Environmental determinants of VMI adoption: An exploratory analysis

This study empirically examines the determinants of adoption of Vendor Managed Inventory programs that have recently gained popularity in many industries. To achieve this goal, survey scales are adapted and developed for buyer and supplier market competitiveness, product demand, buyer operational uncertainty, and buyer–supplier cooperation. Based on the analysis of responses from purchasing managers in three industries, structural equation modeling results suggest that the competitiveness of the supplier’s market and buyer–supplier cooperation are positively associated with VMI adoption, while operational uncertainty for the buyer is negatively associated with VMI adoption. Managerial implications and limitations of the study are also noted.     

Multiple sourcing under supplier failure risk and quantity discount: A genetic algorithm approach

This paper investigates an order allocation problem of a manufacturer/buyer among multiple suppliers under the risks of supply disruption. A mixed integer non-linear programming (MINLP) model is developed for order allocation considering different capacity, failure probability and quantity discounts for each supplier. We have shown that the formulated problem is NP-hard in nature and genetic algorithm (GA) approach is used to solve it. The model is illustrated through a numerical study and the result portrays that the cost of supplier has more influence on order quantity allocation rather than supplier’s failure probability.     

Impact of decision sequence on reliability enhancement with supply disruption risks

This paper considers a supply chain that consists of a manufacturer and a supplier who faces disruption risks. We investigate the impact of decision sequence on the supplier’s endogenous reliability enhancement and the firms’ equilibrium pricing strategies. The supply chain reliability achieves a higher level under the supplier–leader game, but this does not always lead to a higher payoff for the supply chain. Each firm prefers to make the decision first, while any decision sequence can become dominant for the supply chain. We also show that the supply chain can achieve coordination via the revenue sharing contract.     

Diversification strategy with random yield suppliers for a mean–variance risk-sensitive manufacturer

We consider the diversification strategy for a mean–variance risk-sensitive manufacturer with unreliable suppliers. We first analyze the linear model and find that the suppliers are selected according to the descending order of their contributed marginal expected profit, and increasing the manufacturer’s risk-averseness leads to a more even allocation of demand across the suppliers. Then, we study the general newsvendor model. By approximating the leftover inventory with a normal distribution, we establish the general properties of the active supplier set and show that the supplier selection rule is similar to that under the risk-neutral setting when the demand uncertainty is large. Moreover, we conjecture that the selection rule also applies when the demand uncertainty is low, which we verify with an extensive numerical study. Our paper makes two contributions: First, we establish the properties of the optimal diversification strategy and develop corresponding insights into the trade off between cost and reliability under the mean–variance framework. Second, we perform comparative statics on the optimal solution, with a particular emphasis on investigating how changes in the supplier’s cost or reliability affect the risk-averse manufacturer’s ordering decisions and customer service level.     

Price,capacity and concession period decisions of Pareto-efficient BOT contracts with demand uncertainty

In this paper, we study the impact of demand uncertainty on the build-operate-transfer (BOT) contract design by optimizing a bi-objective problem via three critical decisions: toll, capacity and concession period. We derive the optimums and identify the public and private sector’s economic incentives. We find that the optimal length of concession period and the service quality of the infrastructure depend on the two parties’ operational costs and negotiation powers. Under mild conditions, we prove that the government will build a larger capacity but charge less than the private sector. Furthermore, the efficiency of BOT contract is improved with demand uncertainty.     

Managing the risks of outsourcing: Time,quality and correlated costs

Cost, quality and time to market are three main factors for outsourcing management. A game theoretic model is used to design optimal outsourcing contracts including these three factors for a buyer and a supplier under Full Information (F) case and Asymmetric Information (A) case where the buyer does not share her internal variable cost information with the supplier. Optimal outsourcing contracts are derived and results of numerical experiment are also presented. Several insights of managing the outsourcing risks due to the Asymmetric Information are given for various industries, like cost-sensitive industry, time-sensitive industry, and quality-sensitive industry.     

A supply chain model of vendor managed inventory

This research evaluates how vendor managed inventory (VMI) affects a supply channel. Specifically, VMI always leads to a higher buyer's profit, but supplier's profit varies. In the short-term, VMI is found to reduce total costs of the channel system, but under certain cost conditions between buyer and supplier, it could decrease the purchasing price and supplier's profit. In the long-run, it could more likely increase supplier's profit than in the short-run. Finally, VMI is an effective supply chain strategy that can realize many of the benefits obtainable only in a fully integrated supply chain.     

