Vendor Managed Inventory Supply Chain Research Articles

Vendor-managed inventory supply chain coordination based on commitment-penalty contracts with bilateral asymmetric information

ABSTRACT Vendor Managed Inventory (VMI) is adopted to improve the efficiency of overall supply chain. Research shows that asymmetric information might cause supply chain inefficiency. This study looks at how VMI supply chain coordination can be achieved in an asymmetric information environment. We propose a commitment penalty contract to improve the performance of a VMI supply chain where the manufacturer holds private production cost information and the retailer holds private demand information. The replenishment quantity and contract under symmetric information and bilateral asymmetric information are solved respectively. We find that the commitment penalty contract may reveal the private information of the manufacturer and the retailer can coordinate the supply chain under the asymmetric information of bilateral information. We present numerical examples to illustrate the effectiveness of the contract and develop managerial guidelines.

Coordination of VMI supply chain with replenishment tactic under risk aversion and sales effort

This paper considers a vendor-managed inventory (VMI) supply chain consisting of a risk-averse supplier and a risk-neutral retailer engaging in sales promoting efforts. Under the conditional risk at value, we examine three different types of contracts with the replenishment option for coordinating the VMI supply chain, namely, a risk diversification and cost sharing (RDCS) contract, an option and cost sharing contract, and a subsidy and cost sharing contract. Firstly, we derive the optimal production strategy and analyze the capability of each of the proposed contracts in coordinating the VMI supply chain. We find that all of those contracts can achieve coordination of the supply chain with Pareto improvement. Secondly, we also show that both the supplier and the retailer prefer the RDCS contract over the other two contracts. Finally, we use numerical experiments to analyze the impact of risk aversion on contracts’ parameters.

Quality competition and coordination in a VMI supply chain with two risk-averse manufacturers

<p style='text-indent:20px;'>Quality competition and risk aversion have become more and more common in today's many industries, making it a challenge to supply chain management and coordination. This paper considers a vendor-managed inventory (VMI) supply chain comprising two risk-averse manufacturers who sell their competing products through a common retailer. Market demand shared by each manufacturer is dependent on the quality level of its own product as well as on the competitor's product quality. The Conditional Value-at-Risk (CVaR) criterion is employed to formulate the risk aversion of manufacturers. This study first develops basic models without coordination mechanism and analyzes the effect of the quality sensitivity, competition intensity, risk aversion degree and cost coefficient of quality improvement on equilibrium decisions and supply chain efficiency. Further, a combined contract composed of option and cost-sharing is proposed to investigate the supply chain coordination issue. The results reveal that the combined contract can coordinate the supply chain and achieve a win-win outcome only when the manufacturers are low in risk aversion, and the system-wide profit of the supply chain can be allocated arbitrarily only by the option price. Also, this research examines the effect of the quality sensitivity, competition intensity, risk aversion degree and cost coefficient of quality improvement on the feasible region of option price.

Enhancing Vendor Managed Inventory Supply Chain Operations Using Blockchain Smart Contracts

Supply chain networks have grown in complexity and size due to increased globalization leading to a variety of challenges and opportunities for improvement. Optimizing inventory levels and adjusting replenishment policies have significant effects on the operational performance and profitability of supply chains. Vendor Managed Inventory (VMI) is a mutually beneficial arrangement between supplier and buyer, where the supplier is responsible for making inventory and replenishment decisions based on buyers’ inventory status. Potential benefits of VMI include reducing inventories, enabling information sharing, eliminating safety stock, and reducing purchasing related costs across the supply chain. In today’s supply chains, VMI operations face critical challenges related to data integrity, transparency, traceability, and single point of failure due to its centralized architecture. Blockchain technology is a distributed ledger that ensures a transparent, safe, and secure exchange of data among supply chain stakeholders. The advantages of adopting blockchain technology for VMI operations in a supply chain include decentralized control, security, traceability, and auditable time-stamped transactions. In this paper, we present a blockchain-based approach using smart contracts to transform VMI supply chain operations. We propose a generic framework using Ethereum smart contracts and decentralized storage systems to automate the processes and information exchange and detailed algorithms that capture the interactions among supply chain stakeholders. The smart contract code was developed and tested in Remix environment. We present cost and security analysis incurred by the stakeholders in the supply chain. Adopting a blockchain-based solution to VMI operations in supply chains is economically viable and provides a streamlined, secure, trusted, and transparent mode of communication among various stakeholders.

