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Capacity Choice and Allocation: Strategic Behavior and Supply Chain Performance
Abstract
We consider a simple supply chain in which a single supplier sells to several downstream retailers. The supplier has limited capacity, and retailers are privately informed of their optimal stocking levels. If retailer orders exceed available capacity, the supplier allocates capacity using a publicly known allocation mechanism, a mapping from retailer orders to capacity assignments. We show that a broad class of mechanisms are prone to manipulation: Retailers will order more than they need to gain a more favorable allocation. Another class of mechanisms induces the retailers to order exactly their needs, thereby revealing their private information. However, there does not exist a truth-inducing mechanism that maximizes total retailer profits.
We also consider the supplier's capacity choice. We show that a manipulable mechanism may lead the supplier to choose a higher level of capacity than she would under a truth-inducing mechanism. Nevertheless, her choice will appear excessively restrictive relative to the prevailing distribution of orders. Furthermore, switching to a truth-inducing mechanism can lower profits for the supplier, the supply chain, and even her retailers. Hence, truth-telling is not a universally desirable goal.
... Within this context, we consider three allocation mechanisms that are relevant for integrated distribution systems: proportional, linear and uniform (Cachon and Lariviere, 1999c). These mechanisms reflect different allocation goals that a central planner may have, and are representative of broader classes of mechanisms employed in practice for more complex settings (see discussion in section 3.1). ...
... We next formally define possible allocation mechanisms used by the DC, and evaluate their efficiency in allocating inventory in the special case where retail demand information is common knowledge (i.e., retailers' orders are set equal to their realized demand). We focus on the family of allocation mechanisms for capacity rationing first proposed by Cachon and Lariviere (1999c). ...
... Unlike the case of ordering and allocating a resource under demand uncertainty (e.g., see Cachon and Lariviere (1999c)) where an optimal allocation mechanism needs to be increasing and individually responsive (i.e., a retailer who orders more receives more unless the retailer has been allocated all of capacity), in our setting excluding wastage is a sufficient condition for optimality. Then, in the special case of common knowledge of demand information, all three allocation mechanisms exclude wastage, and therefore are optimal. ...
We study the impact of three well‐known inventory allocation mechanisms, including proportional, linear, and uniform, on the ordering behavior of retailers serviced from a central distribution center. Based on the allocation mechanism, retailers may have an incentive to adjust (either inflate or deflate) their orders to gain a more favorable allocation, a behavior that may reduce allocation efficiency from a system perspective. We find that while all three mechanisms are centrally optimal under common knowledge of local demands, only the uniform allocation incentivizes retailers to set orders truthfully. Consistent with theory, our experimental results show that using proportional or linear allocation results in larger and more frequent order adjustments, with the degree of strategic ordering being largest under the linear mechanism. Across all mechanisms, order adjustments decrease both allocation efficiency and local retail profits. While uniform allocation results in smaller and less frequent adjustments overall, it may not be feasible to implement in more general settings. Hence, we propose and test a new mechanism, tailored uniform, that leverages the uniform principle while overcoming some practical limitations. It provides more flexibility by allowing for differences in the allocated quantities among retailers, while still providing no incentive for order manipulation. The tailored uniform mechanism performs similarly to uniform in terms of order adjustments, and further increases allocation efficiency when retailers have heterogeneous demands.
... In more complex situations that involve "production", for example, a distribution channel where price is endogenous, additional fairness ideals have been explored, such as strict egalitarianism, liberal egalitarianism, libertarianism (Cappelen et al., 2007), and the sequence-aligned ideal (Cui & Mallucci, 2016). However, the reference outcome, or in other words the fairness ideal, in supply chain settings where a common resource (e.g., inventory or capacity) is shared across multiple retailers is poorly understood. 1 Allocation mechanisms commonly used in practice and studied in the literature prioritize retailers differently when there is either inventory shortage or surplus (Cachon & Lariviere, 1999b;Spiliotopoulou et al., 2019). Understanding the drivers of perceived fairness in inventory allocation has direct implications for the design of more "fair" allocation mechanisms. ...
... Next, we consider a disintegrated setting where multiple retailers buy from the same supplier. In this setting, we only focus on the shortage case, where inventory is rationed among retailers when the total demand from the retailers exceeds available supply (Cachon & Lariviere, 1999b). When available supply exceeds total demand, that is, there is surplus at the supplier, the allocation of inventory is not relevant as the retailers can each receive the exact amount they order. ...
... While the topic of capacity allocation is well-studied in the analytical supply chain literature (see, e.g., Cachon & Lariviere, 1999a, 1999bHall & Liu, 2010), behavioral issues in such contexts only recently started being explored. When several retailers compete for limited capacity, a broad class of allocation mechanisms incentivize retailers to order more than needed to secure a higher allocation. ...
We study fairness ideals in distribution systems where inventory is allocated to multiple retailers and there is supply–demand mismatch. In particular, we focus on (a) what is considered fair inventory allocation by retailers (e.g., equal profit, same fill rate, and equal share of supply–demand mismatch?) and (b) how the supply chain context affects fairness perceptions. We consider an integrated supply chain setting where total inventory is allocated at the retail level and retailers may face either shortage or surplus, and a disintegrated supply chain where retailers may face supply scarcity when total demand exceeds available inventory. Our experimental data suggest that subjects, taking on the role of retailers in the same supply chain, are often motivated by fairness considerations: they claim for themselves inventory that is not exactly equal to their needs in more than one‐third of the instances. Across settings, “fair” allocations depend on retail demands rather than on profit comparisons, even when these are facilitated by a decision support tool. However, in cases of surplus, the most prevalent fairness ideal is that of equal split of inventory–demand mismatch, while in cases of shortage, the most prevalent fairness ideal is that of equal fill rates. Follow‐up experiments suggest that retailers under both cases of shortage and surplus are more likely to evaluate an allocation as fair when it is based on realized demands, and this is independent of whether it was determined by a rule or a human decision maker.
... Several studies have proposed various policies to either equitably allocate capacity/stock to buyers, or to minimize/maximize the penalty/award associated with the Service Level Agreement (SLA) between product/service provider and clients (e.g., Cachon and Lariviere 1999b;Benjaafar et al. 2007;Chen et al. 2013;Qing et al. 2017;Chen and Thomas 2018). However, limited number of studies could model the strategic reasoning of players in capacity allocation games (e.g., Cachon and Lariviere 1999c;Liu and Ryzin 2011;Cui and Zhang 2018). ...
... A basic application of the capacity allocation problem can be observed in a supply chain in which a single supplier sells a product to several downstream buyers (Cachon and Lariviere 1999c). The capacity of supplier is limited and the information regarding the buyers' optimal stocking levels is private for each buyer. ...
From a common point of view, quantum mechanics, psychology, and decision science disciplines try to predict how unruly systems (atomic particles, human behaviors, and decision makers’ choices) might behave in the future. Effective predicting outcome of a capacity allocation game under various allocation policies requires a profound understanding as how strategic reasoning of decision makers contributes to the financial gain of players. A quantum game framework is employed in the current study to investigate how performance of allocation policies is affected when buyers strategize over order quantities. The results show that the degree of being manipulative for allocation mechanisms is not identical and adopting adaptive quantum method is the most effective approach to secure the highest fill rate and profit when it is practiced under a reasonable range of entanglement levels.
... Allocation is one of the widely used mechanisms to improve the efficiency of a supply chain when the demand from the buyers exceeds the limited capacity of the supplier [5]. For many firms, it is quite difficult to increase the capacity without spending significant amount of cost and time in industries such as steel, semi-conductor, airline, etc. ...
... To scrutinize the nature of the problem, many studies in supply chain management simplify the problem as having one supplier and two buyers. Cachon and Lariviere [5] studied three capacity allocation methods: linear, proportional, and uniform, and found conditions where Nash Equilibrium can be formed. Chen et al. [9] studied two allocation mechanisms: proportional and lexicographic. ...
In a demand driven market, optimal allocation of capacity to the demand has been one of the major issues. In this paper, we consider a single global freight firm allocating its capacity to its own regional sales offices. The firm sells cargo space based on two types of contracts: long-term and spot sales. Regional sales offices utilize their effort to generate more demand in their designated region. In other words, it is assumed that the demand is dependent on their efforts. First, we find a closed-form solution for the optimal level of the efforts of a single sales office in a specific region. Then, we study the case when the firm allocates its limited total capacity to two sales offices. We investigate different methods of capacity allocation: decentralization, centralization, and mixed, by conducting numerical studies. Different from the traditional finding, we suggest that the decentralization method is not always dominated by the centralization method.
