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Supplier managed inventory in the OEM supply chain: The impact of relationship types on total costs and cost distribution
Abstract and Figures
We investigate the impact of four variants of supplier managed inventory on total costs and cost distribution in a capital
goods supply chain consisting of a parts supplier who delivers parts to an original equipment manufacturer’s assembly plant.
The four supplier managed inventory variants differ in the components of inventory costs that the supplier has to carry. The
performance of the supplier managed inventory relationships is benchmarked with the situation where the assembly plant manages
the inventories. Interesting managerial insights follow from this comparison.
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... e existing literature studies the coordination of the OEM supply chain by the perspective of operation research. Van et al. [1] examine the impact of four variants of vendormanaged inventory on channel cost and cost allocation in OEM supply chain comprised of a supplier and an OEM. Li et al. [2] explore a direct-sale closed-loop supply chain consisting of an OEM, a remanufacturer, and two advertising agents and analyze the impacts of the parameters of design and advertising on equilibrium and the profits of the parties. ...
... Finally, the chaotic system can be transformed into the stable state by using the parameter control method and variable feedback control method, respectively. Several interesting conclusions are drawn as follows: (1) e equilibrium point of the system is locally asymptotically stable when the value of the time delay τ is less than the critical value τ 0 ; however, if the value of time delay τ is more than the critical value τ 0 , the system loses its stability and undergoes a Neimark-Sacker bifurcation. (2) If the adjustment speed of decision variable of the OEM or the CM increases to some threshold, the system will go into the chaotic state and the entropy of the system will increase. ...
In this paper, we investigate a supply chain consisting of an OEM (i.e., original equipment manufacturer) and a CM (contract manufacturer), in which the OEM decides design effort level and marketing level, and the CM makes decision on product manufacturing effort level. We establish a three-dimensional discrete dynamic model with time delay. Firstly, the sufficient conditions for Neimark-Sacker bifurcation are obtained by using different combinations of decision delay periods as bifurcation parameters, and the effect of the adjustment speeds of decision variables on the system stability and impact of time delay on the system stability are discussed, respectively. Secondly, we perform numerical simulation of this model from the perspective of entropy theory. Finally, we propose two methods to control chaos. Results show that when the time delay or the adjustment speed of decision variable exceed a certain threshold, the system will be led into a chaotic state and the entropy of the system will increase. For alleviating the negative effects of chaotic systems, we introduce control parameters to make efficient control on the chaos. At the critical point, the critical value of the adjustment speed of design effort level (or marketing level) of OEM increases as the adjustment speed of the manufacturing effort level of CM decreases, and vice versa.
... Savasaneril and Erkip (2010) examine a VMI system where the customer requires service level and average inventory level to be as good as those under traditional system. Yao and Dresner (2008), Van Nyen et al. (2009), and Choudhary and Shankar (2015a, 2015b) consider service level requirements or target fill rates as part of VMI agreement in examining the benefits of VMI. ...
... Çetinkaya and Lee (2000),Axsäter (2001),Chaouch (2001),Aviv (2002),Cheung and Lee (2002),Kleywegt et al. (2002),Çetinkaya et al. (2006, 2008),Shah and Goh (2006),Al-Ameri et al. (2008),Gumus et al. (2008),Yao and Dresner (2008),Van Nyen et al. (2009), Bookbinder et al. (2010,Chen et al. (2010),Darwish and Odah (2010),Kiesmüller and Broekmeulen (2010),Mutlu and Çetinkaya (2010),Savasaneril and Erkip (2010),Zhao et al. (2010),Darwish and Goyal (2011),Lee and Ren (2011), Marklund (2011), Ben-Daya et al. (2013,Hariga et al. ( , 2014,Jemai et al. (2013),Chen (2014),Verma et al. (2014),Chakraborty et al. (2015), Shankar (2015a, 2015b) andMateen et al. (2015) Section 3.3:Goyal (1976),Hill (1997Hill ( , 1999,Braglia and Zavanella (2003),Jaruphongsa et al. (2004),Hill and Omar (2006),Archetti et al. (2007),Zhang et al. (2007),Solyalı and Süral (2008),Szmerekovsky and Zhang (2008),Webster and Weng (2008), Wang(2009), Zavanella and Zanoni (2009), Zhao and Cheng (2009), Yang et al. (2010), Wee et al. (2011), Yu et al. (2011, 2012), Zanoni et al. (2012), Braglia et al. (2013, 2014), Hariga and Al-Ahmari (2013), Bazan et al. (2014), Chen (2014), Choudhary et al. (2014), Diabat (2014), Rad et al. (2014), Zanoni et al. (2014), Bazan et al. (2015), Choudhary and Shankar (2015a, 2015b), Darwish et al. (2015), Dem and Singh(2015),Govindan (2015),Lu et al. (2015),Tat et al. (2015) andZanoni and Jaber (2015) Gametheoretic models Sections 3.1 and 3.2:Corbett (2001),Fry et al. (2001),Dong and Xu (2002),Kulp (2002),Gerchak and Khmelnitsky (2003),Choi et al. (2004),Gerchak and Wang (2004),Kraiselburd et al. (2004),Mishra and Raghunathan (2004),Wang et al. (2004),Gerchak et al. (2007),Kim (2008),Nagarajan and Rajagopalan (2008),Wong et al. (2009),Almehdawe and Mantin (2010),Guan and Zhao (2010),Lee and Cho (2014) andLee et al. (2016) Section 3.3:Yao et al. (2007Yao et al. ( , 2010,Bichescu and Fry (2009), Yu et al. (2009a, 2009b, 2009c andYu and Huang (2010) Simulation studies Sections 3.1 and 3.2:,Disney and Towill (2003), and Sari(2007)Section 3.3:Yang et al. (2003),Angulo et al. (2004),White and Censlive (2006),Wilson (2007),Southard and Swenseth (2008), Leung (2009), Chen andWei (2012),Braide et al. (2013),Yu et al. (2013) andCai et al. (2015) ...
Vendor-managed inventory (VMI) is a business practice in which the supplier manages the inventory at the customer's premises and makes replenishment decisions. VMI has been extensively studied over a short period of time since it was successfully implemented in the industry in the late 1980s. In this paper, we classify and review theoretical and empirical studies on VMI. In particular, we provide three classifications of theoretical papers focusing on the contractual agreement between supply chain partners, drivers of the benefits of VMI, and research approaches, while we classify empirical papers based on their research purposes and methodologies. One of our findings is that most theoretical studies examine event-based VMI contracts such as (z, Z)-Type contracts, while the penalties/rewards are based on long-Term performance in many VMI agreements observed in practice. We also find that most theoretical studies focus on cost reductions from VMI and their drivers, while empirical studies report various benefits of VMI such as lower inventory and fewer stockouts. We suggest several future research directions based on our literature review.
... Companies look for ways to improve this performance by integrating their operations across subsequent echelons and separate functions in the supply chain (Lohman et al., 2004). The supply chain management literature reports a number of studies on the benefits that a firm derives from linking performance with suppliers and customers (Carter and Ellram, 1994;Salvador et al., 2001;Rungtusanatham et al., 2003;Brewer and Speh, 2000;Chen et al., 2006;Van Nyen et al., 2009). Despite the importance of logistics performance in the construction industry, empirical data about this performance of this industry is scarce (Costa et al., 2006;Wegelius-Lehtonen, 2001). ...
... Outside the construction industry, these trade-offs have been studied in several supply chains. Examples are the capital goods supply chain (Van Nyen et al., 2009), chemicals (Tsiakis and Papageorgiou, 2008) and the bread industry (Haq and Kannan, 2006). Despite its potential importance, analysis of trade-offs of physical distribution cost patterns has not yet received much attention from either the building sector or the academics. ...
The objective of this study is to provide insights into the trade-offs of physical distribution cost patterns in construction supply chains by modelling and measuring these costs. The model of the physical distribution system consists of the following (cost) elements: inventory, transport, handling, and warehousing. It is applied in the supply chain of insulation materials. It is concluded that the model gives a valid explanation for the major cost trade-offs in the supply chain analysed. By using this model, firms involved in the physical distribution of building materials in construction supply chains may find the optimal distribution strategy.
... By examining the background of the researches, it is clear that the main researches done in the field of design of VMI models was aimed at optimizing the objective function of total costs of supply chain (Kaasgari et al., 2017;Anna, 2016;Arora et al., 2010;Bani-Asadi and Zanjani, 2017;Ben-Daya et al., 2013;Bookbinder et al., 2010;Braglia et al., 2014;Escuin et al., 2017;Gou et al., 2008;Gümüş et al., 2008;Kannan et al., 2013;Kastsian and Mönnigmann, 2011;Kiesmüller and Broekmeulen, 2010;Lee and Ren, 2011;Liao et al., 2011;Mahamani and Rao, 2010;Omar et al., 2010;Pasandideh et al., 2010Pasandideh et al., , 2014Nia et al., 2014;Sadeghi et al., 2013Sadeghi et al., , 2014aSari, 2008;Southard and Swenseth, 2008;Van Nyen et al., 2009;Wang et al., 2008;Wong et al., 2009;Yao et al., 2007;YongQiang et al., 2010;Zhang et al., 2007). In the field of designing an optimal model of VMI system with the goal of finding the optimal level of inventory turnover, no research has been observed. ...