Benders’ decomposition for concurrent redesign of forward and closed-loop supply chain network with demand and return uncertainties

This paper attempts to design a reverse supply chain network (SCN), add it to an existing multi-product forward SCN and simultaneously redesign the existing forward supply chain (SC). The problem considers uncertainty on products demand and and also returned products in multi-period context. Benders’ decomposition is applied to solve the stochastic mixed-integer model to optimality. The scenarios are generated based on the demand distribution function using Cholesky’s factorization method to consider correlation among different products’ demands. To decrease the computational effort, the number of scenarios is reduced using k-means clustering algorithm. The method is tested on a cell phone SC.     

Green Supply Chain Collaboration implementation in China: The mediating role of guanxi

This study aims to understand how buyer–seller relationship, competitive environment and guanxi affect Chinese manufacturers’ decision to implement Green Supply Chain Collaboration (GSCC). We also examine whether guanxi is able to mediate the buyer–seller relationship and GSCC implementation. Data collected from 222 Chinese manufacturing organizations were analyzed using the partial least squares method of structural equation modeling. The result shows that buyer–seller relationship influences Green Supply Chain Collaboration through asset specificity, volume uncertainty, transaction frequency and competitive environment. The results also showed support for our hypotheses that guanxi mediates the effect of asset specificity, volume uncertainty and environmental competition on GSCC.     

Post-disaster grain supply chain resilience with government aid

Assessing the disruption and resilience of the agricultural grain supply chain is critical to ensure grain supply and stabilize grain price in the final market. This research proposes a quantitative model to analyze how a grain processor regains robustness when supply is disrupted by a natural disaster upstream, and how this disruption affects grain retailers downstream. Two supply chain recovery methods, contingent sourcing and government aid, are considered for grain processor recovery. The results show that (1) a processor prefers timely full recovery, and (2) government aid as an intervention means is indispensable but cannot fully replace the backup supplier.     

Reprint of “Green Supply Chain Collaboration implementation in China: The mediating role of guanxi”

This study aims to understand how buyer–seller relationship, competitive environment and guanxi affect Chinese manufacturers’ decision to implement Green Supply Chain Collaboration (GSCC). We also examine whether guanxi is able to mediate the buyer–seller relationship and GSCC implementation. Data collected from 222 Chinese manufacturing organizations were analyzed using the partial least squares method of structural equation modeling. The result shows that buyer–seller relationship influences Green Supply Chain Collaboration through asset specificity, volume uncertainty, transaction frequency and competitive environment. The results also showed support for our hypotheses that guanxi mediates the effect of asset specificity, volume uncertainty and environmental competition on GSCC.     

Robust optimization for United States Department of Agriculture food aid bid allocations

The U.S. Department of Agriculture (USDA) currently uses a bidding system to determine carriers and suppliers that would partner in providing food aid annually in response to global emergencies and famine. We mimic the USDA approach via a robust optimization model featuring box and ellipsoid uncertainty frameworks to account for uncertainties in demand, supplier and carrier bid prices. Through a case study utilizing historical invoice data, we demonstrate our model applicability in improving ocean carrier and food supplier bid pricing strategy and similar supply chain network optimization problems. Through a validation algorithm we demonstrate the value of our robust models.     

An integrated vendor–buyer model with stock-dependent demand

We develop an integrated vendor–buyer model for a two-stage supply chain. The vendor manufactures the product and delivers it in a number of equal-sized batches to the buyer. The items delivered are presented to the end customers in a display area. Demand is assumed to be positively dependent on the amount of items displayed. The objective is to maximize total supply chain profit. The numerical analysis shows that buyer–vendor coordination is more profitable in situations when demand is more stock dependent. It also shows that the effect of double marginalization provides a link between the non-coordinated and the coordinated case.     

Optimizing shipment,ordering and pricing policies in a two-stage supply chain with price-sensitive demand

In this paper we focus on a two-stage supply chain consisting of one vendor and one buyer. We develop an integrated production–inventory–marketing model to determine the relevant profit-maximizing decision variable values. The model proposed is based on the joint total profit of both the vendor and the buyer, and it finds out the optimal ordering, shipment and pricing policies. We are able to ascertain the optimal decision variable values employing an analytical solution procedure. The numerical evidence suggests that it is more beneficial for the buyer and the vendor to cooperate with each other when the demand is more price sensitive.     