... 1 When inventory is limited, and there is no price mechanism to balance supply and demand in the short term, rationing is required. This is especially common in industries such as consumer electronics, automotive, and fashion, where fixed capacity is not easily expanded, lead times are long, and/or demand is highly uncertain (e.g., in case of new product offerings) ( Cachon & Lariviere, 1999a;1999b;Caro & Gallien, 2010;Lu & Lariviere, 2012 ). Additionally, with the growth of online sales and the corresponding adoption of so called multi-channel strategies where sales take place both through offline channels (physical stores) and online channels (web shops and social media) ( Melacini, Perotti, Rasini, & Tappia, 2018;Verhoef, Kannan, & Inman, 2015 ), retailers are faced with the decision of how to allocate inventory among their multiple distribution channels. ...
... The literature on inventory allocation has mainly taken an analytical approach to studying allocation decisions. Some studies focus on the effect of inventory (or capacity) allocation mechanisms, e.g., the proportional, uniform and "turn-and-earn" allocation rules, on orders placed by stores, on allocation efficiency and on overall performance of the supply chain ( Cachon & Lariviere, 1999a;1999b;1999c;Lu & Lariviere, 2012 ). Others propose methods for allocating limited capacity to maximize expected profits, for example when customers remember past service ( Adelman & Mersereau, 2013 ). ...
When demand exceeds available inventory, suppliers ration their inventory among customers (e.g., retail channels). While there are quantitative methods to facilitate these choices, in practice, humans play an important role in making final decisions that affect allocation efficiency. How do inventory risk and contractual differences (i.e. payment schemes) behaviorally affect allocation decisions when there is scarcity? We study inventory allocations between two retail channels: a high value channel with risk, and a low value channel without risk. The retail channels may also differ in terms of the timing and type of payments that take place. We develop theoretical predictions from behavioral models based on risk aversion, loss aversion, and mental accounting and test these through incentivized controlled laboratory experiments. When profit differences between channels are medium to large, subjects allocate significantly less inventory than the expected–profit–maximizing quantity to the risky, yet more profitable, channel and subjects with stronger risk appetite allocate larger quantities to the risky channel. More interestingly, risk appetite moderates the effect of the timing and type of payments on allocations in these settings. When profit differences between channels are small, the effect of risk appetite depends on the timing of payments. Overall, the possibility of experiencing negative payments (e.g., through buy–backs) reduces allocated quantities across settings. Our insights can inform planner assignment to tasks but also the design of support systems that provide information to planners who make allocation decisions.
... Despite exchange of ADI, supply shortage situations are common in industry for a number of reasons. Firstly, in many industries capacity expansion is costly and/or time consuming (Cachon and Lariviere 1999b), such as in the semiconductor industry (e.g. Seitz et al. substituting with a product from an alternative supplier. ...
... In a supply shortage, the supplier has to decide how the limited supply is allocated to the ADI communicated by the different customers. Hence, industry also calls this situation 'going on allocation' (see also Cachon and Lariviere 1999b). Based on the allocations, the supplier subsequently has to decide whether an incoming order from a customer with a long order lead time should be accepted or if the order should be (partially) declined to reserve supply for other customers. ...
When demands exceed capacities, suppliers allocate available supply to customers based on customer importance and advance demand information. The accuracy of advance demand information interacts with the length of customer order lead times and influences overall customer service levels. In this paper, we analyse industrial contract portfolios with customer-specific terms in order to derive insights for contract portfolio management and the design of demand fulfilment processes. For this purpose, we develop a framework for analysis of contract portfolios capturing the dynamics of industrial planning processes. The framework is applied to portfolios from the semiconductor sector. Our numerical analysis shows that, in order to improve service levels, demand fulfilment processes and contract portfolio management must especially take into account the length of order lead times and the accuracy of advance demand information. Even though suppliers often prefer long order lead times, our analysis shows that demand fulfilment performance is not primarily determined by the absolute length of the order lead times but by the presence of a negative correlation with the accuracy of advance demand information in the entire contract portfolio. Consequently, these factors require increased attention in the management of contract portfolios and in the negotiation of individual contracts.
... In the supply chain management domain, the literature focuses on supply chain contracts and coordination issues (Akan, et al., 2011;Islam & Olsen, 2014), mainly between the wholesalers (or suppliers) and retailers to achieve sustained economic performance. There are many types of coordination, such as revenue sharing contracts (Cachon & Lariviere, 2005), information sharing (Chen, 2003;Drake & Schlachter, 2008), and allocation rules (Cachon & Lariviere, 1999). To be more specific, collaboration at the operational level in the shipper-carrier context has become increasingly critical to supply chain coordination (Islam & Olsen, 2014) due to driver shortages and fluctuating fuel prices (Fugate, et al., 2009). ...
Despite the indispensability of freight trucking services and truckers’ role as critical stakeholders in supply chains, relatively little attention has been paid to analyzing small independent truckers’ roles. Shippers often prefer working with larger trucking companies to the detriment of smaller independent truckers, who must grapple with an inherently disadvantageous job market. Furthermore, in the presence of uncertainty or peak demand periods, trucker shortages can pose significant economic challenges for shippers and downstream customers. In this paper, we propose an analytical framework to address these challenges in efforts to enhance the sustainability of the freight service industry. We formulate and solve a weighted bi-objective optimization model that simultaneously maximizes the total profits of both shippers and truckers to design a sustainable freight services market. Further, we leverage Monte Carlo simulation trials to examine how all players in this market can achieve a better solution under uncertainty. Ultimately, after evaluating multiple scenarios, we find that shippers and truckers yield the highest economic benefits under a balanced design that leverages principles of supply chain coordination, while satisfying all demand from shippers. This framework can serve as a decision support tool for policymakers who aim to ensure all stakeholders in the market can become and remain profitable. Based on our findings, this study suggests practical implications on how to consider humanitarian policies aimed at promoting equity for truckers and ensuring the timely shipment of essential products for both shippers and truckers.
... Authors of Literature Prediction of demand , Mahajan et al. (1990), Chaharsooghi et al. (2008), Hosoda and Disney (2004) Grouping of orders Warburton and Disney (2007), Cachon and Lariviere (1999), Cachon and Fisher (2000), Holland and Sodhi (2004), Pujawan (2004) , Chandra and Grabis (2005) ...
The phenomenon of the bullwhip effect (BWE) has become a pressing concern in contemporary supply chain management. Every echelon of the supply chain faces the negative consequences of BWE somehow. So, it is crucial to determine the reasons responsible for the BWE to mitigate the consequences. The boutique industry in Bangladesh is a rapidly growing industrial sector. In this study, we focused on finding the reasons and consequences of the bullwhip effect on the boutique industry in Dhaka city. The main targets of this study are to examine the underlying reasons for the BWE, identify the most significant causes from the perspective of Dhaka city, and determine the major consequences of the bullwhip effect. Studies of previous literature and consultation with experts have identified sixteen common causes behind the bullwhip effect. This study uses a survey-based method; respondents are chosen through clustered sampling. Necessary data have been collected with a semi-organized inquiry form. Among all the 16 causes, six causes are found to be the most significant causes from the perspective of retailers and wholesalers. SPSS Version 26 has been used for statistical analysis to make the final decision. We also found ten consequences commonly faced by these two echelons of the boutiques' supply chain because of the bullwhip effect. These are high inventory costs, workforce wastages and higher labor costs, higher replenishment lead-time, higher transportation costs, tension in the buyer-supplier relationship, product unavailability, loss of profit, poor customer service, etc.
... A large body of literature supports the notion of data-sharing in the context of supply chain disruptions (please see (Duong and Chong, 2020) for a recent review). While original studies on supply chain data sharing date back to bullwhip effect (Lee et al., 1997) and shortage gaming problems (Cachon and Lariviere, 1999), the premise of data sharing has recently been highlighted as an important activity in building supply chain resilience (Pettit et al., 2019). ...
The development and use of Artificial Intelligence technology for predicting supply chain risk has gained popularity. However, proposed approaches are based on the premise that organisations act alone, rather than as a collective when predicting risk, despite the interconnected nature of supply chains. This yields a problem: organisations that have inadequate datasets cannot predict risk. While data-sharing has long been proposed to help coordinate risk evaluation, in practice this does not happen due to privacy concerns. In this paper we propose a novel technique from the field of AI, namely federated learning, to facilitate collective risk prediction in supply chains without the risk of data leakage. We ask: Can organisations who have inadequate datasets tap into collective knowledge? This consequently raises the secondary question: Under what circumstances would collective risk prediction be beneficial? Our approach is tested on empirical case study to help buyers predict order delays from their shared suppliers before and after Covid19. Results show that federated learning can indeed help supply chain members predict risk effectively, especially for buyers who have limited datasets. We also find that training data imbalance, disruption levels, and algorithm choice are significant factors in the efficacy of this approach. Interestingly, data-sharing or collective risk prediction is not always the best choice for buyers who have disproportionately larger order books and they should pursue prediction alone. We thus call for further research on the trade offs between risk prediction with local and collective learning paradigms in supply chains.
