Vendor Managed Inventory System Research Articles

Factors Affecting the Management of Warehouse Inventory through the Vendor Managed Inventory (VMI) System in Superstore Businesses in the Bangkok Metropolitan Area

Factors Affecting the Management of Warehouse Inventory through the Vendor Managed Inventory (VMI) System in Superstore Businesses in the Bangkok Metropolitan Area

UX Design for an E-commerce Application: A Design Thinking Perspective

The development of e-commerce applications has revolutionized the way people conduct business, but challenges still exist in designing applications that align with user needs and expectations. This study aims to document the design-thinking (DT) approach used in the development of a customized e-commerce application based on the user's perspective, with the goal of extracting the user experience of traditional commerce processes to identify its features. In this study, we focused on the first four phases of DT, which include empathize, define, ideate, and prototype, to gather insights and understand the experiences, needs, pain points, and solutions of the local producers. The data gathered from interviews and observations was subjected to content analysis to identify patterns and themes related to desired features. After analysis, the researchers identified key app features, including integrating data analytics for product performance and demand forecasting, introducing a peer-vendor marketplace, a vendor-managed inventory management system, and a product verification system for authenticity. A mockup prototype design was created and presented to users for feedback. The study highlights the importance of user perspectives in e-commerce app design and the value of design thinking in creating a customized user experience that aligns with user needs.

Optimal Green Investments and Replenishment Decisions in Vendor Managed Inventory System for Non Instantaneous Deteriorating Products with Partially Backordering

Optimal Green Investments and Replenishment Decisions in Vendor Managed Inventory System for Non Instantaneous Deteriorating Products with Partially Backordering

An application of deep reinforcement learning and vendor-managed inventory in perishable supply chain management

This article delves into the challenging supply chain management domain, explicitly addressing the intricate issue of perishable inventory allocation within a two-echelon supply chain. The approach outlined here leverages deep reinforcement learning with a keen understanding of the inherent stochasticity arising from uncertain demands and variable supply conditions. The examined supply chain encompasses two retailers and a central distribution center operating under a vendor-managed inventory system. The primary goal of this research is to combat the prevalent problems of wastage and shortages frequently encountered at the retail level in such supply chains. The study employs the Advantage Actor-Critic (A2C) algorithm, tailored to handle the continuous action space inherent in inventory allocation. To rigorously evaluate this approach, empirical data from a real-world blood supply chain in Tabriz is used for numerical experiments. This practical case study involves a single distribution center and two hospitals. The outcomes of these experiments affirm the effectiveness of the A2C algorithm, showcasing its ability to address the complex inventory allocation problem successfully. Furthermore, the research highlights that the algorithm outperforms existing supply chain policies, underscoring the pivotal role of optimal allocation in enhancing efficiency and operational excellence in perishable supply chains.

A Three-Front Parallel Branch-and-Cut Algorithm for Production and Inventory Routing Problems

Production and inventory routing problems consider a single-product supply chain operating under a vendor-managed inventory system. A plant creates a production plan and vehicle routes over a planning horizon to replenish its customers at minimum cost. In this paper, we present two- and three-index formulations, implement a branch-and-cut algorithm based on each formulation, and introduce a local search matheuristic-based algorithm to solve the problem. In order to combine all benefits of each algorithm, we design a parallel framework to integrate all three fronts, called the three-front parallel branch-and-cut algorithm (3FP-B&C). We assess the performance of our method on well-known benchmark instances of the inventory routing problem (IRP) and the production routing problem (PRP). The results show that our 3FP-B&C outperforms by far other approaches from the literature. For the 956 feasible small-size IRP instances, our method proves optimality for 746, being the first exact algorithm to solve all instances with up to two vehicles. 3FP-B&C finds 949 best known solutions (BKS) with 153 new BKS (NBKS). For the large-size set, our method provides two new optimal solutions (OPT), and finds 82% of BKS, being 70% of NBKS for instances with up to five vehicles. This result is more than twice the number of BKS considering all heuristic methods from the literature combined. Finally, our 3FP-B&C finds the best lower bounds (BLB) for 1,169/1,316 instances, outperforming all previous exact algorithms. On the PRP, our method obtained 278 OPT out of the 336 instances of benchmark set of small- and medium-size instances being 19 new ones in addition to 335 BKS (74 NBKS) and 313 BLB (52 new ones). On another set of PRP with medium- and large-size instances, our algorithm finds 1,105 BKS out of 1,440 instances with 584 NBKS. Besides that, our 3FP-B&C is the first exact algorithm to solve the instances with an unlimited fleet, providing the first lower bounds for this subset with an average optimality gap of 0.61%. We also address a very large-size instance set, the second exact algorithm to address this set, outperforming the previous approach by far. Finally, a comparative analysis of each front shows the gains of the integrated approach. History: This paper has been accepted for the Transportation Science Special Issue: DIMACS Implementation Challenge: Vehicle Routing. Funding: C. M. Schenekemberg was supported by the São Paulo Research Foundation (FAPESP) [Grant 2020/07145-8]. A. A. Chaves was supported by FAPESP [Grants 2018/15417-8 and 2016/01860-1] and Conselho Nacional de Desenvolvimento Científico e Tecnológico [Grants 312747/2021-7 and 405702/2021-3]. L. C. Coelho was supported by the Canadian Natural Sciences and Engineering Research Council [Grant 2019-00094]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.0261 .