The principle of integrity in designing the vendors managed inventory (VMI) models has made these models more complicated. The implementation of these models requires the use of a lot of data from the manufacturer and the suppliers. Therefore, the application of these models in the industry is difficult to do. In this paper, a new model of VMI is proposed. Our objective is to maximize inventory turnover along with the constraint of lack of shortage of goods in the production lines and compliance with the minimum and maximum constraints of inventory in the warehouse of the producer which can be simpler and more practical than minimizing the total cost of the supply chain. For obtaining an optimal solution, a hybrid algorithm based on genetic algorithm (GA) and particle swarm optimization (PSO) has been proposed in order to gain both proper global and local search abilities. Simulation results and performances metrics indicate that the proposed hybrid algorithm outperforms the genetic algorithm (GA) and particle swarm optimization (PSO) and significantly in maximizing the objective function.
... Furthermore, improvements of operational SCM processes can affect one or more value driver components and hence increase company value. One example is the positive impact of supplier managed inventory on direct cost and inventory levels, which is elaborated in supply chains of original equipment manufacturers (van Nyen et al. 2009). ...
... Furthermore, improvements of operational SCM processes can affect one or more value driver components and hence increase company value. One example is the positive impact of supplier managed inventory on direct cost and inventory levels, which is elaborated in supply chains of original equipment manufacturers (van Nyen et al. 2009). ...
Supply chain management (SCM) plays a crucial role in achieving competitive advantage thereby influencing company value. Nevertheless holistic models that allow for quantifying these value impacts of SCM are missing so far. Efficient approaches to calculate and compare value contributions from supply chain (SC) value drivers are needed. This chapter proposes a model to efficiently calculate and compare value contributions from four SC value drivers that affect the profitability and asset performance: sales, SC cost, working capital and fixed assets. Thereby, financial performance metrics are linked on a strategic level to the operational layer of SCM. Properties and characteristics of the quantitative model, which is based on the discounted cash flow concept, are illustrated by an industrial example. In this context, the importance of acceleration, enhancement and volatility of cash flows for value creation is systematically explored.
... Furthermore, improvements of operational SCM processes can affect one or more value driver components and hence increase company value. One example is the positive impact of supplier managed inventory on direct cost and inventory levels, which is elaborated in supply chains of original equipment manufacturers (van Nyen et al. 2009). ...
Supply chain management (SCM) strives for creating competitive advantage and value for customers by integrating business processes from end users through original suppliers. However, the question of how SCM influences the value of a firm is not fully answered. Various conceptual frameworks that explain the coherence of SCM and company value, comprehended as value-based SCM, are well accepted in scientific research, but quantitative approaches to value-based SCM are found rather seldom. The book contributes to this research gap by proposing quantitative models that allow for assessing influences of SCM on the value of a firm. Opposed to existing models that limit the observation to chosen facets of SCM or selected value drivers, this holistic approach is adequate to
• reflect configurational and operational aspects of SCM,
• cover all phases of the product life cycle,
• financially compare value impacts of profitability-related and asset-related value drivers, and
• assess influences of dynamics and uncertainties on company value.
... Some researchers investigated the impact of specific aspects of VMI/SMI, i.e. the bullwhip effect (Disney and Towill, 2003), transport (Disney, Potter and Gardner, 2003) or in specific supply chains, i.e. VMI in the retail supply chain (Kiesmuller and Broekmeulen, 2010) or both, i.e. batch size impact in a supplier job shop environment in an OEM supply chain (Nyen et al, 2009). ...
... In contrast to SCF, stock keeping has been discussed by researches and practitioners for many years and literature on this topic is multifaceted. Various authors provide an overview on different costs and risks related to stock keeping (La Londe and Lambert, 1977;Nyen et al., 2007;Olayinka, 2010). However, these studies mainly address the material flow and handling. ...