... The focus of that literature is on coordinating supply chain decisions in a single supply chain via contracts that incentivize information sharing. Important early contributions that consider supply chain coordination with contracts are the papers of Whang (1995), Cachon and Lariviere (1999), and Tsay, Nahmias, and Agrawal (1999); a good survey of these and related contributions appear in Cachon (2003) and Chen (2003). This literature is mainly concerned with information and risk sharing between one or more vendors (suppliers) and a single manufacturer. ...
We construct an economic framework for understanding the incentives of the participants of a permissioned blockchain for supply chains and other related industries. Our study aims to determine whether adoption of blockchain is socially beneficial and whether such adoption arises in equilibrium. We find that blockchain reduces information asymmetry for consumers, thereby enhancing consumer welfare. Consumer welfare gains can be sufficiently large that blockchain adoption is socially beneficial; nonetheless, we find that blockchain adoption does not arise in equilibrium. This situation arises because blockchain adoption costs are borne by manufacturers, and manufacturers cannot extract consumer gains through prices due to the competitive nature of the manufacturing sector. We offer a system of transfers to generate blockchain adoption in equilibrium when it is socially beneficial.
This paper was accepted by Vishal Gaur, operations management.
Funding: This research was partially supported by a seed grant from the Columbia–IBM Center for Blockchain and Data Transparency.
Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2022.4532 .
... Ehlers and Klaus [2003] extend this characterization to randomized allocation mechanisms. Cachon and Lariviere [1999] consider the uniform allocation rule in a setting where groups have decreasing marginal returns from additional items. ...
We study a setting where tickets for an experience are allocated by lottery. Each agent belongs to a group, and a group is successful if and only if its members receive enough tickets for everyone. A lottery is efficient if it maximizes the number of agents in successful groups, and fair if it gives every group the same chance of success. We study the efficiency and fairness of existing approaches, and propose practical alternatives. If agents must identify the members of their group, a natural solution is the Group Lottery, which orders groups uniformly at random and processes them sequentially. We provide tight bounds on the inefficiency and unfairness of this mechanism, and describe modifications that obtain a fairer allocation. If agents may request multiple tickets without identifying members of their group, the most common mechanism is the Individual Lottery, which orders agents uniformly at random and awards each their request until no tickets remain. Because each member of a group may apply for (and win) tickets, this approach can yield arbitrarily unfair and inefficient outcomes. As an alternative, we propose the Weighted Individual Lottery, in which the processing order is biased against agents with large requests. Although it is still possible to have multiple winners in a group, this simple modification makes this event much less likely. As a result, the Weighted Individual Lottery is approximately fair and approximately efficient, and similar to the Group Lottery when there are many more agents than tickets.
... In particular, this condition applies to rice which by far is one of the four strategic commodities. Typical risks occur in every supply chain management, including interruptions and delays caused by suppliers, such as supply capacity (Bollapragada et al., 2004;Cachon & Lariviere, 1999;Ellram, 1990;Feng, 2010;Kahraman et al., 2003;Wu et al., 2008), product design changes (Novak & Eppinger, 2001) and delivery delays (Feng, 2010). ...
Rice is the main consumption food for Indonesians. The demand for food increased from 114.6 kg per capita in 2016 to 124.89 kg in 2017. However, rice farmers and supply chain actors in rice agribusiness have experienced high challenges, such as production, transportation, price, product quality, and the environment. This research aimed to understand actors involved in the supply chain, their perception of occurring risks, and evaluation and risk mitigation in the supply chain. This was a quantitative descriptive study done purposively in Watugede Village, Singosari Sub-District, Malang Regency. Non-probability sampling was taken to gather primary data. The respondent of this research was 16 involved actors, from on-farm actors to consumers. The data were analyzed using the Fuzzy analytical hierarchy process (FAHP) to provide descriptive risk mitigation strategies. The results show that six involved actors are suppliers, farmers, grinders, traders, and buyers. Each actor faces different risks, and thus, the recommended mitigation strategies are adjusted to their risks. Sharing information, optimizing the level of supply availability, measuring supply chain performance, and building more coordination with the government are the best strategies to mitigate risks.
... The rise in computer power enabled algorithm-based optimization approaches to address problems encountered in supply-chain management (Aviv and Federgruen 1998). This sparked the sharing of information between the firms and the integration of technology, thus creating new administrative approaches such as EDI, VMI, and CPFR (Cachon and Lariviere 1999;Kleindorfer and Wasselhove 2003). ...
... e contract requires the retailers to have a minimum order quantity every time or within a certain period, and they can choose not to order [52,53]. e researches on the minimum order quantity contract can be divided into two categories. ...
We develop a game model for a supply chain consisting of one e-commerce platform, one supplier from other channels, and one retailer. The platform has a well-known brand that can influence consumers’ purchase decisions, and it provides good-quality products with high prices, while supplier from other channels provides cheaper products but possibly with low quality, and there may even be some serious quality problems, sometimes leading to serious problems such as “free-riding” behavior by the retailer and reducing the profits of the supply chain members. First, we study the decisions of platform and retailer under centralized decision (CD) scenario, decentralized decision (DD) scenario, cost sharing contract (CS) scenario, and minimum order quantity contract (QC) scenario. Second, we found that channel conflicts have a negative impact on supply chain members under DD scenario; however, CS and QC scenarios can make the optimal empowerment level of platform the same as CD scenario and encourage retailer to order more products from platform. Finally, the improvement effect in QC and CS scenarios is affected by the substitutability of the two products, the coefficient of empowerment cost, and the reaction coefficient of product price on goodwill. Furthermore, we found that under QC scenario, only within an appropriate range can the platform and the retailer achieve a win-win situation.
At the onset of the coronavirus (COVID-19) pandemic, hospitals experienced a demand surge for COVID-19–related medical care while simultaneously struggling to source prescription drugs needed to manage COVID-19 patients. This is worrisome as shortfalls in the supply of essential drugs can negatively impact patient outcomes. The popular press reporting on these challenges suggests that they are caused by supply chain disruptions. However, rigorous research on the relationship between the pandemic and prescription drug supply is limited. To address this gap, we leverage a quasi-experimental design and IQVIA’s National Sales Perspectives™ data from 2018 to 2020. We focus on prescription drugs indicated for the management of COVID-19 patients and a set of control drugs (i.e., drugs not used for COVID-19). We find that in the early phases of the pandemic, hospitals stockpiled prescription drugs indicated for the management of COVID-19, making this behavior an under-recognized cause of the sourcing challenges. The sales volume of drugs indicated for COVID-19 management was concentrated in the first two months of the pandemic, after which sales decreased significantly despite a nationwide increase in COVID-19–related hospitalizations. We investigate another potential cause of stockpiling: expected price increases. Counter to concerns of price gouging, we find little evidence of price inflation for these drugs. An implication for drug manufacturers is that orders due to stockpiling by downstream buyers early on in a pandemic may need to be discounted when predicting future demand. Our results have implications for drug suppliers, hospitals, and policymakers interested in improving medical product supply chain resilience.
This paper was accepted by Stefan Scholtes, healthcare management.
Funding: R. M. Conti was supported by the National Science Foundation (Grant 1123649-463157) and the Sloan Foundation.
Supplemental Material: The online appendices and data files are available at https://doi.org/10.1287/mnsc.2021.04150 .
Companies that are investing in blockchain technology to enhance supply chain transparency face challenges in fostering collaborations with others and deciding what information to share. Transparency over the actions of supply chain partners can improve operational decisions, but sharing own data on the blockchain can put firms at a competitive disadvantage. In this paper, we investigate the resulting questions of when blockchain should be adopted in a supply chain and how it should be designed by analyzing two ways that it can enhance supply chain transparency: making the manufacturer’s sourcing cost transparent to the buyers (i.e., vertical cost transparency) and making the ordering status of buyers transparent to each other (i.e., horizontal order transparency). Given such transparency, firms can design a smart contract that automates transactions contingent on the revealed information and enables them to realize better equilibrium outcomes. We find that blockchain increases supply chain profit only when the manufacturer’s capacity is large and decreases supply chain profit otherwise. If the capacity is sufficiently large to eliminate the buyers’ competition, blockchain leads to a win–win–win and the incentives of all participants are naturally aligned. If the capacity is only moderately large, the manufacturer needs to compensate the buyers to facilitate a blockchain implementation. However, if the capacity is small, horizontal order transparency enabled by the blockchain mitigates the buyers’ overorder incentive to compete for the manufacturer’s capacity and increases double marginalization. For such cases, we show that a blockchain that only enables vertical cost transparency should (and can) still be adopted in a range of small capacity cases, and we propose an access control layer for the logistics data to implement such a blockchain.
