1,720,998 research outputs found
Crossdocking insights from a 3rd party logistics firm in Turkey
Get PDFIn crossdocking, the inbound materials coming in trucks to the crossdock facility (CF) are directed to outbound doors and are directly loaded into trucks that will perform shipment, or are staged for a very brief time period before loading. Crossdocking has a great potential to bring savings in logistics; for example, most of the logistics success of Wal-Mart, the world’s leading retailer, is attributed to crossdocking. This paper firstly reviews different types of crossdocking. Then a case study that describes the crossdocking applications of a 3rd party logistics firm in Turkey is presented. It is found out that crossdocking brings new challenges, which should be resolved for successful operations. Many of the challenges faced by the described 3rd party logistics firm and the practical solutions the firm applies are applicable to other companies that are interested in applying crossdocking
Information control and usage for Pareto improvement of supply chains
No full textIn Chapter 1, we study the problem of using manufacturer queue information with order placement control to provide Pareto improvement in a decentralized supply chain. Motivated by examples in industry with heterogeneous information availability in decentralized supply chains, we model a supply chain with one manufacturer (or assembler) and two retailers (or OEMs) who order independently. The manufacturer may provide her queue state information to one (informed) retailer with possible constraints on order placement. We propose an order placement constraint imposed by the manufacturer and a conforming order policy class for the informed retailer that may potentially increase the retailer\u27s order flow variability, but, by being negatively correlated with queue length, stochastically decreases the manufacturer queue\u27s length and variability. Within this order policy class, we propose a specific order policy that is guaranteed to make the informed retailer not worse off while making the other retailer better off and the manufacturing queue stochastically smaller and less variable, hence beneficial to the manufacturer, and thus providing Pareto improving outcomes. We provide a general analysis and a computational method to obtain stationary distributions of the queue size and thereby costs of the retailers for any given pure, Markov and stationary order policy. We propose a non-preemptive priority scheduling to permit different splits of the benefit across the two retailers. Numerical and simulation results confirm and demonstrate the magnitude of these cost reductions. When both retailers have access to the information associated with the proposed constraint and implement the proposed ordering policy, our simulations show that inducing a negative correlation between the order flow and the manufacturer state decreases the cost of each entity even further. This model is thus a building block towards understanding how information sharing can be coupled with order management to improve outcomes for participants in a supply chain. In Chapter 2, we study the problem of using manufacturer queue information with pricing control policies to provide Pareto improvement in a decentralized supply chain. Our research problems are motivated by the scenarios in industry with information availability in decentralized supply chains, we model a supply chain with one manufacturer (or assembler) and two retailers (or OEMs) who order independently using a base stock order policy. The manufacturer queue state information can be provided to one or both retailers. The case of no information sharing is used as a benchmark in this paper. We propose and analyze the promotion policies via pricing, given the information availability, that provide cost saving to each business entity and thereby Pareto improvement to the supply chain. We establish analytically that the number of outstanding orders of each retailer is stochastically reduced and less variable when both retailers have the queue state information and employ our proposed promotion policies. This results in a lower cost for each retailer. When only one retailer is informed, we show that the total queue is stochastically smaller and less variable, resulting in a lower cost for the uninformed retailer. Using approximate analysis, we show that the cost of the informed retailer is also decreased. Finally, our numerical calculation and simulation results demonstrate that the cost of each retailer is indeed significantly reduced, monotonically in promotion level. Our results challenge a popular belief for a similar scenario that promotion of retailers should not be synchronized. The problems in Chapter 3 are motivated by the procurement problems faced by a manufacturer of finished products that uses commodity metals as raw material. We study the optimal procurement and inventory control policies when the raw material can be procured from two sources, i.e., fixed period swing contracts and spot markets, in order to satisfy a stochastic demand. Under a swing contract, the company makes a commitment on the total purchase quantity over a fixed contract interval. The contract price is specified and fixed throughout the term, but the quantity delivered in each period of the contract is flexible. Given that the spot price evolves stochastically, the company may want to use a swing contract to hedge against the uncertainty of the spot market price and at the same time take advantage when low spot prices are realized. We model the problem as a finite horizon, multi-period stochastic dynamic program in which the company makes decisions regarding the swing contract quantity and then, at the beginning of each period, dynamically chooses the quantities purchased from swing contract and spot market. We use data from the manufacturer to numerically compare the impact of the optimal policies against that of the policies implemented currently. Our numerical results show that by applying the proposed policy, the company can obtain, on average, more than 8.89% cost saving, compared with their current policies. (Abstract shortened by UMI.