Inventory and Production Dynamics in a Discrete-Time Vendor-Managed Inventory Supply Chain System

This paper presents some analytical results on production and order dynamics in the context of a discrete-time VMI supply chain system composed of one retailer and one manufacturer. We firstly derive the lower bound and upper bound on the range of inventory fluctuations for the retailer under unknown demand. We prove that the production fluctuations can be interestingly smoothed and stabilized independent of the delivery frequency of the manufacturer used to satisfy the retailer’s demand, even if the retailer subsystem is unstable. The sufficient and necessary stability condition for the whole supply chain system is obtained. To further explore the production fluctuation problem, the bullwhip effect under unknown demand is explored based on a transfer function model with the purpose of disclosing the influences of parameters on production fluctuations. Finally, simulation experiments are used to validate the theoretical results with respect to inventory and production fluctuations.

Coordination of VMI supply chain with a loss-averse manufacturer under quality-dependency and marketing-dependency

This paper addresses a vendor-managed inventory (VMI) supply chain with a loss-averse manufacturer and a risk-neutral retailer. Market demand faced by the retailer is stochastic and dependent on product quality level and marketing effort level. We propose a combined contract composed of option and cost-sharing to investigate coordination and profit allocation issues of the supply chain. To model loss aversion of the manufacturer, we employ multiple mental accounts and apply the utility function to upside and downside potentials of manufacturer's production decision separately. We derive the optimal strategy for each member with a Stackelberg game in which the retailer acts as the leader. It is proved that both coordination of the supply chain and Pareto-improvement can be achieved synchronously by the combined contract. In the premise of coordination, the system-wide profit can be allocated arbitrarily only by option price. Through negotiation, the retailer and the manufacturer just need to confirm an appropriate option price to obtain that neither of them becomes worse off. We also find that the manufacturer's loss aversion is a significant element for contract design and profit allocation, and the manufacturer could benefit from its own loss aversion behavior under certain condition.

BI-LEVEL PROGRAMMING APPROACH TO OPTIMAL STRATEGY FOR VENDOR-MANAGED INVENTORY PROBLEMS UNDER RANDOM DEMAND

We present an extension of vendor-managed inventory (VMI) problems by considering advertising and pricing policies. Unlike the results available in the literature, the demand is supposed to depend on the retail price and advertising investment policies of the manufacturer and retailers, and is a random variable. Thus, the constructed optimization model for VMI supply chain management is a stochastic bi-level programming problem, where the manufacturer is the upper level decision-maker and the retailers are the lower-level ones. By the expectation method, we first convert the stochastic model into a deterministic mathematical program with complementarity constraints (MPCC). Then, using the partially smoothing technique, the MPCC is transformed into a series of standard smooth optimization subproblems. An algorithm based on gradient information is developed to solve the original model. A sensitivity analysis has been employed to reveal the managerial implications of the constructed model and algorithm: (1) the market parameters of the model generate significant effects on the decision-making of the manufacturer and the retailers, (2) in the VMI mode, much attention should be paid to the holding and shortage costs in the decision-making.