This paper was accepted by David Simchi-Levi, operations management.
Funding: J. Liu was supported by the National Natural Science Foundation of China [Grant 72101110] and The MOE (Ministry of Education in China) Project of Humanities and Social Sciences [Grant 20YJC630084].
Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2023.4851 .
The supply chain is a network of organizations that collaborate and leverage their resources to deliver products or services to end-customers. In today's globalized and competitive market, organizations must specialize and form partnerships to gain a competitive edge. To thrive in their respective industries, organizations need to prioritize supply chain coordination, as it is integral to their business processes. Supply chain management focuses on the collaboration of organizations within the supply chain. However, when each echelon member optimizes their goals without considering the network's impact, it leads to suboptimal performance and inefficiencies. This phenomenon is known as the Bullwhip effect, where order variability increases as it moves upstream in the supply chain. The lack of coordination, unincorporated material and information flows, and absence of ordering rules contribute to poor supply chain dynamics. To improve supply chain performance, it is crucial to align organizational activities. Previous research has proposed solutions to mitigate the Bullwhip effect, which has been a topic of intense study for many decades. This research aims to investigate the causes and mitigations of the Bullwhip effect based on existing research. Additionally, the paper utilizes ARENA simulation to examine the impact of sharing end-customer demand information. As far as we are aware, no study has been conducted to deeply simulate the bullwhip effect using the ARENA simulation. Previous studies have investigated this phenomenon, but without delving into its intricacies. The simulation results offer potential strategies to mitigate the Bullwhip effect through demand information sharing.
This article investigates the viability of using forced allocation as a mechanism to alleviate capacity challenges through scenarios of varying allowable citrus throughput at the Port of Durban. Rule-based resource allocation techniques are used to divide the allowable citrus throughput at the constrained port between production regions by setting quotas. The allocation model framework minimises the impact of the forced allocation on the citrus export cold chain by setting the quotas as constraints in an integer programming (IP) formulation of a minimum cost transportation problem (TP) for the system. The results show that forced allocation is feasible under at least one resource allocation technique for four of the scenarios tested.
Problem definition: With the rapid growth of the gig economy, on-demand staffing platforms have emerged to help companies manage their temporary workforce. This emerging business-to-business context motivates us to study a new form of supply chain coordination problem. We consider a staffing platform managing an on-demand workforce to serve multiple firms facing stochastic labor demand. Before demand realization, each individual firm can hire permanent employees, whereas the platform determines a compensation rate for potential on-demand workers. After knowing the realized demand, firms in need can request on-demand workers from the platform, and then, the platform operator allocates the available on-demand workforce among the firms. We explore how to maximize and distribute the benefits of an on-demand workforce through coordinating self-interested parties in the staffing system. Methodology/results: We combine game theory and online optimization techniques to address the challenges in incentivizing and coordinating the online workforce. We propose a novel and easily implementable fill rate-based allocation and coordination mechanism that enables the on-demand workforce to be shared optimally when individual firms and the platform operator make decisions in their own interest. We also show that the proposed mechanism can be adapted to the cases when contract terms need to be identical to all firms and when actual demand is unverifiable. Managerial implications: The proposed contract mechanism is in line with the performance-based contracting commonly used in on-demand staffing services. Our results suggest that under an appropriately designed performance-based mechanism, individual firms and the platform operator can share the maximum benefits of on-demand staffing.
Funding: This work was supported by the National Natural Science Foundation of China [Grant 71871097] and the Fundamental Research Funds for the Central Universities, China.
Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0327 .
Problem definition: We study a repeated interaction between a manufacturer and a retailer, where the retailer may share with the manufacturer past sales information. In our model, such information cannot improve the latter’s predictive capabilities of future demand, but it does allow him to infer past demand. Academic/practical relevance: Our main research questions are under what conditions the retailer and the manufacturer benefit from sharing such past sales information and how dynamic interaction and past sales information affect the efficiency of the distribution channel. Methodology: We model a repeated relationship between a manufacturer and a retailer, where demand fluctuates in an independent and identically distributed manner between periods. In each period, the retailer privately observes the current demand, and the manufacturer offers a menu of contracts to elicit the retailer to reveal its private information. The manufacturer may observe sales information that reveals past demand at the end of each period if the retailer chooses to share such information. Results: We find that even without sharing sales information, repeated interaction by itself enhances efficiency and profits for both firms. Past sales information further improves the channels’ efficiency and increases the manufacturer’s expected profit. Yet, past sales information increases (decreases) the retailer’s per-period expected profit when the retailer places a low (high) value on its future profits. Managerial implications: Our results provide a new strategic reasoning for sharing past sales information—as a way to increase trust in repeated vertical relationships. Furthermore, when the retailer can share a noisy signal regarding past demand, this may facilitate the exchange of sales information. We also consider the case of a financially constrained retailer and demonstrate that financial constraints may benefit the retailer as they limit the market power of the manufacturer. In contrast, the manufacturer and the channel’s efficiency are always worse off when the retailer is financially constrained.
Funding: The authors acknowledge financial support from the Coller Foundation, the Eli Hurvitz Institute, and the Henry Crown Institute.
Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.1208 .
We study the allocative challenges that governmental and nonprofit organizations face when tasked with equitable and efficient rationing of a social good among agents whose needs (demands) realize sequentially and are possibly correlated. As one example, early in the COVID-19 pandemic, the Federal Emergency Management Agency faced overwhelming, temporally scattered, a priori uncertain, and correlated demands for medical supplies from different states. In such contexts, social planners aim to maximize the minimum fill rate across sequentially arriving agents, where each agent’s fill rate (i.e., its fraction of satisfied demand) is determined by an irrevocable, one-time allocation. For an arbitrarily correlated sequence of demands, we establish upper bounds on the expected minimum fill rate (ex post fairness) and the minimum expected fill rate (ex ante fairness) achievable by any policy. Our upper bounds are parameterized by the number of agents and the expected demand-to-supply ratio, yet we design a simple adaptive policy called projected proportional allocation (PPA) that simultaneously achieves matching lower bounds for both objectives (ex post and ex ante fairness) for any set of parameters. Our PPA policy is transparent and easy to implement, as it does not rely on distributional information beyond the first conditional moments. Despite its simplicity, we demonstrate that the PPA policy provides significant improvement over the canonical class of nonadaptive target-fill-rate policies. We complement our theoretical developments with a numerical study motivated by the rationing of COVID-19 medical supplies based on a standard compartmental modeling approach that is commonly used to forecast pandemic trajectories. In such a setting, our PPA policy significantly outperforms its theoretical guarantee and the optimal target-fill-rate policy.
This paper was accepted by Omar Besbes, revenue management and market analytics.
Supplemental Material: The data files and online appendices are available at https://doi.org/10.1287/mnsc.2023.4700 .
We consider a manufacturer that has a capacity constraint and allocates capacity according to the lexicographic mechanism, which involves assigning different priority levels to different retailers. The manufacturer sells products to two competing retailers: a high-priority one and a low-priority one. The manufacturer's capacity information is either public or private, which makes our paper the first to examine the impact of capacity information on the capacity allocation problem of a manufacturer. We investigate two contract types: wholesale-price and wholesale-price-and-quantity. Our results show that when capacity information is public, the manufacturer will always prefer a wholesale-price contract. Moreover, it can benefit from a lower capacity limit (capacity scarcity) due to the retailer's capacity-withholding behavior. Interestingly, the high-priority retailer may prefer that the manufacturer use a wholesale-price-and-quantity contract to limit how many items the retailer can order. When capacity information is private, the retailer can order more than the capacity of a low-type manufacturer to reveal the manufacturer's capacity level under the wholesale-price contract. At the same time, under a wholesale-price contract, the manufacturer may not want to supply all the order quantities to the retailers to avoid disclosing its capacity level. We find that pooling equilibrium can survive the Intuitive Criterion, and the manufacturer cannot benefit from capacity scarcity and withholding no longer occurs. Lastly, contrary to the case where capacity information is public, the manufacturer may prefer the wholesale-price-and-quantity contract when capacity information is private. Therefore, it is possible to achieve a win-win situation between supply chain partners with the right contract type, which is not possible when capacity information is public.