Optimal product mix and supply chain design
No full textIn the first essay, we analyze 360 independent retailers that are supplied by a durable goods manufacturer with a large product variety. Our objective is to identify the links between product variety, mean demand and associated demand uncertainty and suggest how to design product mix and supply chain strategies to manage the impact of variety. Our results reveal that the impact of variety on demand and demand predictability varies by consumer segment. We find that higher variety does not necessarily lead to higher aggregate level demand while higher variety does lead to higher demand variability across time periods and higher volatility in consumer choices. Our results suggest that a product strategy that offers either a focused product line with high value or a tailored product line with high price and margin may improve the profitability of a firm without compromising consumer value. We also see the evidence that firms may improve both operational efficiency and consumer satisfaction by designing its supply chain to reduce delivery lead time for high value products but to utilize multi-sourcing for customized products. Our results suggest that managing variety can be a key ingredient of supply chain performance. In the second essay, we analyze how to leverage product mix strategies to manage the impact of product variety. We model the demand forecast problem that arises both because historical data does not reveal the time-variant consumer preference and also because the consumer choice is fluid due to the complexity of making choices. We describe how the mean and the variance are affected by product variety and product value based on our empirical observations and determine the optimal product mix (the number of products and the level of product features) to offer and the quantity to order for each product using a newsvendor framework. We perform sensitivity analysis to show how the optional solutions are affected by the characteristics of target consumers, the capabilities of a firm (supply chain flexibility and market reputation) and market characteristics (supply chain structure and salvage value). Our results suggest reasons why firms adopt different market approaches when demand uncertainty increases in variety and suggest how to serve consumers to achieve both market opportunity and cost efficiency. In the third essay, we model supply chain design and product variety offered at each facility location and analyze the benefits of a joint decision of product mix and supply chain strategy when demand and demand uncertainty are endogenously determined by the chosen product mix and supply chain structure. We assume that in each region, demand arises in multiple consumer preference scenarios due to unknown demand drivers and model two consumer segments which differ in their willingness to substitute in case of reduction in product variety and their willingness to wait in case of an out-of-stock. We allow the supply chain to satisfy demands from multiple locations, possibly with a different set of DCs under each demand scenario, and analyze if such flexibility mitigates some of the adverse impact of demand uncertainty. Using the dataset offered by a durable goods manufacturer with a large variety, we provide computational results. Our results suggest that coordinating variety choice and supply chain flexibility can provide significant benefits
Information visibility and its impact in a supply chain
No full textWe model the impact of information visibility in a two-level supply chain consisting of independent retailers who share upstream supply. The manufacturer supplies similar products to the two retailers and each retailer serves its independent end market. Retailers face one period of stochastic demand and can satisfy the demand by ordering in the first period or back-ordering some of the demand and satisfying it in the second period. The wholesale price in the second period is decreasing in the total order size, across the two retailers, in the first period. This decrease in wholesale price captures the market learning effect of aggregate orders that has been extensively documented in the empirical literature. We use a game-theoretic framework to investigate the ex ante incentives for the retailers to share their private demand information. We show that: (1) the two retailers have no incentives to share information about their private values when equilibrium order quantities are interior i.e., the order size is between zero and the demand: (2) one retailer who unilaterally discloses more information to his competitor may make himself better off while making his competitor worse off, if some order quantities are on the boundary; and (3) partial information sharing may be the equilibrium strategy for retailers. This paper thus identifies conditions under which different levels of information sharing may be the equilibrium outcomes in a supply chain
Models for managing product upgrade in closed loop supply chains
