Overview and classification of coordination contracts within forward and reverse supply chains

Among coordination mechanisms, contracts are valuable tools used in both theory and practice to coordinate various supply chains. The focus of this paper is to present an overview of contracts and a classification of coordination contracts and contracting literature in the form of classification schemes. The two criteria used for contract classification, as resulted from contracting literature, are transfer payment contractual incentives and inventory risk sharing. The overview classification of the existing literature has as criteria the level of detail used in designing the coordination models with applicability on the forward and reverse supply chains.Coordination contracts; forward supply chain; reverse supply chain

On the design for flexibility of manufacturing systems : a stochiastic approach

Flexibility has emerged as one of the most strategic imperatives for company viability in today\u27s fast paced economy. This realization has stimulated extensive research efforts in this area most of which have focused mainly on defining flexibility and its attributes, the need for flexibility and how to measure it. Nevertheless, despite the considerable amount of publications regarding flexibility and its related subjects, insufficient attention has been given to the optimality of the design for flexibility and the inherent needs to meet uncertainty. Bridging this gap is the intent of this work. In this dissertation, developed analytical models are for the optimum design of flexible systems. The models introduced are based on extensions of the single period stochastic inventory model and real option theory to determine the optimum level of the various flexibility attributes that are required to meet the needs of a concern in an uncertain environment. Our premise stems from the fact that flexibility does not come at no cost. That is, when designing a system, the more flexibility built in it, the more the cost that will be incurred to maintain it. On the other hand, if the system is designed with low levels of flexibility, it may not be able to meet the uncertain demand, therefore causing loss of future revenue. The developed models, then, are applied to examples where data are obtained from machine tool manufacturers to show how to strike a balance between the two conflicting scenarios of over and under-flexible designs

A Multiproduct Single-Period Inventory Management Problem under Variable Possibility Distributions

In multiproduct single-period inventory management problem (MSIMP), the optimal order quantity often depends on the distributions of uncertain parameters. However, the distribution information about uncertain parameters is usually partially available. To model this situation, a MSIMP is studied by credibilistic optimization method, where the uncertain demand and carbon emission are characterized by variable possibility distributions. First, the uncertain demand and carbon emission are characterized by generalized parametric interval-valued (PIV) fuzzy variables, and the analytical expressions about the mean values and second-order moments of selection variables are established. Taking second-order moment as a risk measure, a new credibilistic multiproduct single-period inventory management model is developed under mean-moment optimization criterion. Furthermore, the proposed model is converted to its equivalent deterministic model. Taking advantage of the structural characteristics of the deterministic model, a domain decomposition method is designed to find the optimal order quantities. Finally, a numerical example is provided to illustrate the efficiency of the proposed mean-moment credibilistic optimization method. The computational results demonstrate that a small perturbation of the possibility distribution can make the nominal optimal solution infeasible. In this case, the decision makers should employ the proposed credibilistic optimization method to find the optimal order quantities

Virtual transshipments and revenue-sharing contracts in supply chain management

This dissertation presents the use of virtual transshipments and revenue-sharing contracts for inventory control in a small scale supply chain. The main objective is to maximize the total profit in a centralized supply chain or maximize the supply chain\u27s profit while keeping the individual components\u27 incentives in a decentralized supply chain. First, a centralized supply chain with two capacitated manufacturing plants situated in two distinct geographical regions is considered. Normally, demand in each region is mostly satisfied by the local plant. However, if the local plant is understocked while the remote one is overstocked, some of the newly generated demand can be assigned to be served by the more remote plant. The sources of the above virtual lateral transshipments, unlike the ones involved in real lateral transshipments, do not need to have nonnegative inventory levels throughout the transshipment process. Besides the theoretical analysis for this centralized supply chain, a computational study is conducted in detail to illustrate the ability of virtual lateral transshipments to reduce the total cost. The impacts of the parameters (unit holding cost, production cost, goodwill cost, etc.) on the cost savings that can be achieved by using the transshipment option are also assessed. Then, a supply chain with one supplier and one retailer is considered where a revenue-sharing contract is adopted. In this revenue-sharing contract, the retailer may obtain the product from the supplier at a less-than-production-cost price, but in exchange, the retailer must share the revenue with the supplier at a pre-set revenuesharing rate. The objective is to maximize the overall supply chain\u27s total profit while upholding the individual components\u27 incentives. A two-stage Stackelberg game is used for the analysis. In this game, one player is the leader and the other one is the follower. The analysis reveals that the party who keeps more than half of the revenue should also be the leader of the Stackelberg game. Furthermore, the adoption of a revenue-sharing contract in a supply chain with two suppliers and one retailer under a limited amount of available funds is analyzed. Using the revenue-sharing contract, the retailer pays a transfer cost rate of the production cost per unit when he obtains the items from the suppliers, and shares the revenue with the suppliers at a pre-set revenue-sharing rate. The two suppliers have different transfer cost rates and revenue-sharing rates. The retailer will earn more profit per unit with a higher transfer cost rate. How the retailer orders items from the two suppliers to maximize his expected profit under limited available funds is analyzed next. Conditions are shown under which the optimal way the retailer orders items from the two suppliers exists