Capacity shortfall is frequently observed in various industries when retailers’ total order size exceeds a supplier’s available capacity. For examples, capacity shortage often arises in the fashion goods, telecommunications, and electricity industries (Iyer et al. 2003). Also, it is a common practice for an automobile manufacturer to sell through multiple dealers in the same geographic region; they compete for both the manufacturer’s limited supply capacity and customer demand for popular vehicle models (Liu 2012). However, capacity investment/expansion is usually costly and difficult to timely achieve, such as for vehicles and seasonal products. Thus, it becomes an important issue for the supplier to price and allocate scarce capacity effectively. The objective of this paper is to study how different allocation mechanisms affect a supplier’s wholesale pricing and retailers’ ordering decisions, and to suggest how the supplier can choose an allocation mechanism together with pricing decisions to increase profit. Specifically, we investigate a two-echelon supply chain in which a monopoly supplier (he) sells through duopoly retailers (she) with demand competition. The capacity allocation mechanism considered works in the following way. First, the supplier announces his capacity size, unit wholesale price of this capacity, an allocation rule that defines how capacity will be allocated as a function of retailer order sizes, and a requirement that no order size can be more than total capacity. Second, the retailers place their orders. Third, the supplier allocates capacity to retailers using the pre-announced allocation rule. Finally, the retailers sell the allocated capacity to their customers.
In practice, a supplier with limited capacity often puts capacity on allocation, i.e., rationing capacity through quantity competition of retailers rather than through a pricing mechanism. Capacity allocation is a common occurrence in industries in which capacity expansion is costly and time consuming and price is given exogenously (e.g., steel and paper). A supplier can use his prior beliefs on his own and the retailers’ needs to construct a capacity allocation mechanism for allocation of his capacity among retailers.
Product capacity shortages frequently occur in many industries, such as electrical goods, pharmaceuticals and automobiles. For example, in 2018 the supply of the Tesla Model 3 electric car was unable to meet the market demand (Stangel 2018). An allocation mechanism is typically implemented by suppliers in such cases to partly fill the demand of the retailers, in which a supplier makes decisions about pricing and how the available capacity can be allocated among retailers.
Capacity investment is typically a long-term decision, and capacity adjustments can only be made infrequently. In the short run, mismatches between capacity and demand are inevitable. When capacity lags demand, rationing is necessary. It is the purpose of this paper to study how the mechanism used by the seller to allocate her capacity influences the strategic behavior of the buyers and its impact on the supply chain members’ profits. Consider a supply chain with one supplier and multiple retailers. The supplier produces a single product and sells it to the retailers, who in turn sell the product to consumers. The supplier has limited production capacity. She sets the wholesale price and chooses a mechanism for allocating her capacity in case it is insufficient to satisfy all the retailers’ orders. The retailers determine their order quantities, and are engaged in a Cournot competition at the market level. Notice that the strategic interaction among the retailers occurs not only at the market level but also at the supply level for scare capacity. The wholesale price and the capacity allocation mechanism, both chosen by the supplier, define the game that the retailers play with their order-quantity decisions.
This study investigates the implication of demand information sharing in a supply chain where a manufacturer sources an emerging component from two capacitated suppliers. The two suppliers compete for the manufacturer's order in the emerging market and the demand in the mature market. Two common rules are examined for the manufacturer to allocate its order: fixed allocation rule, with which the ratio to each supplier is fixed, and proportional allocation rule, where the proportion depends on the reserved capacities of the two suppliers for the manufacturer. Our results show that the manufacturer's incentive to share demand information depends on the allocation rule and the option of side payment that the manufacturer proposes between itself and the suppliers for information sharing. Under the fixed allocation rule, no sharing occurs with or without side payment. Under the proportional allocation rule, no sharing occurs without side payment, because full information sharing benefits the manufacturer but hurts the supplier with a low production cost when the information accuracy is low and the mature market potential is moderate. With side payment, the manufacturer shares information with the two suppliers when the information accuracy is not too high and the mature market potential is moderate. Moreover, we examine the influence of information sharing on the choice of the allocation rule. Result shows that the manufacturer's preference for the allocation rule depends on the information accuracy, the mature market potential, and the ratio of the fixed allocation rule. Compared with no sharing, the sharing behavior drives the manufacturer more likely to select the proportional allocation rule.
We consider a two-stage supply chain consisted of one supplier and two retailers in a two-period selling, where retailers compete for limited capacity, and the supplier adopts both a minimum order quantity contract and allocation mechanisms during dealing with retailers. In this study, we solve the equilibrium order quantities of retailers in the presence of the minimum order quantity under fixed and turn-and-earn allocations by game theory. Further, we compare two allocations on inducing the downstream order, and explore the impact of the minimum order quantity on retailers’ ordering decisions as well as the supply chain profit. We find that turn-and-earn allocation induces more order quantity in low demand state than fixed allocation. Moreover, compared to the case without the minimum order quantity, when the minimum order quantity is on a certain interval, it can help shrink the gap of equilibrium order quantities between turn-and-earn allocation and fixed allocation in the first period. Besides, under the two allocation mechanisms, the profits of the supplier and the supply chain are both non-decreasing while each retailer’s profit is non-increasing with the minimum order quantity. Last, in the extension of allocation mechanisms by involving inventory, both the first period order quantity and selling quantity under turn-and-earn allocation are still more than that under fixed allocation, when capacity is large.
An important source of funds for the conflict in the Democratic Republic of the Congo is the revenue from mineral mining. NGOs and legislators made efforts to require manufacturers that use “conflict minerals” to learn and disclose their sources. In the mineral supply chain, the critical link between mines and manufacturers is smelters. We study equilibrium sourcing decisions in the supply network consisting of manufacturers and smelters. We show the equilibrium depends on the total demand of “compliance‐prone” manufacturers, which would choose to be compliant if the prices of certified and noncertified metals were equal. We identify the conditions for the existence of several types of equilibrium: an all‐certified equilibrium where all smelters become certified; an equilibrium where both metal types coexist with no shortage of certified metal; and an equilibrium where both metal types coexist with a shortage of certified metal. In the event that an all‐certified equilibrium is out of reach, we identify how the usage of conflict minerals changes as an NGO or a legislative body targets additional manufacturers. Our model does not incorporate deliberate choices by the mines to become verified conflict‐free, that may enhance the effect of the penalties in the long run. However, in the short‐to‐intermediate run, for a given pool of mines that are verified, an implication of our results is that imposing penalties on manufacturers goes only so far: If penalties induce sufficiently many manufacturers to become compliance‐prone, certified metal may become so expensive that some compliance‐prone manufacturers will not comply.
The product’s yield rates of somewhat unpredictable due to their innovative and sophisticated manufacturing processes, and as a result, the demand for downstream supplier chain members may not always be satisfied. Therefore, contractual agreements are often established to facilitate order fulfilment between suppliers and retailers and thus sustain their cooperative partnerships. This study considers a supply chain, in which a single supplier cooperates with long-term and newly cooperating retailers and develops a game-theoretic model to investigate the optimal capacity allocation strategy for the supply chain. In considering the supplier’s uncertain capacity and competition from the other rival retailer, both retailers will tend to order more than they actually need to avoid possible shortage loss. However, this may strengthen the bullwhip effect and damage the effectiveness of the supply chain. A strategic capacity allocation mechanism with contractual agreements is adopted to prevent distortion of order quantities and also to ensure that the retailers can receive their minimal acceptable quantities. In addition, it is determined that revenue-sharing coordination will mitigate the bullwhip effect and enhance profits for the entire supply chain. The results indicate that the proposed approach will make it possible for the long-term cooperating retailer to honestly place orders.
We study decentralized task coordination. Tasks are of varying complexity and agents asymmetric: agents capable of completing high-level tasks may also take on tasks originally contracted by lower-level agents, facilitating system-wide cost reductions. We suggest a family of decentralized two-stage mechanisms, in which agents first announce preferred individual workloads and then bargain over the induced joint cost savings. The second-stage negotiations depend on the first-stage announcements as specified through the mechanism’s recognition function. We characterize mechanisms that incentivize cost-effective task allocation and further single out a particular mechanism, which additionally ensures a fair distribution of the system-wide cost savings.
We consider a supply chain consisting of one supplier and multiple retailers, in which the supplier has limited capacity and sells one product to retailers engaged in demand competition. When the retailers’ total order quantity exceeds the supplier’s capacity, the supplier implements a preannounced allocation mechanism to fulfill part of the retailers’ orders. We examine a general fixed allocation mechanism with a guaranteed proportion of capacity allocated to each retailer and recognize the retailers’ equilibrium order quantities and the supplier’s optimal pricing strategy under this fixed allocation. We also analyze the impacts of fixed proportions on different supply chain members’ optimal decisions and profits. We find that when at least two fixed proportions differ, or all fixed proportions are identical with only part of capacity guaranteed, fixed allocation induces order inflation, which benefits the supplier. We also demonstrate that fixed allocation is equivalent to proportional allocation under multiple vectors of fixed proportions, by comparing fixed and proportional allocations in terms of the supplier’s optimal price and the supply chain members’ profits. Moreover, we find that fixed allocation can approximately coordinate the supply chain when the number of retailers is sufficiently large. Last, with capacity hoarding in consideration, retailers would hold inventory without using it under fixed allocation, which benefits the supplier.
