between production and marketing decision-making by developing model-driven optimization-based policies in a vertically decentralized single-manufacturer and singleretailer dynamic channel over a multi-period planning horizon. Moreover, this work develops policies for a VMI system in which consignment with a revenue-sharing contract is applied to solve the problem of optimally dynamic decisions in business-to-business (B2B) traditional markets (TMs) and electronic markets (EMs).
Electronic markets are an increasingly important research topic in the IT domain (Kaplan and Sawhney, 2000). Via advances in technology, Internet-based EMs, which typically have low transaction cost and are easily searched by buyers and sellers, have changed the way in which trade is conducted in a channel (Gunasekaran et al., 2002; Hausen et al., 2006). Notably, EMs reduce procurement costs by a few percent to 40% depending on the industry, on average, approximately 15% (Simchi-Levi et al., 2008). Wang and Benaroch (2004) demonstrated that supplier and buyer decisions about whether to join a B2B EM depend on the revenue structure of the EM owner, and that the optimal replenishment quantity decision with a full return policy under a single-period newsvendor supply chain in EMs can increase the channel efficiency. An EM system is an inter-organizational information system that exists away from the physical location of a TM, serves as an intermediary between buyers and sellers in a vertical market, and allows buyers and sellers to exchange information about prices and goods (Bakos, 1991). The latest ITs enhance interorganizational interactions between production and marketing, such as those in customer relationship management, enterprise resource planning (ERP), decision support systems (DSSs), SCM, and EMs. In recent years, SCM with the growth of ERP has become an important focus of decision support applications, and the difficulty in obtaining the data required to model supply chains has decreased due to ERP (Power and Sharda, 2007). Model-driven optimization-based DSSs are typically applied to such SCM stages as transportation, manufacturing, capacity, demand, replenishment, and pricing. Vigus et al. (2001) reported that the Kellogg Company saved millions of dollars by using model-driven optimization-based systems. Smith et al. (2003) developed a model-driven DSS that maximizes profit for a retail supply chain with multiple vendors. However, information system benefits cannot be fully realized without a finely tuned alignment and reconciliation between system configurations, organizational imperatives, and core business processes (Al-Mashari et al., 2003). Further, the fundamental basis of planning and scheduling in an information system is based on fixed and static settings (Petty et al., 2000; Hsiang, 2001). As a result, the system generates suboptimal solutions to the pricing and lot-size/scheduling problem. Supply chains are dynamic systems that evolve over time; that is, customer demand changes over time, as do supply chain relationships (Simchi-Levi et al., 2008). Numerous studies have implicitly assumed that enterprises cannot control demand, when in fact this is untrue. In a competitive business environment, enterprises have relied heavily on the dynamic pricing scheme to improve their net profits. Owing to its effectiveness in demand-side control over on-hand stock that generates considerable profits, dynamic pricing schemes are increasingly prevalent in retailing and e-commerce merchandising (Boyd and Bilegan, 2003). Furthermore, deterioration affects numerous inventory items, such as fashion goods and high-tech products, which are subject to depletion by phenomena other than demand—such as shrinkage and obsolescence. Deteriorating inventory problems have attracted recently significant research attention.