Supply Chain of Iron Chelators for Thalassaemia in Malaysia: An Overview for Process Optimisation

Iron chelating therapy (ICT) drugs are prescribed to transfusion dependent thalassaemia (TDT) patients to prevent iron overload complications. The high number of TDT patients in Malaysia strained the public healthcare resources. This paper aims to provide an insight on existing supply chain management of ICT medicines in Malaysia and to explore ways for a more efficient system. A rapid review of literatures was conducted in electronic databases (PubMed, Emerald, Scopus and ScienceDirect) and other relevant Malaysian government documents. Supply of ICT to thalassaemia patients is publicly funded but much availability of oral ICT is restricted due to financial consideration. ICT in Malaysia is supplied through central procurement but purchasing and inventory management are decentralised to each hospital. Vendor-managed inventory system is an ideal option to the inventory management practice in Malaysian public healthcare facilities as it could provide better efficiency and reduces inventory management costs.

Weighted Round Robin (WRR) Based Replenishment Model in Vendor Managed Inventory (VMI) System

Vendor managed inventory (VMI) is a popular supply chain system where vendor or supplier take responsibility and decision in managing its customers’ inventory. Two important goals of the VMI are improving service level and maintaining inventory still low and available. Many studies in VMI compare their performance with the traditional system. Unfortunately, studies in improving VMI performance are rare. This work aims to improve VMI by implementing Weighted Round Robin (WRR), a popular scheduling model in computer system, in the replenishment model in VMI. WRR is popular because of its load balancing nature. Environment in this work is two-echelon supply chain. The vendor is a multi-product manufacturer. The customers are retailers. This WRR based replenishment model is then compared with two common replenishment models: (s, S) model and (r, Q) model. In this work, we observe two performance parameters: sales and inventory condition. Based on the simulation result, it is shown that the WRR model performs better than the existing (s, S) model and (r, Q) model and it occurs in most of the observed variables. In the certain condition, performance of the WRR model compared with the (s, S) model and the (r, Q) model is as follows. The WRR model performs 31 percent better than the (s, S) model and 12 percent better than the (r, Q) model in success ratio. Manufacturer’s stock in the WRR model is only 36 percent than in the (s, S) model and 40 percent than in the (r, Q) model. Total stock in the supply chain in the WRR model is only 63 percent than in the (s, S) model and 89 percent than in the (r, Q) model.

Optimizing service level, price, and inventory decisions for a supply chain with retailers’ competition and cooperation under VMI strategy

In a vendor-managed inventory (VMI) system, a manufacturing vendor manages their retailer inventories. Studies on VMI-type supply chains mostly have not considered competition between retailers. There are few works on the price competition; however, to the best of the authors’ knowledge, none of the papers formulated a service competition strategy. The service level is one of the competitive factors among competing retailers. Sometimes retailers choose to compete cooperatively instead of competing independently with the manufacturer. The present work investigates service, price, and inventory decisions under retailers’ competition and cooperation. Considering the manufacturer and retailers as the leader and followers, respectively, a Stackelberg game model of the problem is developed. The present study proposes a solution algorithm to search the Stackelberg–Nash equilibrium in the retailer cooperation and retailer independence models. The algorithm is numerically demonstrated to explore the impacts of decision parameters. To validate the model, a number of parameters are subjected to sensitivity analyses. It was found that a higher self-service (cross-service) level parameter would lead to higher (lower) profits of the retailer and manufacturer and the total profit in the two models. Retailer cooperation enhances retailer performance; however, manufacturer and system profits decline. Furthermore, when retailers cooperate, they are motivated to offer lower service levels.