Flexible contract design for VMI supply chain with service-sensitive demand: Revenue-sharing and supplier subsidy

To coordinate a VMI (vendor-managed inventory) supply chain with service-level sensitive customers, we relate retailer's service level to customer demand. We establish the dynamic game relationship under a revenue-sharing contract. A pure revenue-sharing contract, focusing on revenue-sharing ratio, often forms a competitive relationship among SC members, as the optimal sharing ratio is determined by the game leader and may fail to optimize SC performance. An improved mechanism is necessary to avoid sub-optimality. In this research, we propose three flexible subsidy contracts for VMI supply chains: (i) subsidizing all surplus products; (ii) subsidizing the inventory that exceeds the target level and unsold; (iii) subsidizing the inventory that exceeds target regardless it is sold or not. These contracts help SC members to arrive at optimal price, revenue-sharing ratio, inventory target, subsidy rate; and to commit inventory early. The proposed mechanism can better ensure SC collaboration, and bring the SC to Pareto improvement by allowing members to negotiate, share profit, subsidize suppliers for their risks, and select from alternative contracts under each VMI setting. They enhance service levels, maximize SC performance, and share SC profits fairly. Numerical examples are provided to validate our findings and to derive managerial implications.

Analysis of lead time effects on VMI system: system dynamics approach

Vendor managed inventory (VMI) is a widely used strategy for the collaborative inventory management. It is known to be a very effective strategy in terms of inventory reduction and service level improvement. And these benefits of VMI depend on the environment of supply chain such as customer demand, ordering policy and lead time. This research aims to figure out the effect of lead time on the benefit of VMI system. System dynamics approach is adopted so as to model the traditional supply chain and the VMI supply chain. Verification and validation tests are carried out to check the appropriateness of the models. To perform the experimental test under various environments, multiple scenarios considering various characteristics of lead time were developed. Some interesting findings are observed from the experimental test and their managerial implications are discussed.

Analysis of lead time effects on VMI system: system dynamics approach

Vendor managed inventory (VMI) is a widely used strategy for the collaborative inventory management. It is known to be a very effective strategy in terms of inventory reduction and service level improvement. And these benefits of VMI depend on the environment of supply chain such as customer demand, ordering policy and lead time. This research aims to figure out the effect of lead time on the benefit of VMI system. System dynamics approach is adopted so as to model the traditional supply chain and the VMI supply chain. Verification and validation tests are carried out to check the appropriateness of the models. To perform the experimental test under various environments, multiple scenarios considering various characteristics of lead time were developed. Some interesting findings are observed from the experimental test and their managerial implications are discussed.

Coordinating VMI supply chain under yield uncertainty: Option contract, subsidy contract, and replenishment tactic

The paper introduces an option contract to improve the performance of a vendor-managed inventory (VMI) supply chain under yield uncertainty. We first assume that there is a deterministic demand and find that the option contract can coordinate the supply chain and arbitrarily allocate coordinated profit among members. Compared with a subsidy contract, we then investigate the relationship between the option contract and the subsidy contract in the paper. When a replenishment tactic is considered in the model, supply chain coordination and Pareto improvement can be achieved by introducing the option contract. The paper further shows that the option contract also can effectively coordinate the supply chain when demand is stochastic. Numerical studies are conducted to illustrate our models and validate our findings.

Introducing the Time Value of Money in a Non-consignment Vendor Managed Inventory Model

Vendor managed inventory (VMI) is an integrated approach for buyer–vendor coordination, according to which the vendor (supplier or manufacturer) decides on the appropriate buyer’s (retailer’s) inventory levels. The time value of money has not traditionally been considered in evaluating VMI supply chain’s total inventory cost in any studies up to now. Therefore, in the present study a new model for two-echelon single-manufacturer multi-retailer supply chain under non-consignment VMI program by considering time value of money is proposed. In order to take the time value of money into consideration, the present value of each inventory cost is evaluated in a single period and generalized to infinity horizon and then is transformed to the equivalent annual cost. This model also explicitly includes contractual agreements between the manufacturer and each retailer. Under this type of contracts, an upper bound on each retailer’s inventory level is placed such that the manufacturer is penalized for items exceeding this bound. At the end, a sensitivity analysis is conducted to study effects of key parameters on the optimal solution and validate the proposed model.