No full textIn this dissertation, we develop strategic and operational level decision making tools to help manage product upgrades in closed loop supply chains under limited availability of upgrade resources. The models developed in this research are mainly applicable to products that are modular, repairable and expensive. This research was directly motivated by a research project done for the United States Coast Guard (USCG) to help them plan an aircraft upgrade from an older to newer version. The upgrade also involved upgrading an aircraft component (gearbox) in order to achieve higher operational efficiency. Apart from developing the planning tools, we also study how the decisions made by these planning tools vary under different levels of operational flexibility. We first develop strategic level decision making tools that can be used to plan the rate of product upgrades in closed loop supply chains under limited availability of upgrade resources. We show that in closed loop supply chains, the optimal rate of product upgrade depends on current availability of upgrade resources and future supply of spare parts and that the flexibility available to operate in different product-part configurations during product upgrade can compensate for scarcity of upgrade resources. We then develop models of operational level decisions during product upgrade over a shorter planning horizon, under different levels of operational flexibility. We first develop a model for the discrete version of the problem to study the exact allocation of upgraded and regular components to products when they fail. Here, we develop the optimal product and component hold back policy for a given level of operational flexibility and show that a myopic policy of always keeping products operational or using up all the upgraded components immediately may not always be optimal. We then study a continuous time version of the problem and provide numerical results. Finally, we consider a maintenance network of repairable parts with age-dependent maintenance times, motivated by a maintenance network at the USCG. Studying the impact of inventory of repairable parts (say gearbox) on system performance shows that as the inventory of gearboxes increases, the aircraft availability may vary non-monotonically, which is counterintuitive. Though we have not been able to find any analytical results to prove the same, we present numerical evidence showing this non-intuitive result. We leave this problem as an open question for future research. In summary, this research develops decision making tools to assist the optimal management of product upgrades in a closed loop supply chain. Managerial insights are developed using data motivated by the US Coast Guard problem context
Essays in operations management
Get PDFIn the first essay, we investigate the impact of reciprocity in the dyadic supply chain. Our study is motivated by the experiences of the semiconductor and LCD industries, we investigate the impact of reciprocity in the dyadic supply chain. A notable characteristic in the above technology industries is the alternating possession of bargaining power caused by cyclical demand. We incorporate a reciprocal game in a dyadic supply channel over two periods. We investigate how a supplier is influenced and protects himself during the oversupply period by anticipating the buyer\u27s reciprocal behavior. Our results show that a supplier\u27s understanding of a buyer\u27s reciprocal behavior can mitigate double marginalization and can even fully coordinate the channel. This implies that even without a costly mechanism to resolve the double marginalization, appropriate consideration of the counterpart will increase channel efficiency. In the second essay, we consider a firm that manages a portfolio of customers placing orders that need replacement parts. The firm has both long-term and short-term customers. A long-term customer places both routine orders for routine maintenance and urgent orders due to emergency with a low margin for the firm and a short-term customer places urgent orders with a high margin for the firm. Routine orders provide stable loads and generate efficiency. Considering the increase in efficiency by routine orders, there is a trade-off between the efficiency and profitability of the order portfolio. Motivated by data provided by the company, we build an analytical model to support optimal decision making. We identify the impact of an additional urgent order to the cost embedded in the future operations. Finally, we model the mixed integer program to support the company\u27s capacity plan. We conclude with in sights provided to the firm and managerial insights for optimal customer order portfolios. In the third essay, we focus on the economic benefit of profound technology projects as milestones are achieved. Ce-Al alloy project by CMI promotes an example. The project we use replaces the current aluminum (Al) alloy with Al-cerium (Ce) alloy in an engine block and an engine head to increase the operational efficiency of the vehicle. We can expect higher fuel efficiency as well as a lower cost of production. The Ce-Al alloy development project by the Critical Material Institute (CMI) announces an achievement level at every milestone. The model keeps track of two goals, efficiency improvement and production cost reduction. Based on the progress about those two R&D tracks, the research question involves when to add capacity for production to maximize profits and how to adjust the R&D strategy to maximize benefit. The problem is modeled in a Bayesian update and a stochastic dynamic program. Insights from the model were used to estimate the economic benefit for CMI and suggestion for improvement