Collaborative vendor managed inventory model by using multi agent system and continuous review (r, Q) replenishment policy

In the vendor-managed inventory (VMI) system, the vendor takes over responsibility for managing customer inventory so that delivery is not based on the order but the customer's inventory condition. It makes the vendor becomes a dominant entity, and customers are supplied by its own vendor exclusively. That is why most studies in VMI implement a single-vendor-single-customer or single-vendor-multi-customer scenario. In certain conditions, this exclusiveness can increase lost sales. Besides, most of them implement a single product scenario. In this work, we develop VMI model for the multi-vendor-customer-product scenario. This model is developed based on the collaborative multi-agent system. The relationship between vendors and customers is many-to-many. This work aims to reduce lost sales and maintain efficiency in the inventory. The continuous review (r, Q) policy is used as the replenishment model. The simulation result shows that the collaborative model creates higher sales, lower lost sales, and competitive inventory than the non-collaborative one. The lost sales is 50 to 75 percent lower. The sales percentage is 17 to 27 percent higher. The total retailers' stock is 20 to 38 percent higher. The total vendors' stock is 11 to 30 percent lower. The total stock in the supply chain in the collaborative model is 2 to 16 percent higher. The number of retailers is directly proportional to the total vendor's stock and total supply chain stock gaps; inversely proportional to the lost sales gap; and not related to the sales percentage and total retailers' stock gaps.

Interval Type-2 Fuzzy Vendor Managed Inventory System and Its Solution with Particle Swarm Optimization

Interval Type-2 Fuzzy Vendor Managed Inventory System and Its Solution with Particle Swarm Optimization

A closed vendor managed inventory system under a mixed fleet of electric and conventional vehicles

In a closed-loop supply chain, where a Vendor Managed Inventory system is executed, the Closed-Loop Inventory Routing Problem is one of the main problems confronted by logistics decision-makers. This study addresses a Closed-Loop Inventory Routing Problem under a mixed fleet of electric and conventional vehicles. The problem is formulated as a Mixed-Integer Linear Programming model and a Fix&Optimize algorithm is developed to tackle larger problem instances. The proposed decision support models incorporate comprehensive estimation approaches for energy consumption of both electric and conventional vehicles that allow to better estimate fuel and electric cost and transportation emissions. The models respect uncertain reverse returnable transport items flow from customers as well. The numerical analyses demonstrate the benefits that could be obtained by means of the provided models. The Fix&Optimize heuristic yields in 5.72% lower costs within 59.23% shorter computation times on average compared to the Mixed-Integer Linear Programming model. The proposed models are capable to provide trade-off analyses for sustainable logistics management.

A Proposed Order Prediction Methodology for Vendor-Managed Inventory System in FMCG Sector Based on Interval-Valued Intuitionistic Fuzzy Sets

A Proposed Order Prediction Methodology for Vendor-Managed Inventory System in FMCG Sector Based on Interval-Valued Intuitionistic Fuzzy Sets

Vendor Managed Inventory and Strategy

Vendor-managed inventory (VMI) may be perceived as a business agreement between a supplier and a buyer in which the supplier manages the customer's inventory for a fee. The purpose of this paper is to examine potential and current VMI relationships between two large Pittsburgh-based companies, mostly from a global reach perspective. The initial findings of the research show that not implementing a VMI system could potentially hurt these companies, while supporting the basic research proposition that VMI will increase customer satisfaction ratings and revenues for both companies. Within this case study, there are several main questions throughout the paper. These questions are what are the goals in the case study of supply chain management (SCM) in terms of their operations? How will these goals affect the operations? What are the different perspectives from the buyer and supplier? Will the need to implement a global SCM strategy continue to exist, especially under the pressures of Covid-19?

