Two-stage Serial Supply Chain Research Articles

Service capacity coordination in IT services supply chain

PurposeThe purpose of this paper is to investigate capacity coordination in services supply chain (SSC). It provides discussion and application of various contracts in a two-stage single period SSC.Design/methodology/approachThis paper considers a two-stage serial supply chain with demand uncertainty and price insensitivity. A model is developed to represent a global IT SSC incorporating services specific factors like over-capacity cost and higher degree of substitution resulting in flexibility to meet unplanned demand. At first, centralized and competitive solutions of the model are studied. Then, the paper studies coordination in this supply chain using some of widely used contract templates.FindingsThis paper finds several key insights for the researchers and practitioners in this area around adverse impact of over-capacity cost on demand, positive effect of delivery team’s exposure to market on contracting terms and better understanding of efficient frontiers for selected contracting mechanism.Research limitations/implicationsThis paper has limited its analysis to three key and most widely used contracts and made assumptions about risk-neutrality of the firms. Future research can study other contracting templates and/or relax for the model as laid out in this paper.Practical implicationsAn automated software agent can be built leveraging the closed form equations developed here to help decide on optimal capacity investment and devise coordinating contracts.Originality/valueThis paper established that because of higher degree of substitution, perishability and non-trivial over-capacity cost, SSC behave bit differently than the physical goods supply chain and coordination of participating firms needs to be studied in a services specific context for improving system-wide performance.

An inventory model of supply chain disruption recovery with safety stock and carbon emission consideration

Abstract Managing highly evolved supply chains and maintaining environmental sustainability at the same time is not easy, especially when it is vulnerable to various disruption risks. In the event of a disruption, the sustainability of a supply chain will be compromised as a consequence of attempting to quickly recover from the disruption with minimal costs. However, a balance can be achieved between sustainability and recovery. Companies can maintain its sustainability, while recovering its operations with a comprehensive analysis of the decision criteria for the intended system. This study presents a recovery model of a two-stage serial supply chain subject to supply disruption with consideration of safety stock and carbon emission. The system consists of a single manufacturer and a single retailer, while the carbon emission consideration is from the transportation activities during the recovery cycle. The model is capable of determining the new recovery schedule of the manufacturer and the retailer, the optimal safety stock level, and the carbon emission cost impact during recovery. The results show that for a range of short disruption periods, the optimal safety stock level is significantly affected by the holding cost values. Furthermore, the cost of carbon emission is dependent on the number of recovery cycles, the distance between supply chain entities, and the social cost of carbon at the time of the disruption. A comparison of two disruption scenarios with different transportation arrangement illustrates a solution for a better recovery plan with faster recovery cycles, lower total recovery cost, and without any change to carbon emission cost impact. This study contributes to the supply disruption literature with the findings on how safety stock and environmental effect consideration affect the disruption recovery decision-making process in term of lot sizing and transportation arrangement.

Optimal periodic flexible policies for two-stage serial supply chains

In a two-stage serial supply chain, a periodic flexible policy (PF policy) allows the retailer to receive fixed orders that may depend on demand history in one period of the ordering cycle and order freely in other periods. Existing literature has shown that certain PF policies can significantly reduce the inefficiency in a decentralized supply chain. However, these works have mostly defined and implemented ad-hoc periodic flexible policies and have not attempted to identify the optimal periodic flexible policies. In this paper, we characterize the structure of the optimal PF policy using calculus of variations. In particular, we show that under the optimal PF policy, the retailer receives shipments either according to a state dependent capacitated policy or a state dependent order up to policy. Furthermore, we can approximate the retailer’s optimal restricted ordering function by a piecewise linear function and show numerically that this approximation is near optimal.

Optimal Periodic Flexible Policies for Two-Stage Serial Supply Chains

In a two-stage serial supply chain, a periodic flexible policy (PF policy) allows the retailer to receive fixed orders that may depend on demand history in one period of the ordering cycle and order freely in other periods. Existing literature has shown that certain PF policies can significantly reduce the inefficiency in a decentralized supply chain. However, these works have mostly defined and implemented ad-hoc periodic flexible policies and have not attempted to identify the optimal periodic flexible policies. In this paper, we characterize the structure of the optimal PF policy using calculus of variations. In particular, we show that under the optimal PF policy, the retailer receives shipments either according to a state dependent capacitated policy or a state dependent order up to policy. Furthermore, we can approximate the retailer's optimal restricted ordering function by a piece-wise linear function and show numerically that this approximation is near optimal.

VMI versus information sharing: an analysis under static uncertainty strategy with fill rate constraints

A vendor-managed inventory (VMI) relationship between a downstream retailer and an upstream vendor consists of two distinct components: (i) information sharing (IS) and (ii) a shift in decision-making responsibility. This study compares these two components of VMI in a two-stage serial supply chain based on the ‘static uncertainty’ strategy under dynamic and random demand with fill rate constraints. Numerical experiments are conducted using analytical models to identify the conditions where the incremental value of VMI over IS is significant. The results provide guidelines relevant to academia and supply chain practitioners in taking VMI adoption decision above and beyond IS according to their specific business environment.

Decision support system for vendor managed inventory supply chain: a case study

Vendor-managed inventory (VMI) is a widely used collaborative inventory management policy in which manufacturers manages the inventory of retailers and takes responsibility for making decisions related to the timing and extent of inventory replenishment. VMI partnerships help organisations to reduce demand variability, inventory holding and distribution costs. This study provides empirical evidence that significant economic benefits can be achieved with the use of a genetic algorithm (GA)-based decision support system (DSS) in a VMI supply chain. A two-stage serial supply chain in which retailers and their supplier are operating VMI in an uncertain demand environment is studied. Performance was measured in terms of cost, profit, stockouts and service levels. The results generated from GA-based model were compared to traditional alternatives. The study found that the GA-based approach outperformed traditional methods and its use can be economically justified in small- and medium-sized enterprises (SMEs).