Optimizing Cooperative Advertizing, Profit Sharing, and Inventory Policies in a VMI Supply Chain: A Nash Bargaining Model and Hybrid Algorithm

Members in a vendor managed inventory (VMI) supply chain make joint decisions on inventory policy and cooperative advertizing by capitalizing on their interactions. However, very few investigations have been reported to develop methods to facilitate such joint decision making due to the modeling difficulty and computation complexity. This study is, thus, to address the joint VMI, cooperative advertizing, and profit-sharing decision making in a coordinative way. It considers a two-level VMI supply chain including a manufacturer and m retailers, and deals with many decisions, e.g., chain members’ advertizing investments profit sharing. A nonlinear mixed integer Nash bargaining model is developed to model the complex joint decision making of ( m +1) players. In view of the difficulties in model solving, this study further develops a solution methodology, including an integrated model and hybrid algorithm for obtaining optimal solutions. Thanks to the integrated model, the hybrid algorithm, which is developed based on analytical methods, a genetic algorithm, and a Lagrange multiplier method, obtains optimal solutions to the Nash bargaining model while greatly reducing computation complexity. Numerical examples demonstrate the validity of the Nash bargaining model and the effectiveness of the solution methodology. Finally, a number of managerial implications are drawn based on sensitivity analysis.

The impact of cooperation mechanism on the chaotic behaviours in nonlinear supply chains

The chaotic behaviours of nonlinear inventory systems are investigated via control theory, nonlinear system theory, and chaos theory. Two well-established production and distribution scheduling algorithms, automatic pipeline, variable inventory and order-based production control system (APVIOBPCS) with demand information sharing (DIS) and vendor managed inventory (VMI) supply chains are adopted and analysed by bifurcation diagram and Lyapunov exponent spectrum. Simulation results show that with the target inventory parameter ([2) changes, behaviours of the DIS and VMI systems are different from that of traditional inventory system. The Lyapunov exponent confirms that behaviours of DIS supply chain members shift between chaotic and periodic movement with [2 increases. The system shows periodic movement when [2 is set between 1 and 1.5. For VMI system, the system is not sensitive to [2 and keeps periodic movement. Therefore, the DIS and VMI supply chain members can effectively reduce inventory, and keep periodic movement simultaneously. [Received 13 July 2013; Revised 13 January 2014; Revised 10 June 2014; Accepted 14 June 2014]

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).

The optimal replenishment policy for time-varying stochastic demand under vendor managed inventory

Abstract A Vendor Managed Inventory (VMI) partnership places the responsibility on the vendor (rather than on buyers) to schedule purchase orders for inventory replenishment in the supply chain system. In this research, the supply chain network considers the Silver–Meal heuristic with an augmentation quantity replenishment policy between both traditional and VMI systems. We consider time-varying stochastic demand in two-echelon (one vendor, multiple retailers) supply chains. This paper seeks to find the supply chain that minimizes system cost through comparing performance between traditional and VMI systems. A mathematical model is developed, and total supply chain cost is used as the measure of comparison. The models are applied in both traditional and VMI supply chains based on pharmaceutical industry data, and we focus on total cost difference compared through the use of Adjusted Silver–Meal (ASM) and Least Unit Cost heuristics. Finally, a numerical example and a sensitivity analysis are also illustrated to show the applicability of the model.