Due to the increasing worldwide competition and the desire to enter new markets, a growing number of manufacturers have to cope with globally dispersed customer and supplier networks. Not only does the globalization cause an increased number and complexity of buyer-supplier relationships, extended lead times, and supplementary inventory buffers, but also higher capital commitments and increased financial risks. However, the allocation of these additional costs and risks along the supply chain is often found to be suboptimal from a supply chain perspective. A collaborative strategy on how to distribute the financial burden and risks along the supply chain optimally would both enhance supply chain stability and reduce the overall supply chain costs. Although various concepts are discussed in literature, an overview on potential benefits and risks to be addressed is so far missing. Hence, the purpose of this paper is to inform upon the key factors to be considered when collaboratively financing inventories, and on how these factors correlate. Identifying the different conditions that enable or restrict the application of supply chain finance in practice, the paper provides valuable insights for both, researchers and practitioners.
Extending the research on the impact of learning effect on inventory management is of particular importance, this paper studies two different inventory management models with considering stochastic learning effect, one is retailer-managed inventory (RMI) scenario, and another is vendor-managed inventory (VMI) scenario. We find that inventory exists in equilibrium provided that the holding cost is under a respective threshold both in the RMI and VMI scenarios, also, the threshold in the RMI scenario is significantly larger than that in the VMI scenario. Moreover, the RMI scenario is Pareto dominant over the VMI scenario except for a very large holding cost, and the advantage in enhancing profit is highlighted in the RMI scenario as the variability of the learning rate increases. Furthermore, the traditional double marginalization effect is weakened by a large variability in the RMI scenario while intensified in the VMI scenario. The results obtained in this paper can provide guidance for the inventory management with considering stochastic learning effect.
This paper describes the Queueing Network Analyzer (QNA), a software package developed at Bell Laboratories to calculate approximate congestion measures for a network of queues. The first version of QNA analyzes open networks of multiserver nodes with the first-come, first-served discipline and no capacity constraints. The first version of QNA uses two parameters to characterize the arrival processes and service times, one to describe the rate and the other to describe the variability. The nodes are then analyzed as standard GI/G/m queues partially characterized by the first two moments of the interarrival-time and service-time distributions. Congestion measures for the network as a whole are obtained by assuming as an approximation that the nodes are stochastically independent given the approximate flow parameters.
This paper considers the performance of a production or distribution-scheduling algorithm termed Automatic Pipeline, Inventory and Order Based Production Control System (APIOBPCS) embedded within a Vendor Managed Inventory (VMI) supply chain where the demand profile is deemed to change significantly over time. A dynamic model of the system using causal loop diagrams and difference equations is presented. The APIOBPCS ordering algorithm is placed within a VMI relationship and a near saturated search technique evaluates optimum solutions based on production adaptation cost, system inventory cost and distributors' inventory costs. The procedure can also cope with supply chains that operate in a localized region (where small, frequent deliveries are possible) or on a global scale, where large batch sizes are needed to gain economies of scale in transport costs. Properties of the optimal systems are highlighted via various Bullwhip, customer service level and inventory cost metrics. Managerial insights are gained and a generic decision support system is presented for ‘tuning’ VMI supply chains. An important feature of the optimization procedure is the ability to generate a number of competing ordering algorithm designs. Final selection of the ‘best’ system is then made via managerial judgement on the basis of the simulated response to typical real-life demands. We finish with a discussion of how the procedure may be used in an industrial context to design and strategically manage VMI supply chains.
A great many manufacturing facilities can be described as closed job shops which process multiple items through multiple work centers for stock or for assembly. The performance of these shops is strongly dependent on the batching policies employed for work in the shop. In particular, waiting time in queue and total manufacturing lead time for batches are functions of lotsizes. In turn these affect work-in-process costs, safety stock requirements, schedule performance and part coordination for assembly. The relationship between lot-sizing and shop performance is represented using a queueing network model which is then embedded in an optimization routine that searches for optimal lot sizes.
This chapter discusses the information pertaining to the downstream part of the supply chain and then reviews the upstream information. The chapter discusses the papers that investigate the consequences of imperfect transmission of information. All the papers adopt the perspective of a central planner whose goal is to optimize the performance of the entire supply chain. The chapter addresses the incentive issues in information sharing. Supply chains are composed of independent firms with private information. Information sharing in supply chains with independent players is tricky. When a player has superior information, two things may happen. He may withhold it to gain strategic advantage or he may reveal it to gain cooperation from others. If the former, the less informed players try to provide incentives for him to reveal his private information, then it is termed “screening.”