An analytical study of the modification ability of distribution centers

This paper considers a two-level vendor managed inventory (VMI) system comprising a distribution center (DC) and a retailer. Both the DC’s and the retailer’s replenishment decisions follow the order-up-to-level policy and aim at maximizing the profit of the overall system. We critically examine the potential of the DC’s ability to modify delivery decisions, identify and quantify the cost factors that influence the DC’s modification ability, establish a relationship between the DC’s location and its modification ability, and show the trade-off between the DC’s modification ability and related costs. Our analysis provides a new insight into the role of the DC and reveals the full potential of the VMI system. Our findings and their practical implications, demonstrated with the aid of computational examples, are helpful for enhancing the practice of VMI at both strategic and operational levels.     

Managing supply chain backorders under vendor managed inventory: An incentive approach and empirical analysis

This paper shows how a manufacturer may use an incentive contract with a distributor under a VMI arrangement to gain market share. The manufacturer promises a distributor lower inventory levels in exchange for efforts by the distributor to convert potential lost sales due to stockouts to backorders. Data gathered from a third party provider of information services are then used to illustrate that this incentive arrangement may, at least implicitly, be employed in industry. Our data estimations show that when a manufacturer and distributor are operating under a VMI arrangement, lower inventory at the distributor is associated with a higher conversion rate of lost sales stockouts to backorders.     

Delivery performance in vendor selection decisions

Increased reliance upon outsourcing has made the issue of vendor selection even more critical to the success of the modern manufacturing organization. The usual performance measure on which selection is based has been the distribution of the vendor’s delivery lead-time (LT), often as characterized by the mean and variance. In this paper, we show that the distribution of demand per unit time (DPUT) must also be considered if an optimal decision is to be made.     

A distributed coordination mechanism for supply networks with asymmetric information

The paper analyses the problem of coordination in supply networks of multiple retailers and a single supplier, where partners have asymmetric, private information of demand and costs. After stating generic requirements like distributedness, truthfulness, efficiency and budget balance, we use the apparatus of mechanism design to devise a coordination mechanism that guarantees the above properties in the network. The resulting protocol is a novel realisation of the widely used Vendor Managed Inventory (VMI) where the responsibility of planning is at the supplier. We prove that together with the required generic properties a fair sharing of risks and benefits cannot be guaranteed. We illustrate the general mechanism with a detailed discussion of a specialised version, assuming that inventory planning is done according to the newsvendor model, and explore the operation of this protocol through computational experiments.     

企业选择SaaS 供应商的决策及实例研究

选择合适的SaaS供应商是很多企业信息化顺利进行的保障．本文建立了合理的SaaS供应商选择评价指标体系及SaaS供应商选择的多层次评估模型,阐述了基于层次分析法综合评估SaaS供应商的过程,并将该方法应用于某一企业,选择出了恰当的SaaS供应商．     

A robust optimization approach to reduce the bullwhip effect of supply chains with vendor order placement lead time delays in an uncertain environment

Supply chain management is important for companies and organizations to improve their business and enhance competitiveness in the global marketplace. The bullwhip effect problem of supply chain systems with vendor order placement lead time delays in an uncertain environment is addressed in this paper. Among the numerous causes of bullwhip effect, we focus on uncertainties with respect to demand, production process, supply chain structure, inventory policy implementation and especially vendor order placement lead time delays. Minimizing the negative effect of these uncertainties in inducing bullwhip effect creates a need for developing dynamical inventory policy that increases responsiveness to demand and decreases volatility in inventory replenishment. First, a dynamic model of supply chain with above uncertainties is developed. Then, a novel uncertainty-dependent robust inventory control method using inventory position information is proposed. Additionally, the maximum allowable vendor order placement lead time delay that ensures the stability of supply chains and the suppression of bullwhip effect under the proposed inventory control policy is explored and measured. We find that vendor order placement lead time delays do play important role in supply chain dynamics and contribute to its turbulence and volatility. The effectiveness and flexibility of proposed method is verified through simulation study.     

Fuzzy multi-objective vendor selection under lean procurement

This paper investigates a fuzzy multi-objective vendor selection program under lean procurement based on cost minimization, delivery schedule violation minimization, and maximizing the quality level of the purchased quantity. Specifically, the paper incorporates the vendor production capacity uncertainty into the model to identify an appropriate selection policy for vendors under practical operating conditions. The use of a soft time-window mechanism for the vendor selection model enables decision makers to further incorporate a time based performance metric for vendor evaluation, based on the degree of urgency or need for a part. A solution algorithm using fuzzy AHP is proposed. The results of a numerical example suggest that decision makers prefer vendors who can promise tighter delivery schedules rather than on cost or quality. A sensitivity analysis of the soft time-window on the achievement of the lean procurement objectives is also conducted.     

An efficient and simple model for multiple criteria supplier selection problem

Simply looking for vendors offering the lowest prices is not “efficient sourcing” any more. Selection of suppliers is a multiple criteria decision. We propose a weighted linear program for the multi-criteria supplier selection problem. In addition to mathematical formulation, this paper studies a transformation technique which enables our proposed model to be solved without an optimizer. The model for multi-criteria supplier selection problem can be easily implemented with a spreadsheet package. The model can be widely applied to practical situations and does not require the user with any optimization background.     

Optimal operational service levels in vendor managed inventory contracts - an exact approach

Vendor Managed Inventory (VMI) contracts are anchored on a fill rate at which the vendor is expected to meet the end-customer demand. Violations of this contracted fill rate due to excess and insufficient inventory are both penalized, often in a linear, but asymmetric manner. To minimize these costs, the vendor needs to maintain an operational fill rate that is different from the contracted fill rate. We model, analyze and solve an optimization problem that determines this operational fill rate and the associated optimal inventory decision. We establish that, for some special, yet popular, models of demand (e.g. truncated normal, gamma, Weibull and uniform distributions), the optimal solution can be derived in closed form and computed precisely. For other demand distributions, either the optimization problem becomes ill-defined or we may need to use approximate solution methods. An extensive computational study reveals that, for realistic values of problem parameters, the operational fill rate is often larger (by as much as 20%) than the contracted service level, possibly explaining the inventory glut commonly observed in real-world VMI systems.     

Stochastic vendor selection problem: chance-constrained model and genetic algorithms

We study a vendor selection problem in which the buyer allocates an order quantity for an item among a set of suppliers such that the required aggregate quality, service, and lead time requirements are achieved at minimum cost. Some or all of these characteristics can be stochastic and hence, we treat the aggregate quality and service as uncertain. We develop a class of special chance-constrained programming models and a genetic algorithm is designed for the vendor selection problem. The solution procedure is tested on randomly generated problems and our computational experience is reported. The results demonstrate that the suggested approach could provide managers a promising way for studying the stochastic vendor selection problem. The authors would like to thank the referees for providing constructive comments that led to an improved version of the paper. Also, this research was partially supported by grants from National Natural Science Foundation (60776825)—China, 863 Programs (2007AA11Z208)—China, Doctorate Foundation (20040004012)—China, Villanova University Research Sabbatical Fall 2006, and the National Science Foundation (0332490)—USA.