Dropshipping, where e-retailers manage marketing and dropshippers manage inventory and fulfillment, has become a common practice in e-commerce. However, due to information asymmetry, some e-retailers require dropshippers to share available inventory quantity committed to them on a periodic basis (x-hour, day,...), via an availability commitment. Such commitment leaves the potential for dropshippers to overpromise and exploit availability pooling benefit as it does not involve physical transaction. To further prevent fulfillment failure, some e-retailers stop accepting more orders once remaining promised quantity falls below a promised availability threshold, which is unknown to the dropshipper. In this paper, leveraging the collaboration with a dropshipping furniture manufacturer in the US market, we tackle the inventory availability commitment (IAC) problem for dropshippers at the operational decision level. Three commitment policies are proposed based on varying overpromising allowance: guaranteed fulfillment, controlled fillrate, and penalty-driven fillrate policies. The IAC optimization problem, under uncertain customer demand and retailers threshold, is modeled as a two stage stochastic program. Experimental results on a case study demonstrated that penalty-driven fillrate policy is a dominating policy for a dropshipper under any availability level while the impact of overpromising is maximized under lean availability. E-retailers imposing thresholds tend to receive more availability than those not imposing any threshold in general. However, non-overpromising dropshippers with lean or lower availability level will promise less availability to e-retailers imposing threshold.
This chapter discusses the application of cooperative solution methods, especially cooperative game theory, to decentralized supply chain scheduling problems. We consider many scheduling situations that model diverse applications, and that have classically been analyzed from the perspective of a centralized decision maker. By viewing the jobs or resources within those situations as individual self-interested players, cooperative supply chain scheduling games are defined over those situations. The analysis of these games applies all the main concepts of cooperative games, from the perspective of achieving and sustaining cooperation among the players. Mechanisms for achieving cooperation, and many examples of supply chain scheduling applications, are discussed.
Problem definition: We study profit allocation for a sourcing network, in which a buyer sources from a set of differentiated suppliers with limited capacity under uncertain demand for the final product. Whereas the buyer takes the lead in forming the sourcing network and designing the contract mechanism, due to their substantial bargaining power, the suppliers take the lead in determining the terms of the contract. Academic/practical relevance: We identify contracting mechanisms that will ensure the stability of the sourcing network in the long term, where a stable sourcing network requires an effective profit-allocation scheme that motivates all members to join and stay in the network. Methodology: We apply methods from game theory to model the network and analyze the Nash equilibrium of a noncooperative game under a proposed contracting mechanism. We then use a cooperative game model to study the stability of the resulting equilibrium. Results: We show that the optimal network profit, as a set function of the set of suppliers, is submodular, which allows us to demonstrate that the core of the cooperative game is not empty. We also establish a set of conditions that are equivalent to, but much simpler than, the original conditions for the core. We use these results to demonstrate that the proposed fixed-fee contracting mechanism can implement a stable network in the competitive setting by achieving a profit allocation that is in the core of the cooperative game. We also demonstrate that the grand coalition is stable in a farsighted sense under the Shapley value allocation. Managerial implications: Under the fixed-fee mechanism, the buyer’s decisions maximize the network profit, and each supplier earns a profit equal to its marginal contribution. When the aggregate capacity of the supplier network is high relative to demand, or demand is more likely to be small, the fixed-fee mechanism is likely to outperform the Shapley value allocation from the perspective of the buyer.
Bullwhip effect reduces the efficiency, responsiveness, and value of the supply chain. There are some indirect causes like lead time, the number of echelons, and some direct causes of bullwhip effect such as rationing or price variation. Due to capacity constraints, retailers are forced to experience rationing of their demands. Fear of rationing usually gives rise to manipulable demand and hence increases the bullwhip effect. Moreover, if the retailer’s demand is price sensitive then it will cause price variation. The offerings of premium payment by retailers due to unfulfilled demand lure the supplier to extend his existing capacity and to allocate them more supply. In this paper, an attempt has been made to mitigate the impact of the bullwhip effect using a premium payment scheme. A technique has been coined that will help in reducing the bullwhip effect. The increased value of the supply chain on using a premium payment scheme is proof of the reduction of the bullwhip effect. Results are validated through numerical analysis.
This work presents a mathematical model for the strategic planning of the energy supply chain accounting for fossil fuels and biofuels. The proposed model considers the fuel extraction, production, processing, and distribution to the consumer, as well as the interaction with forest plantations to capture emissions generated in the whole process. Matching law is incorporated to predict, control and understand the situation under study. People's behavior has been included to determine their preference for deciding whether to install a refinery or a biorefinery. The decision is influenced by economic factors such as emissions taxation. The proposed approach was applied to a case study in Mexico, where the profit of the whole system, generated emissions, and jobs are considered. Results show that by implementing matching law it is possible to promote biofuels production together with fossil fuels, as well as to determine the preferences in the system.
Supply chains require the interaction between retailers and suppliers in a dynamic environment. One frequent and costly source of inefficiencies produced by these interactions is known as the bullwhip effect. The bullwhip effect takes place when the variability of orders increases as one moves up the supply chain. However, the impact of this variability is influenced by retailers who compete both for supply and demand. This study systematically explores the impact of the bullwhip effect when there is horizontal competition among retailers. We develop a mathematical model in a competitive system and create two behavioral studies to evaluate the effect of supplier's responsiveness, customers' overreaction, and capacity allocation on retailers' decisions. We show evidence of participants' limited ability to reduce the bullwhip effect. Our results show that competition for supply has a strong effect on the participants' ordering decisions, while the competition for demand does not impact the way how retailers inflate their orders. Furthermore, we show that by adjusting the supplier's allocation mechanism, order variability decreases in up to 50%. Our econometric model shows that (i) more complex systems increase the bullwhip effect but decrease retailers' biases, and (ii) retailers underreact to shipments received from the supplier and ignore customers' order cancellations. We provide recommendations to decrease the impact of the bullwhip effect on supply chains.
The five-volume set IFIP AICT 630, 631, 632, 633, and 634 constitutes the refereed proceedings of the International IFIP WG 5.7 Conference on Advances in Production Management Systems, APMS 2021, held in Nantes, France, in September 2021.*
The 378 papers presented were carefully reviewed and selected from 529 submissions. They discuss artificial intelligence techniques, decision aid and new and renewed paradigms for sustainable and resilient production systems at four-wall factory and value chain levels. The papers are organized in the following topical sections:
Part I: artificial intelligence based optimization techniques for demand-driven manufacturing; hybrid approaches for production planning and scheduling; intelligent systems for manufacturing planning and control in the industry 4.0; learning and robust decision support systems for agile manufacturing environments; low-code and model-driven engineering for production system; meta-heuristics and optimization techniques for energy-oriented manufacturing systems; metaheuristics for production systems; modern analytics and new AI-based smart techniques for replenishment and production planning under uncertainty; system identification for manufacturing control applications; and the future of lean thinking and practice
Part II: digital transformation of SME manufacturers: the crucial role of standard; digital transformations towards supply chain resiliency; engineering of smart-product-service-systems of the future; lean and Six Sigma in services healthcare; new trends and challenges in reconfigurable, flexible or agile production system; production management in food supply chains; and sustainability in production planning and lot-sizing
Part III: autonomous robots in delivery logistics; digital transformation approaches in production management; finance-driven supply chain; gastronomic service system design; modern scheduling and applications in industry 4.0; recent advances in sustainable manufacturing; regular session: green production and circularity concepts; regular session: improvement models and methods for green and innovative systems; regular session: supply chain and routing management; regular session: robotics and human aspects; regular session: classification and data management methods; smart supply chain and production in society 5.0 era; and supply chain risk management under coronavirus
Part IV: AI for resilience in global supply chain networks in the context of pandemic disruptions; blockchain in the operations and supply chain management; data-based services as key enablers for smart products, manufacturing and assembly; data-driven methods for supply chain optimization; digital twins based on systems engineering and semantic modeling; digital twins in companies first developments and future challenges; human-centered artificial intelligence in smart manufacturing for the operator 4.0; operations management in engineer-to-order manufacturing; product and asset life cycle management for smart and sustainable manufacturing systems; robotics technologies for control, smart manufacturing and logistics; serious games analytics: improving games and learning support; smart and sustainable production and supply chains; smart methods and techniques for sustainable supply chain management; the new digital lean manufacturing paradigm; and the role of emerging technologies in disaster relief operations: lessons from COVID-19
Part V: data-driven platforms and applications in production and logistics: digital twins and AI for sustainability; regular session: new approaches for routing problem solving; regular session: improvement of design and operation of manufacturing systems; regular session: crossdock and transportation issues; regular session: maintenance improvement and lifecycle management; regular session: additive manufacturing and mass customization; regular session: frameworks and conceptual modelling for systems and services efficiency; regular session: optimization of production and transportation systems; regular session: optimization of supply chain agility and reconfigurability; regular session: advanced modelling approaches; regular session: simulation and optimization of systems performances; regular session: AI-based approaches for quality and performance improvement of production systems; and regular session: risk and performance management of supply chains
*The conference was held online.