The two-echelon production-routing problem

This paper introduces the Two-Echelon Production-Routing Problem. This problem is motivated from the petrochemical industry, enlarging the supply chain integration by taking into account production, inventory, and routing decisions in a two-echelon vendor-managed inventory system. We describe, model, and design a branch-and-cut (B&C) to solve the problem under different inventory policies. We also propose a novel exact algorithm, by employing parallel computing techniques, in order to combine local search procedures within a traditional B&C scheme. We evaluate the performance of our methods through extensive computational experiments, both by comparing the algorithms, the effectiveness of the different inventory policies, and the impact of these policies on the partial costs. We derive many managerial insights based on the results. We also validate our new exact algorithm by solving similar problems from the literature, such as the two-echelon multi-depot inventory-routing (2E-MDIRP) and the classical multi-vehicle production-routing problem (MV-PRP). Computational experiments show that our method is very competitive. Based on 512 experiments for the 2E-MDIRP, our algorithm was able to find 111 new best known solutions (BKS), besides proving 412 optimal solutions, against 298 from the literature. For 336 experiments over small and medium size MV-PRP instances, we proved 242 optimal solutions, 11 more than the exact methods from the literature, besides providing 95 new BKS. Moreover, we were the first to tackle large MV-PRP instances exactly, and in this case, our algorithm provides all BKS for instances up to 50 customers, 20 periods and 5 vehicles, outperforming all meta/matheuristics procedures from the literature.

A multi grid discrete time based framework for maritime distribution logistics & inventory planning for refinery products

In this paper, a mixed integer linear programming(MILP) framework is presented to rigorously represent a maritime inventory routing problem(MIRP). The MIRP discussed here involves multiple refinery liquid products that need to be transported from multiple supply ports to associated consumer ports using ships with undedicated compartments. The model is based on VMI(vendor managed inventory) system where the vendor decides the product delivery quantity and time of delivery by tracking the consumer inventory and consumption patterns. The proposed mathematical framework involves a multi grid discrete time formulation which rigorously represents several intricacies related to products loading/unloading, jetty activities, port inventory limitations and ship voyages. Several scenarios representative of the actual process are designed and the results demonstrates the efficacy of the proposed framework in finding an optimum ship and jetty schedule that minimizes the total transportation cost in a computationally efficient manner.

The two-echelon inventory-routing problem with fleet management

This paper introduces the Two-Echelon Inventory-Routing Problem with Fleet Management. This problem arises under a two-echelon vendor-managed inventory system when a company must make vehicle routing and inventory management decisions, while renting a fleet subject to short- and mid-term agreements. Different chemical products are transported contaminating the vehicles that may require cleaning activities. Pickups of input take place in the first echelon, and the final product deliveries are performed in the second echelon. Based on a real-life case in the petrochemical industry, we introduce a formulation that takes into account vehicle rentals, cleanings, transportation, inventory management, and vehicle returns decisions. We design a branch-and-cut algorithm to solve it and also propose a matheuristic, in which vehicle routes are handled by an adaptive large neighborhood mechanism, while input pickups, product deliveries, and fleet planning are performed by solving several subproblems to optimality. Moreover, we introduce a hybrid parallel framework, combining our matheuristic and the branch-and-cut algorithm in order to solve very large instances exactly. We validate our methods by solving a set of instances of the two-echelon multi-depot inventory-routing problem from the literature, obtaining new best solutions for all instances. We have introduced a set of instances for this rich and new problem, and performed an extensive assessment of our methods. The results provide interesting data about the supply chain structure.

Stock replenishment policies for a vendor-managed inventory in a retailing system

Using Vendor Managed Inventory (VMI), the vendor determines the replenishment decisions at the location of buyers (retailers). This strategy is used primarily for handling demand fluctuations stemming from the Bullwhip effect, leading the system to prevent from holding excessive inventory that result in a reduction in the overall cost of the supply chain. The main advantages of VMI for vendors are higher levels of accessibility to inventory information and more direct contact with the customers. Similarly, VMI has some pros for the buyers, such as shared risk with upper levels of supply chain and reduction in their holding costs of inventory. In this paper, a vendor-managed inventory system is developed containing one vendor and two buyers in which the main assumption is that back-ordering and lost sales are permitted. In this system, (r, Q) and (R, T) replenishment policies are compared according to their performances to see which one performs more cost-efficiently when partial back-ordering is allowed. In accordance, mathematical models utilizing (r, Q) and (R, T) replenishment policies are developed, and algorithms for deriving the optimal replenishment decision variables are proposed. Moreover, significant differences between the two replenishment policies are discussed. The main finding obtained by this research is that when shortage is permitted, both (r, Q) and (R, T) replenishment policies under VMI have pros and cons in different contexts.