Open source solution approaches to a class of stochastic supply chain problems

This research proposes a variety of solution approaches to a class of stochastic supply chain problems, with normally distributed demand in a certain period of time in the future. These problems aim to provide the decisions regarding the production levels; supplier selection for raw materials; and optimal order quantity. The typical problem could be formulated as a mixed integer nonlinear program model, and the objective function for maximizing the expected profit is expressed in an integral format. In order to solve the problem, an open source solution package BONMIN is first employed to get the exact optimum result for small scale instances; then according to the specific feature of the problem a tailored nonlinear branch and bound framework is developed for larger scale problems through the introduction of triangular approximation approach and an iterative algorithm. Both open source solvers and commercial solvers are employed to solve the inner problem, and the results to larger scale problems demonstrate the competency of introduced approaches. In addition, two small heuristics are also introduced and the selected results are reported

Impact of Back up Quantity Contract on Two-level Supply Chain: A Simulation Approach

In this new era of Supply chain coordination contracts are offered and accepted according to the varying need and specification of the industry and business in discussion. The contract variations arise according to the circumstances and adaptability by both manufacturer and retailer. An important challenge faced by the contracts being offered is the adaptability and robustness when demand observed is different from the forecast. Because of stochastic and uncertain demand, retailer faces lost sales and eventually loses revenue within the same horizon. This paper discusses a back up quantity contract for a single season in which retailer orders for one-shot inventory ordering. Manufacturer retains some part of the ordered inventory as backup and provides the units at first stage. If stock at retailer lags behind the demand, he gets the backup quantity otherwise he pays some agreed upon nominal price to manufacturer in case that inventory is not at all required. We assumed the case of single manufacturer and single retailer. If units are not required, the risk of holding inventory lies with manufacturer and salvaged at zero. The strategy is suitable for businesses having short seasonal products and high demand variability. We used Monte Carlo simulation for analyzing lost sales and supply chain profit scenario and used worst case distribution and normal distribution to validate the Pareto improving contractual relationship

An Integrated Strategy for a Production Planning and Warehouse Layout Problem: Modeling and Solution Approaches

We study a real-world production warehousing case, where the company always faces the challenge to find available space for their products and to manage the items in the warehouse. To resolve the problem, an integrated strategy that combines warehouse layout with the capacitated lot-sizing problem is presented, which have been traditionally treated separately in the existing literature. We develop a mixed integer linear programming model to formulate the integrated optimization problem with the objective of minimizing the total cost of production and warehouse operations. The problem with real data is a large-scale instance that is beyond the capability of optimization solvers. A novel Lagrangian relax-and-fix heuristic approach and its variants are proposed to solve the large-scale problem. The preliminary numerical results from the heuristic approaches are reported

Capacity Planning with Financial and Operational Hedging in Low‐Cost Countries

The authors of this paper outline a capacity planning problem in which a risk-averse firm reserves capacities with potential suppliers that are located in multiple low-cost countries. While demand is uncertain, the firm also faces multi-country foreign currency exposures. This study develops a mean-variance model that maximizes the firm’s optimal utility and derives optimal utility and optimal decisions in capacity and financial hedging size. The authors show that when demand and exchange rate risks are perfectly correlated, a risk- averse firm, by using financial hedging, will achieve the same optimal utility as a risk-neutral firm. In this paper as well, a special case is examined regarding two suppliers in China and Vietnam. The results show that if a single supplier is contracted, financial hedging most benefits the highly risk-averse firm when the demand and exchange rate are highly negatively related. When only one hedge is used, financial hedging dominates operational hedging only when the firm is very risk averse and the correlation between the two exchange rates have become positive. With both theoretical and numerical results, this paper concludes that the two hedges are strategic tools and interact each other to maximize the optimal utility