In decentralized supply chains, firms often deal with asymmetric information. One company’s private information can be relevant for the other company to make better decisions. Therefore, what to share, when to reveal, and how to share the information are of interest. There has been an increasing interest in supply chain coordination issues under asymmetric information in the past two decades. However, few of them consider strategic information sharing among the supply chain members. Thus, this paper aims to review the development of information sharing in supply chain management. We classify the existing literature into three categories, namely (i) supply chain coordination under information asymmetry, (ii) information sharing technologies, and (iii) a strategic information sharing framework regarding what, when, and how to share. The related supply chain literature is reviewed based on the different focuses when dealing with information asymmetry. We report the research development and gaps of each category. Further, we propose some future research directions based on the findings from the literature review.
Agricultural issues are of great concern in China. Agricultural products have been criticized for large price fluctuation, food safety and low efficiency of agricultural products production enterprises. Effective supply chain management can improve the management efficiency of production-oriented enterprises and bring about the growth of enterprise performance. Based on the perspective of commitment to resources, the existing agricultural products supply chain management, supply chain, supply chain contract behavior and the paper summarizes the research of supply chain performance, to explore the role of supply chain contract on supply chain behavior and supply chain performance, supply chain behavior influence on supply chain performance, and the regulation of resource commitment. Using SPSS21.0, AMOS and other empirical analysis tools, it is concluded that supply chain contract has a significant impact on supply chain behavior and supply chain performance. Supply chain behavior has a significant influence on supply performance. Supply chain behavior plays a part in a mediating role in supply chain contract and supply chain performance. Resource commitment plays a moderating role in the mediation model.
The Quantity Flexibility (QF) contract is a method for coordinating materials and information flows in supply chains operating under rolling-horizon planning. It stipulates a maximum percentage revision each element of the period-by-period replenishment schedule is allowed per planning iteration. The supplier is obligated to cover any requests that remain within the upside limits. The bounds on reductions are a form of minimum purchase commitment which discourages the customer from overstating its needs. While QF contracts are being implemented in industrial practice, the academic literature has thus far had little guidance to offer a firm interested in structuring its supply relationships in this way. This paper seeks to address this need, by developing rigorous conclusions about the behavioral consequences of QF contracts, and hence about the implications for the performance and design of supply chains with linkages possessing this structure. Issues explored include the impact of system flexibility on inventory characteristics and the patterns by which forecast and order variability propagate along the supply chain. The ultimate goal is to provide insights as to where to position flexibility for the greatest benefit, and how much to pay for it.
(This article originally appeared in Management Science, April 1997, Volume 43, Number 4, pp. 546--558, published by The Institute of Management Sciences.) Consider a series of companies in a supply chain, each of whom orders from its immediate upstream member. In this setting, inbound orders from a downstream member serve as a valuable informational input to upstream production and inventory decisions. This paper claims that the information transferred in the form of ÜordersÝ tends to be distorted and can misguide upstream members in their inventory and production decisions. In particular, the variance of orders may be larger than that of sales, and distortion tends to increase as one moves upstreamÔa phenomenon termed Übullwhip effect.Ý This paper analyzes four sources of the bullwhip effect: demand signal processing, rationing game, order batching, and price variations. Actions that can be taken to mitigate the detrimental impact of this distortion are also discussed.
We focus on backup agreements between a catalog company and manufacturers---a scheme to provide upstream sourcing flexibility for fashion merchandise. A backup agreement states that if the catalog company commits to a number of units for the season, the manufacturer holds back a constant fraction of the commitment and delivers the remaining units before the start of the fashion season. After observing early demand, the catalog company can order up to this backup quantity for the original purchase cost and receive quick delivery but will pay a penalty cost for any of the backup units it does not buy. In representative contracts with five companies, the fraction held as backup varies from 20% to 33% and the penalty ranges from 0 to 20% of cost. We model this inventory problem and derive the optimal solution. We provide results from a retrospective parallel test of the model against buyer decisions in 1993 based on a data set from the women's fashion department at a catalog company (Catco). The results indicate that backup arrangements can have a substantial impact on expected profits and may result in an increase in the committed quantity. Also, these arrangements may maintain the manufacturer's expected profit for a wide range of parameters.
This paper considers an inventory system which maintains stock to meet both high and low priority demands. This model is suggested by the operation of a spare parts pool in a military depot: high priority demands are those which might result in the grounding of an aircraft, for example, while low priority demands are those which arise from the routine restocking of base level inventories. We analyze the following type of control policy: there is a support level, say K > 0, such that when the level of on hand stock reaches K, all low priority demands are backordered while high priority demands continue to be filled. Both continuous review and periodic review systems are considered. The objective of the analysis is to develop methods for comparing fill rates when there is rationing and when there is no rationing for specified values of the reorder point, order quantity and support level.
The authors study a rich class of noncooperative games that includes models of oligopoly competition, macroeconomic coordination failures, arms races, bank runs, technology adoption and diffusion, R&D competition, pretrial bargaining, coordination in teams, and many others. For all these games, the sets of pure strategy Nash equilibria, correlated equilibria, and rationalizable strategies have identical bounds. Also, for a class of models of dynamic adaptive choice behavior that encompasses both best-response dynamics and Bayesian learning, the players' choices lie eventually within the same bounds. These bounds are shown to vary monotonically with certain exogenous parameters. Copyright 1990 by The Econometric Society.
Traditionally, fashion products have incurred high losses due to stockouts and inventory obsolence because long lead times coupled with a concentrated selling season force all or at least most production to be committed before demand information is available. Under a quick response system, lead times are shortened sufficiently to allow a greater portion of production to be scheduled in response to initial demand. We model and analyze the decisions required under quick response and give a method for estimating the demand probability distributions needed in our model. We applied these procedures with a major fashion skiwear firm and found that cost relative to the current informal response system was reduced by enough to increase profits by 60%. Relative to the cost that would have been incurred if no response were used, optimized response reduces cost by enough to roughly quadruple profits.
Consider a system that is modeled as an M/M/1 queueing system with multiple user classes. Each class is characterized by its delay cost per unit of time, its expected service time and its demand function. This paper derives a pricing mechanism which is optimal and incentive- compatible in the sense that the arrival rates and execution priorities jointly maximize the expected net value of the system while being determined, on a decentralized basis, by individual users. A closed-form expression for the resulting price structure is presented and studied.
This paper considers the stock rationing problem of a single-item, make-to-stock production system with several demand classes and lost sales. For the case of Poisson demands and exponential production times, we show that the optimal policy can be characterized by a sequence of monotone stock rationing levels. For each demand class, there exists a stock rationing level at or below which it is optimal to start rejecting the demand of this class in anticipation of future arrival of higher priority demands. A simple queueing model is analyzed to compute the operating cost of a rationing policy. In a numerical study, we compare the optimal rationing policy with a first-come first-served policy to investigate the benefit of stock rationing under different operating conditions of the system.
We model the effects of alternative coordination structures on the performance of a firm that faces uncertain demand in multiple horizontal markets. The firm's coordination structure is jointly determined by its decision-rights structure and by its information structure. We compare the performance of decentralized, centralized and distributed structures and study factors that affect the value of coordination. The results quantify and illustrate the value of co-locating decision rights with specific knowledge.
Stereotypically, marketing is mainly concerned about satisfying customers and manufacturing is mainly interested in factory efficiency. Using the principal-agent (agency) paradigm, which assumes that the marketing and manufacturing managers of the firm will act in their self-interest, we seek incentive plans that will induce those managers to act so that the owner of the firm can attain as much as possible of the residual returns. One optimal incentive plan can be interpreted as follows: The owner subcontracts to pay the manufacturing manager a fixed rate for all capacity he delivers. Each marketing manager receives all of the returns from his product. In turn, all managers pay a fixed fee to the owner. Under this plan, the marketing managers will often complain about the stock level decisions, even though these levels are announced in advance. Under a revised plan, the owner can eliminate such complaints by delegating the stocking decisions to the respective marketing managers, without any loss. This plan is interpreted as requiring the owner to make a futures market for manufacturing capacity, paying the manufacturing manager the expected marginal value for each unit of capacity delivered, receiving the realized marginal value from the marketing managers, and losing money on average in the process.
This paper studies optimal pricing and capacity decisions for a service facility in an environment where users' delay cost is important. The model assumes a general nonlinear delay cost structure and incorporates the tradeoff between the delay cost and capacity cost. We find necessary and sufficient conditions for the optimality of a pricing rule that charges out service resources at their marginal capacity cost. We examine the issue of budgetary balance and find that net-value maximization entails a budget deficit for the service facility; that is, the service facility should be evaluated as a "deficit center." The results provide guidelines under which the optimal magnitude of the deficit can be determined.