Partially Smoothing and Gradient-Based Algorithm for Optimizing the VMI System with Competitive Retailers under Random Demands

Vendor managed inventory (VMI) is an improved sustainable inventory management system, but it is difficult to establish and solve an integrated Stackelberg game model under the complicated practical environment. In this paper, a bilevel programming model is proposed to formulate the VMI system by taking into account the uncertainty of demand, the competition among retailers, the cooperative advertising, the shortage and holding costs, and the practical constraints. For the established stochastic model being associated with continuously random demands, a deterministic mathematical program with complementarity constraints (MPCC) is first derived by expectation method and the first-order optimality conditions of the lower-level problem. Then, with a partially smoothing technique, the MPCC is solved by transforming it into a series of standard smooth optimization subproblems. Finally, owing to complexity caused by evaluating the integrals with unknown decision variables in the objective function, an efficient algorithm is developed to solve the problem based on the gradient information of model. Sensitivity analysis has been employed to reveal a number of managerial implications from the constructed model and algorithm. (1) The participation rate depends on advertising expenditures from both the manufacturer and the retailer. There exists an optimal threshold of participation rate for the manufacturer, which can be provided by the intersection point of the manufacturer and retailer’s cost-profit curves. (2) The manufacturer’s advertising policy is less sensitive to uncertainty of demand than the change of the retailer’s advertising policy. (3) The manufacturer in the VMI system should concern about the differences caused by symmetric or asymmetric retailers.

Understanding critical success factors of vendor-managed inventory in healthcare sector: A case study in Thailand

ABSTRACT The healthcare sector is currently facing the severe global competition, resulting to improve its business operations by decreasing overall cost and enhancing competitive advantage. Vendor-managed inventory (VMI) system is one of the effective solutions as its benefits directly support on inventory cost reduction. The VMI implementation is essential, and key critical success factors (CSFs) are diverse. Large efforts are made based on VMI approach from literature; however, a generalized analysis on interrelationship among CSFs of VMI implementation is limited. This paper desires to fill this gap by a new hybrid MCDM model, comprising of Fuzzy Delphi and Grey-DEMATEL methods. The results present the identification of 10 influencing CSFs and their cause–effect relationship. Additionally, this study provides an important insight into managerial implication to improve inventory performance of Thai healthcare sector.

Improving sustainability in a two-level pharmaceutical supply chain through Vendor-Managed Inventory system

Hospitals, as the main customers of medications, typically adopt conservative inventory control policies by keeping large quantities of drugs in stock. Given the perishable nature of medications, such strategies lead to the expiration of excess inventory in the absence of patients’ demand. Consequently, producers are faced with governmental penalties and environmental reputation forfeit due to the negative impact that disposing expired medications pose to the environment. This article aims to improve the sustainability of a pharmaceutical supply chain using a real case study. An analytical model is proposed to explore the effect of implementing a Vendor-Managed Inventory (VMI) system in minimizing the quantity of the expired medications at hospitals. Further, a set of Monte-Carlo simulation tests are conducted to investigate the robustness of the VMI model under demand uncertainty. Experimental results on a real case study under deterministic demand show the efficiency of the VMI model in eliminating the amount of expired medications without compromising customer’s satisfaction. The results also demonstrate that the safety stock (SS) level and the capacity assigned to the customer are crucial factors in the overall cost of the pharmaceutical supply chain (PSC). The PSC cost could be reduced by 19% when reducing the SS level by 50%. Moreover, the producer is recommended to increase the capacity assigned to the customer by a factor of 1.5 so as to fully satisfy the customer’s demand. Finally, the simulation results confirm the efficiency and robustness of embracing a VMI system under random demand scenarios. More precisely, zero amount of expired medications is obtained in 93% of cases. Thus, adopting this strategy could minimize drug wastage and ultimately improve the reputation of the producer in the market in terms of implementing Lean and sustainable practices.