Managing contingencies in supply chains
No full textIn this work, we study contingency related issues arising from both the demand side and the supply side. On the demand side, we model demand surges as the primary source of contingency. We analyze demand postponement as a strategy to handle potential demand surges. Under demand postponement, a fraction of the demands from the “regular” period are postponed and satisfied during a “postponement” period. This permits capacity to be procured to satisfy the postponed demands. A reimbursement per unit is paid to customers whose demands are postponed. The basic idea is that by preempting stock-outs through demand postponement, we can reduce overall stock-out costs. We analytically solve the problem of determining the optimal regular and postponement period capacities, and the demand splitting rule. We then consider the decentralized version of the demand postponement problem, in which the postponement cost is the customers\u27 private information that is not known to the supplier. We show that the agency effect is equivalent to a larger postponement cost, with the increment being the “information cost”. We also model details of customer response to demand postponement and study using price discount to induce customers to agree to demand postponement. We explicitly consider heterogeneous customers with differing preferences. We characterize the optimal discount scheme the supplier should offer in order to maximize her expected profit. On the supply side, we consider a two-tier supply chain consisting of a monopolist supplier and multiple buyers. Suppose the supplier experiences a disruption in supply and actions (with associated costs) are required to restore the supply. During the disruption phase, buyers do not have access to supply and thus experience stock-outs. Buyers incur a back-order cost, which we model as private information not known to the supplier. We obtain insights into issues of how fast to restore the system and what costs to charge each party in the system
Essays in sustainable operations
No full textIn the first chapter, we study the impact of Environmentally Preferable Purchasing (EPP) and municipal supply uncertainty as a driver of recycled content in practice and explore the impact of recycled content claims made as an annual average vs. batch specific claims. We formulate a two stage stochastic dynamic program, consisting of a design stage, where a recycled content claim is declared, and a procurement stage where the manufacturer has recourse to self-collection to meet the commitment. Our goal is to compare manufacturer profits and recycled content levels under batch specific and time averaged claims and explore the effect of EPP and supply-side intervention i.e. moving from single stream to dual-stream collection of recyclables. Our main contributions are as follows: (a) We establish conditions under which batch specific claims are larger than time averaged claims; (b) we find that variability of supply in the municipal stream of recyclables increases (decreases) the recycled content claim if the collection cost is high (low), (c) we find that simultaneously increasing recovery of recyclables through dual-stream collection and/or container deposit legislation and creating demand side incentives for recycled content can create win-win conditions; and (d) show that EPP increases (decreases) the recycled content claim if the collection cost is sufficiently low (high). Based on analytical results, we show that demand side policies like EPP should be tailored to local supply limitations to achieve Pareto-improving outcomes for manufacturers and the environment. Our model is calibrated to data from the fiberglass insulation industry and a glass recycling study conducted in the State of Ohio. In the second chapter, we consider the case of a manufacturer investing production capacity in presence of R&D updates on development of an energy efficient technology. This problem is relevant to the development of clean energy technologies like direct drive wind turbines and energy efficient lighting, where R&D progress is tracked over a Technology Roadmap. We build a stylized model of a manufacturer adding assembly capacity based on realized R&D progress and calibrate it to data for the wind turbine industry. We provide option value estimates for the R&D projects along with the timing of capacity addition. In the third chapter, we consider the problem of a customer that contracts with a Product Recovery Facility (PRF) to dispose of its used electronic equipment in the most environmentally friendly manner i.e, by reuse and refurbishment or disassembly and recycling. Typical disposition