Customers arrive at a service area according to a Poisson process. An arriving customer must choose one of K servers without observing present congestion levels. The only available information about the kth server is the service time distribution (with expected duration μ k ⁻¹ ) and the cost per unit time of waiting at the kth server (h k ). Although service distributions may differ from server to server and need not be exponential, it is assumed that they share the same coefficient of variation. Individuals acting in self-interest induce an arrival rate pattern (λ̂ 1 , λ̂ 2 , …, λ̂ k ).
In contrast, the social optimum is the arrival rate pattern (λ 1 *, λ 2 *, …, λ k *) which minimizes long-run average cost per unit time for the entire system. The main result is that λ̂ k 's and λ̂ k *'s differ systematically. Individuals overload the servers with the smallest h k /μ k values.
For an exponential service case with pre-emptive LIFO service an alternative charging scheme is presented which confirms that differences between individual and social optima occur precisely because individuals fail to consider the inconvenience that they cause to others.
A supplier may distinguish between the demands be receives for an item, attaching greater importance to satisfying some categories of demand than others. This paper discusses use of reserve levels, i. e, stock levels at which to stop issuing in response to lower priority demand. An algorithm which calculates optimum reserve levels is developed. Its effectiveness is compared to use of unsophisticated policies.
We consider the problems associated with an inventory system in which demands for stock are of n classes of varying importance. When demand from a given class arrives one must decide whether to satisfy it or to not satisfy it and conserve stock for possible use later to satisfy demand from a more important class. Conditions are given under which the optimal rationing policy between successive procurements of new stock is determined by a set of critical rationing levels such that at a given time one satisfies demand of a given class only if no demand of a more important class remains unsatisfied and as long as the stock level does not fall below the critical rationing level for that class at that time. Conditions are also given under which the optimal procurement policy at a given time is determined by a single critical level in the usual manner. Further conditions are given which assure that the optimal rationing and procurement policies may be determined myopically.
In many industries a supplier's total demand from the retailers she supplies frequently exceeds her capacity. In these situations, the supplier must allocate her capacity in some manner. We consider three allocation schemes: proportional, linear and uniform. With either proportional or linear allocation a retailer receives less than his order whenever capacity binds. Hence, each retailer has the incentive to order strategically; retailers order more than they desire in an attempt to ensure that their ultimate allocation is close to what they truly want. Of course, they will receive too much if capacity does not bind. In the capacity allocation game, each retailer must form expectations on how much other retailers actually desire (which is uncertain) and how much each will actually order, knowing that all retailers face the same problem. We present methods to find Nash equilibria in the capacity allocation game with either proportional or linear allocation. We find that behavior in this game with either of those allocation rules can be quite unpredictable, primarily because there may not exist a Nash equilibrium. In those situations any order above one's desired quantity can be justified, no matter how large. Consequently, a retailer with a high need may be allocated less than a retailer with a low need; clearly an ex post inefficient allocation. However, we demonstrate that with uniform allocation there always exists a unique Nash equilibrium. Further, in that equilibrium the retailers order their desired amounts, i.e., there is no order inflation. We compare supply chain profits across the three allocation schemes.
Consider a committee which must select one alternative from a set of three or more alternatives. Committee members each cast a ballot which the voting procedure counts. The voting procedure is strategy-proof if it always induces every committee member to cast a ballot revealing his preference. I prove three theorems. First, every strategy-proof voting procedure is dictatorial. Second, this paper's strategy-proofness condition for voting procedures corresponds to Arrow's rationality, independence of irrelevant alternatives, non-negative response, and citizens' sovereignty conditions for social welfare functions. Third, Arrow's general possibility theorem is proven in a new manner.
This paper generalizes the study of nonlinear tariffs, i.e., those depending nonlinearly on the quantity purchased, to the case of a symmetric oligopoly. Competitive equilibria and the corresponding tariffs are analyzed in a Cournot framework. Various equilibria are obtained, which depend both upon the number of competing suppliers and the choice of market parameters used to characterize the competitors' strategies. Buyers are classified by type, each selecting an optimal consumption level in response to the prevailing tariff. The phenomena of buyer self-selection found in monopoly nonlinear pricing and the scaling of equilibrium demand elasticity found in Cournot models both appear in the results.
This paper studies competition between firms that produce goods or services for customers sensitive to delay time. Firms compete by setting prices and production rates for each type of customer and by choosing scheduling policies. The existence of a competitive equilibrium is proved. The competitive equilibrium is well defined whether or not a firm can differentiate between customers based upon physical characteristics because each customer has incentive to truthfully reveal its delay cost. Further insights are derived in two special cases. A unique equilibrium exists for each of the cases. In the first case, firms are differentiated by cost, mean processing time, and processing time variability, but customers are homogeneous. The conclusions include that a faster, lower variability and lower cost firm always has a larger market share, higher capacity utilization, and higher profits. However, this firm may have higher prices and faster delivery time, or lower prices and longer delivery time. In the second case, firms are differentiated by cost and mean processing time, but customers are differentiated by demand function and delay sensitivity. The results include that customers with higher waiting costs pay higher full prices, and that each firm charges a higher price and delivers faster to more impatient customers. Competing firms that jointly serve several types of customers tend to match prices and delivery times.
This paper analyses convergence of unemployment rates in Poland at NUTS4 level by testing nonlinear convergence, applying the modified KSS-CHLL for each pair of territorial units. The results suggest that actually the convergence is a rare phenomenon and occurs only in 1916 cases out of potential over 70 000 combinations. This paper inquires what systematic reasons contribute to this phenomenon. There are some circumstances under which unemployment convergence should be more expected than in others. These include sharing a higher level territorial authority, experiencing similar labour market hardship or sharing the same structural characteristics. For each of these three criteria we analyse the frequency of the differential nonstationarity within groups (as evidence of convergence) and across groups (as evidence of "catching up").
It has been conjectured that no system of voting can preclude strategic voting--the securing by a voter of an outcome he prefers through misrepresentation of his preferences. In this paper, for all significant systems of voting in which chance plays no role, the conjecture is verified. To prove the conjecture, a more general theorem in game theory is proved: a game form is a game without utilities attached to outcomes; only a trivial game form, it is shown, can guarantee that whatever the utilities of the players may be, each player will have a dominant pure strategy.
A $30 billion windfall?
- M Gary
Gary, M. 1993. A $30 billion windfall? Progressive Grocer 72 7
FTC says Toys R Us competes unfairly
- B Gruley
- J Pereira
Gruley, B., J. Pereira. 1996. FTC says Toys R Us competes unfairly. Wall Street Journal May 23 A–3
Frito-Lay puts up more than chips in deal for Olestra Wall Street Journal May 31 A–3. CACHON AND LARIVIERE Capacity Choice and Allocation Management Science Advance-purchase discount and monopoly allocation of capacity
- R Frank
- T J Holmes
Frank, R. 1996. Frito-Lay puts up more than chips in deal for Olestra. Wall Street Journal May 31 A–3. CACHON AND LARIVIERE Capacity Choice and Allocation Management Science/Vol. 45, No. 8, August 1999 1107 rGale, I. L., T. J. Holmes. 1993. Advance-purchase discount and monopoly allocation of capacity. Amer. Econom. Rev. 83 135–146
U.S. jury convicts ex-Honda officials of taking bribes
- A B Henderson
Henderson, A. B. 1995. U.S. jury convicts ex-Honda officials of taking bribes. Wall Street Journal June 2 B–5
Supply contracts for fashion goods: Optimizing channel profits. Working paper, The Wharton School
- K L Donohue
Donohue, K. L. 1996. Supply contracts for fashion goods: Optimizing channel profits. Working paper, The Wharton School,
University of Pennsylvania, Philadelphia, PA.
Frito-Lay puts up more than chips in deal for Olestra
- R Frank
Frank, R. 1996. Frito-Lay puts up more than chips in deal for
Olestra. Wall Street Journal May 31 A–3.
This paper has been with the authors 6 months for 2 revisions. CACHON AND LARIVIERE Capacity Choice and Allocation
- Hau Accepted
- Lee
Accepted by Hau Lee; received December 12, 1996. This paper has been with the authors 6 months for 2 revisions.
CACHON AND LARIVIERE
Capacity Choice and Allocation
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Management Science/Vol. 45, No. 8, August 1999
Optimal multi-unit auctions The Economics of Missing Markets, Information, and Games
- E Maskin
- J Riley
Maskin, E., J. Riley. 1989. Optimal multi-unit auctions. Frank Hahn
ed., The Economics of Missing Markets, Information, and Games.
Oxford University Press, New York.