On the use of RFID in the management of reusable containers in closed-loop supply chains under stochastic container return quantities

This paper studies a closed-loop supply chain that uses containers for transporting products from a supplier to a retailer. At the retailer, used containers are collected and returned to the supplier. The supplier inspects returned containers, and either repairs and reuses or disposes them. This paper studies the case where the fraction of containers that are returned to the supplier is stochastic, and where an RFID system can be used to support the tracking of container positions in the supply chain. The use of RFID leads to improved information on the return of containers and better return predictability as well as higher return rates, which we model as an increase in the mean return rate of containers and a reduction in return variance. The paper first develops a mathematical planning model for this scenario, and it then studies how the use of RFID impacts the performance of the system. In addition, it analyzes under which conditions the use of RFID is economical, and in which situations traditional container systems should be preferred.     

Determining flight frequencies on an airline network with demand–supply interactions

This paper determines flight frequencies on an airline network with demand–supply interactions between passenger demand and flight frequencies. The model consists of two submodels, a passenger airline flight choice model and an airline flight frequency programming model. The demand–supply interactions relevant to determining flight frequency on an airline’s network are analyzed by integrating these two submodels. The necessary condition for the convergence of the demand–supply interaction is discussed. An example demonstrates the feasibility of applying the proposed models. The results are more accurate than those obtained without considering demand–supply interactions, and the models provide ways to consider demand–supply interactions well in advance to determine flight frequencies on an airline network.     

The impacts of Radio Frequency Identification (RFID) technology on supply chain costs

Radio Frequency Identification (RFID) is regarded as a promising technology for the optimization of supply chain processes since it improves manufacturing and retail operations from forecasting demand to planning, managing inventory, and distribution. This study uses a simulation model to calculate the expected benefits of an integrated RFID system on a three-echelon supply chain obtained through performance increases in efficiency, accuracy, visibility, and security level. The study investigates how the product value, lead time, and demand uncertainty affect the performance of the integrated RFID supply chain in terms of cost factors at the echelon level.     

A three-level supply chain network design model with risk-pooling and lead times

This paper considers a single-sourcing network design problem for a three-level supply chain consisting of suppliers, distribution centers (DC’s) and retailers, where risk-pooling strategy and DC-to-supplier dependent lead times are considered. The objective is to determine the number and locations of suppliers and DC’s, the assignment of each location-fixed DC to a supplier and that of each retailer to a DC, which minimizes the system-wide location, transportation, and inventory costs. The problem is formulated as a nonlinear integer programming model, for which a two-phase heuristic solution algorithm is derived based on the Lagrangian relaxation approach. Numerical experiments show that the proposed heuristic is effective and also efficient.     

Transportation disruption risk management: business interruption insurance and backup transportation

Business interruption (BI) insurance and backup transportation have been widely used in distribution centers’ daily risk management. If disruption occurs, a firm (distribution center) can exert efforts to resume its transportation, although its unit transportation cost during the recovery process is uncertain. This paper studies how ex ante BI insurance can affect the ex post transportation recovery, and compares BI insurance with the ex post action—backup transportation. We investigate four strategies: basic strategy, BI insurance strategy, backup transportation strategy, and mixed strategy (integration of the last two). The distribution center that seeks the least profit loss prefers the mixed strategy to other three strategies. However, the mixed strategy might in turn require longer transportation recovery time than the BI insurance strategy. The BI insurance and transportation recovery are complementary, but the BI insurance and backup transportation are substitutable, the backup transportation and the transportation recovery are also substitutable. We also find that the choice of BI insurance strategy and the backup transportation strategy depends on transportation market, insurance market and distribution center’s operational environments.     

How does demand uncertainty affect food and beverage capacity in the hotel industry?

This study investigates the effect of demand uncertainty on hotels’ food and beverage (F&B) capacity using the operation data of international tourist hotels in Taiwan. The empirical results of this study show that demand uncertainty leads to an increase in F&B capacity. Moreover, the magnitude of this effect increasingly strengthens for hotels with larger F&B scales. Our results together with other studies on room capacity collectively indicate that hotels’ overcapacity problem resulting from demand uncertainty considerably varies with hotel scales and between different hotel sectors.