A Novel Continuous KNN Prediction Algorithm to Improve Manufacturing Policies in a VMI Supply Chain

This paper examines and compares various manufacturing policies which manufacturer may adopt so as to improve the performance of a vendor managed inventory (VMI) partnership. The goal is to maximize the combined cumulative profit of supply chain while minimizing relevant inventory management costs. The supply chain is a two-level system with a single manufacturer and single retailer at each level, in which the manufacturer takes the responsibility of overall inventories of supply chain. A base system dynamics (SD) simulation model is first employed to describe the dynamic interactions between the variables and parameters of manufacturer and retailer under VMI. Then, the mentioned policies are constructed using the base SD model that lead us to differentiate the behavior supply chain for each policies within a same duration of time. In this paper, we use continuous K-nearest neighbor (CKNN) as one of the instance-based learning methodologies to predict the best manufacturing rates. This algorithm effectively increases the combined profit of supply chain in comparison with other two policies. Accordingly, a numerical example along with a number of sensitivity analyses is conducted to evaluate the performance of mentioned policies.

Service investment and consumer returns policy in a vendor-managed inventory supply chain

This paper develops a game model of a vendor-managed inventory supply chain consisting of one manufacturer and one retailer to study the manufacturer's consumer returns policy and the retailer's store assistance service decision, and explore the effects of both supply chain decentralization and the service subsidy rate on the consumer returns policy. We find that when the consumer return is not allowed, the retailer would like to sell the products to only the consumers with low mismatching loss if the market scale is sufficiently low. Allowing consumer return decreases the retail price if the basic returns rate is sufficiently high and decreases the unit wholesale price if the subsidy rate of service investment is sufficiently high. Moreover, allowing consumer return decreases the service level but increases the subsidy rate. The expected loss of mismatching can reverse the effects of both the subsidy rate and the service cost factor on the returns policy; the manufacturer allows consumer return if the basic mismatching rate is not too high. In addition, we find that the store's returns handling cost increases the effect of supply chain decentralization on returns policy while the loss of mismatching for the high-type consumer decreases it.

An Inventory Model for Deteriorating Items Using Vendor-Managed Inventory Policy

In recent researches, vendor managed inventory (VMI) policy is rarely considered for deteriorating items. This study considered the supply chain partner’s collaboration via a VMI system and provided an EOQ model for a two-level supply chain (single supplier - single retailer) to examine the inventory management proceedings for VMI and non-VMI supply chains. By a new approach in modeling, the equations are simplified. The demand is stock-dependent and deterioration and backlogging rates are assumed to be constant. A comparison between the cost of two mentioned cases is done in this article. The results of analytical examination of inventory costs with and without VMI show that VMI system in our proposed model always has the ability to reduce total costs of the supply chain and is always better than the traditional one. Some numerical examples and sensitive analysis are also given to illustrate the application of the proposed model.

Optimal decisions and comparison of VMI and CPFR under price-sensitive uncertain demand

Purpose: The purpose of this study is to compare the performance of two advanced supply chain coordination mechanisms, Vendor Managed Inventory (VMI) and Collaborative Planning Forecasting and Replenishment (CPFR), under a price-sensitive uncertain demand environment, and to make the optimal decisions on retail price and order quantity for both mechanisms. Design/ methodology/ approach: Analytical models are first applied to formulate a profit maximization problem; furthermore, by applying simulation optimization solution procedures, the optimal decisions and performance comparisons are accomplished. Findings: The results of the case study supported the widely held view that more advanced coordination mechanisms yield greater supply chain profit than less advanced ones. Information sharing does not only increase the supply chain profit, but also is required for the coordination mechanisms to achieve improved performance. Research limitations/implications: This study considers a single vendor and a single retailer in order to simplify the supply chain structure for modeling. Practical implications: Knowledge obtained from this study about the conditions appropriate for each specific coordination mechanism and the exact functions of coordination programs is critical to managerial decisions for industry practitioners who may apply the coordination mechanisms considered. Originality/value: This study includes the production cost in Economic Order Quantity (EOQ) equations and combines it with price-sensitive demand under stochastic settings while comparing VMI and CPFR supply chain mechanisms and maximizing the total profit. Although many studies have worked on information sharing within the supply chain, determining the performance measures when the demand is price-sensitive and stochastic was not reported by researchers in the past literature.