Comparison of (s, S) and (R, T) Policies in a Serial Supply Chain with Information Sharing

It has been studied that retailer's using a suboptimal (R, T) policy is often more desirable to make the best use of information flows than the locally optimal (s, S) policy in a two-stage serial supply chain. In this paper, by performing an extensive computational study, we tabulate the benefit of the retailer's using (R, T) policy instead of (s, S) policy in a supply chain with information sharing, and compare it to a maximum possible benefit that could be achieved in a centralized supply chain. We can understand the mechanisms of how the cost parameters and demand variance affect the benefit of the retailer's using (R, T) policy instead of (s, S) policy, by comparing decentralized and centralized systems.

Optimizing the Two-Stage Supply Chain Inventory Model with Full Information Sharing and Two Backorders Costs Using Hybrid Geometric-Algebraic Method

We consider the case of a two-stage serial supply chain system. This supply chain system involves a single vendor who supplies a single buyer with a single product. The vendor’s production rate is assumed finite. In addition, the demand at the buyer is assumed deterministic. In order to coordinate their replenishment policies and jointly optimize their operational costs, the two supply chain partners fully share their relevant information. For this purpose, we develop an integrated inventory replenishment model assuming linear and fixed backorders costs. Then, we use a hybrid geometric-algebraic method to drive the optimal replenishment policy and the minimum supply chain total cost in a closed form.

Managing a Noncooperative Supply Chain with Limited Capacity

We consider a two-stage serial supply chain with capacity limits, where each installation is operated by managers attempting to minimize their own costs. A multiple-period model is necessitated by the multiple stages, capacity limits, stochastic demand, and the explicit consideration of inventories. With appropriate salvage value functions, a Markov equilibrium policy is found. Intuitive profit dominance allows for existence of a unique equilibrium solution, which is shown to be a modified echelon base-stock policy. This equilibrium policy structure is sustained in the infinite horizon. A numerical study compares the behavior of the decentralized system with the first-best integrated capacitated system. The performance of this decentralized system relative to the integrated system across other parameters can be very good over a broad range of values. This implies that an acceptable system performance may be attained without the imposition of a contract or other coordinating mechanism, which themselves may encounter difficulties in implementation in the form of negotiation, execution, or enforcement of these agreements. We find instances where tighter capacities may actually enhance channel efficiency. We also examine the effect of capacity utilization on the system suboptimality.

Periodic flexibility, information sharing, and supply chain performance

A two-stage serial supply chain in which a retailer and his supplier are operating in make-to-stock environments and the retailer faces uncertain demands from the end-customers is studied. When this supply chain is centrally managed, the optimal policy is an extension of the Clark–Scarf echelon base stock policy. Since these supply chains are usually operated in a decentralized manner, an operational change is proposed that reduces the inefficiency associated with decentralization. The policy, which is called Periodic Flexibility (PF), provides the retailer with structural flexibility to order any amount in one period of a cycle, while requiring that the retailer receives a fixed-quantity shipment in the other periods. Optimal policies and their associated costs for the non-stationary inventory control problems faced by the retailer and the supplier under PF are characterized. A detailed computational study shows that PF improves the supply chain performance by about 11% on average. This improvement is a 43% (on average) reduction in the efficiency gap between centralized and decentralized control. The improvement of PF is due to information sharing, i.e., the retailer passing her end-customer demand information to the supplier. The PF strategy is compared to the well-known quantity flexibility scheme and it is shown that the PF approach tends to be more efficient. [Supplementary materials are available for this article. Go to the publishers online edition of IIE Transactions for the following free supplemental resources: Additional proofs]

Asynchronous action-reward learning for nonstationary serial supply chain inventory control

Action-reward learning is a reinforcement learning method. In this machine learning approach, an agent interacts with non-deterministic control domain. The agent selects actions at decision epochs and the control domain gives rise to rewards with which the performance measures of the actions are updated. The objective of the agent is to select the future best actions based on the updated performance measures. In this paper, we develop an asynchronous action-reward learning model which updates the performance measures of actions faster than conventional action-reward learning. This learning model is suitable to apply to nonstationary control domain where the rewards for actions vary over time. Based on the asynchronous action-reward learning, two situation reactive inventory control models (centralized and decentralized models) are proposed for a two-stage serial supply chain with nonstationary customer demand. A simulation based experiment was performed to evaluate the performance of the proposed two models.

Competitive and Cooperative Inventory Policies in a Two-Stage Supply Chain

We investigate a two-stage serial supply chain with stationary stochastic demand and fixed transportation times. Inventory holding costs are charged at each stage, and each stage may incur a consumer backorder penalty cost, e.g. the upper stage (the supplier) may dislike backorders at the lower stage (the retailer). We consider two games. In both, the stages independently choose base stock policies to minimize their costs. The games differ in how the firms track their inventory levels (in one, the firms are committed to tracking echelon inventory; in the other they track local inventory). We compare the policies chosen under this competitive regime to those selected to minimize total supply chain costs, i.e., the optimal solution. We show that the games (nearly always) have a unique Nash equilibrium, and it differs from the optimal solution. Hence, competition reduces efficiency. Furthermore, the two games' equilibria are different, so the tracking method influences strategic behavior. We show that the system optimal solution can be achieved as a Nash equilibrium using simple linear transfer payments. The value of cooperation is context specific: In some settings competition increases total cost by only a fraction of a percent, whereas in other settings the cost increase is enormous. We also discuss Stackelberg equilibria.