contracts in the IT Asset disposition industry involve a fixed upfront payment by the customer to the PRF and a rebate for each unit resold that is credited back to the customer. We optimize the optimum transfer payment and rebate fraction, under uncertainty in condition of incoming units, while accounting for the PRFs bankruptcy risk. We find that as customers become increasingly environmentally conscious they choose a lower rebate fractions and decrease the upfront fee paid to the PRF. Moreover, as uncertainty in incoming condition and refurbishing cost increases customers again choose a lower rebate fraction. Overall, our model agrees with existing best practice in the industry i.e., customers who refresh\u27\u27 their IT Assets frequently keep a greater fraction of the resale value. We calibrate our model to a real dataset consisting of end-of-life laptops processed at a PRF
A stochastic production -inventory model with two demand processes of different variability
No full textThe interaction between two demand-streams sharing a common manufacturing capacity is a commonly observed phenomenon in the business-world. This study aims to analyze the business situations where the two demand-streams differ in their variability. A prime motivation for this study comes from the advent of Efficient Consumer Response (ECR) initiative in the Grocery Industry (see Kurt Salmon Associates, 1993). The main impact of many variability reduction programs offered under the ECR umbrella (such as partial elimination of promotion pricing policies—efficient promotions) is that the grocery manufacturers increasingly see a mixture of order-streams that differ in their variability. Such business situations raise many questions for managers. For example, how can such a manufacturer use its capacity management policy as a tool to encourage the low variability behavior in its retailers? The objective of this study is to develop a model to address such questions and provide managerial insights through exact analysis of the model. The model we develop captures the essential features of the business situations described above. The demands are modeled as stochastic renewal arrival processes. The two demand-streams are served by separate retailer inventories that are managed by individually optimal base-stock policies. The two order-streams from these inventories are superimposed and form a common queue for replenishment at the manufacturer\u27s capacity. Unique features of our model are: (i) the difference in variability of the two demand processes is explicitly modeled in an integrated production-inventory setting, (ii) the lead-times that the two retailers receive are endogenous to the model and (iii) the exact analytical evaluation of two retailers\u27 inventory costs is provided. In addition, we significantly advance the application of stochastic ordering tools to inventory theory and provide results that compare and order the two retailers\u27 inventory costs under different capacity planning policies at the manufacturer
Managing multiproduct supply chains with two supply paths
No full textIn this dissertation, we study multiproduct supply chain management problems with two supply paths in the context of a leading wine bottle manufacturer. The company seeks to provide a high level of customization and service for its customers (vineyards or bottlers). It operates under the make-to-sales-order system, based on sales orders from customers. However, the highly volatile nature of demand in the wine industry often creates a huge discrepancy between sales orders and actual demand, which results in frequent order cancellations. The company wanted to reduce inventory while maintaining the current variety and high customer service levels – a necessity for customer satisfaction in the industry. The company has over 370 distinct wine bottles which go into more than 3,800 customized cases called UPIDs. At the end of production lines, glass bottles can be packaged either in UPIDs (customized bottles) or in bulk (bottles in standard containers) on pallets. There are three parts in the thesis, and each of them is motivated by the company\u27s supply chain management problems. In the first part, we develop an optimization model to make the optimal decision to store a selection of products having poor demand forecast in bulk to seek the benefit of a pooled replenishment system. We present comparative statics results, and a heuristic that helps identify characteristics of products potentially move to bulk. In the second part, we bring the problem context and intuition of the first part and study under a stochastic inventory model. We present the close-to-optimal cutoff value to make the optimal bulk-to-case decision, and reconfirm the comparative statics results from the previous part. In the last part, we use empirical analysis to study the company\u27s current repack process that minimizes backorders and improves its customer service level. We also generate various repack heuristics in order to find the policy that is the closest match to the company\u27s actual repack practice
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