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).     

Using simulation-based system dynamics and genetic algorithms to reduce the cash flow bullwhip in the supply chain

The bullwhip effect (BWE) is a phenomenon, which is caused by ineffective inventory decisions made by supply chain members. In addition to known inefficiencies caused by the bullwhip effect within a supply chain product flow, such as excessive inventory, it can also lead to inefficiencies in cash flow such as the cash flow bullwhip (CFB). The CFB reduces the efficiency of the supply chain (SC) through heterogeneous distribution of cash among supply chain members. This paper aims to decrease both the BWE and the CFB across a SC through applying a simulation-based optimisation approach, which integrates system dynamics (SD) simulation and genetic algorithms. For this purpose, cash flow modelling is incorporated into the SD structure of the beer distribution game (BG) to develop the CFB function. A multi objective optimisation model is then integrated with the SD-BG simulation model. Finally, a genetic algorithm (GA) is applied to determine the optimal values for the inventory, supply line, and financial decision parameters. Results show that the proposed integrated framework leads to efficient liquidity management in the SC in addition to cost management.     

Sustainable Supplier Selection Model in Supply Chains During the COVID-19 Pandemic

As global supply chains become more developed and complicated, supplier quality has become increasingly influential on the competitiveness of businesses during the Covid-19 pandemic. Consequently, supplier selection is an increasingly important process for any business around the globe. Choosing a supplier is a complex decision that can result in lower procurement costs and increased profits without increasing the cost or lowering the quality of the product. However, these decision-making problems can be complicated in cases with multiple potential suppliers. Vietnam's textile and garment industry, for example, has made rapid progress in recent years but is still facing great difficulties as the supply of raw materials and machinery depends heavily on foreign countries. Therefore, it is extremely important for textile and garment manufacturing companies in Vietnam to implement an effective supplier evaluation and selection process. While multicriteria decision-making models are frequently employed to assist with supplier evaluation and selection problems, few of these models consider the problem under the condition of a fuzzy decision-making environment. The aim of this paper is to create a hybrid MCDM model using the Fuzzy Analytical Hierarchy Process (FAHP) model and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to assist the supplier selection process in the garment industry in a fuzzy decision-making environment. In this study, the FAHP method is used to evaluate the performance and the weight of each criterion. TOPSIS is then used to rank all potential suppliers. The proposed model is then applied to a real-world case study to demonstrate both the process of calculation as well as its real-world applicability. The results from the case study provide empirical evidence that the model is feasible. The proposed approach can also be used in combination with other MCDM models to better support decision makers and can be modified to be applied in similar supplier selection processes for different industries.     

Just-in-time purchasing: an integrated inventory model involving deterministic variable lead time and quality improvement investment

Nowadays supply chain management is a popular practice in manufacturing systems, and just-in-time (JIT) production plays a crucial role in supply chain environments. Companies are using JIT production to gain and maintain a competitive advantage. The characteristics of JIT systems are consistent high quality, small lot sizes, frequent delivery, short lead time, and close supplier ties. This paper presents an integrated inventory model to minimize the sum of the ordering/setup cost, holding cost, quality improvement investment and crashing cost by simultaneously optimizing the order quantity, lead time, process quality and number of deliveries while the probability distribution of the lead time demand is normal. This integrated inventory model is useful particularly for JIT inventory systems where the vendor and the purchaser form a strategic alliance for profit sharing.     

Manufacturing lot-sizing,procurement and delivery schedules over a finite planning horizon

In this study, an integrated manufacturing system for technology-related companies whose products are experiencing continuous price decrease during the life cycle is studied for optimal procurement, production and delivery schedules over a finite planning horizon. The model considers the inventory cost both at manufacturing and at delivery from supplier. Since the price is continuously decreasing, a manufacturing firm delivers the finished goods in small quantities frequently. Frequent deliveries in small lots are effective to reduce the total cost of the supply chain. The key for high-tech industries is to reduce the inventory holding time since the component prices are continuously decreasing, and this can only be achieved by implementing an efficient supply chain. Therefore, the main purpose of this paper is to develop an integrated inventory model for high-tech industries in JIT environment under continuous price decrease over finite planning horizon while effectively and successfully accomplishing supply chain integration so that the total cost of the system is minimal. An efficient algorithm is developed to determine the optimal or near-optimal lot sizes for raw material procurement, and manufacturing batch under a finite planning horizon. Finally, the solution technique developed for the model is illustrated with numerical examples.     

Designing a collaborative supply-chain plan using the analytic hierarchy process and genetic algorithm with cycle-time estimation

Collaboration management has become a key issue for supply-chain management and can improve the overall production/manufacturing performance and value. This paper aims to conduct collaborative supply-chain planning and establish supplier selection, as well as production and distribution planning. This study uses an Analytic Hierarchy Process with Rough Sets Theory to establish a supplier purchasing value rating system. Also, a cycle-time estimation procedure is developed to effectively estimate the operating time of a collaborative supply chain. Assembly-line balancing technology is also introduced to guarantee smooth production and distribution in a supply chain. A multi-objective optimisation mathematical model, including purchasing value, cost, cycle time, and smoothness index, is constructed to complete the supplier selection and production-distribution planning. To efficiently solve this mathematical model, this paper proposes a genetic algorithm (E_ctGA) combined with a cycle-time estimation procedure. Finally, it presents a case study for validation. The results show that a better collaborative supply chain plan could be achieved by combining the proposed cycle-time estimation procedure.     

产品生命周期过程(横向集成)供应商选择模型研究

通过分析集成化供应链管理环境下供应商选择过程,给出了选择指标体系及选择步骤。在此基础上,建立了“横向”集成型企业供应商选择的模糊综合评价选择模型。应用此模型可对选择最满意的供应商做出相应的决策。     

Fuzzy hybrid decision model for supplier evaluation and selection

This paper proposes a structured, integrated decision model for evaluating suppliers by combining the fuzzy analytical hierarchy process (FAHP) and grey relational analysis (GRA). The qualitative and partially-known information is incorporated in this decision model using the fuzzy set theory. In this proposed methodology, the weights of the evaluation criteria are calculated by using FAHP, then the ranking of the suppliers is determined by using GRA. Finally to show the robustness of the model, a sensitivity analysis is also performed. In this study, the supplier selection problem of an electroplating industry in the southern part of India was investigated, demonstrating the effectiveness of this developed integrated model. This model can help in solving the complex decision in supplier selection practice. The results generated from the model are properly validated and finally a systematic solution with decision support is provided for decision makers. This model can be integrated with other decision support systems of similar kinds of industries.     

An integrated inventory optimization model for facility location-allocation problem

This paper presents an integrated inventory distribution optimisation model for multiple products in a multi-echelon supply chain environment. Inventory, transportation and location decisions are considered. The objective is to offer practical guideline to the steel retail supply chain practitioners in choosing the correct distribution centre, finding out inventory level at individual inventory keeping points (retailers and distribution centres) point thereby helping them in reducing overall distribution cost. The framework presented endorses systems approach and suggests near-optimal approach to calculating inventory for an individual distributor and his retailers. Two algorithms are used to solve this problem, a novel hybrid Multi-objective Self-learning particle swarm optimiser and Non-dominated sorting genetic algorithm-II. The model and solution methods are tested on real data-sets obtained from organisations in the steel retail environment. The actual data on inventory holding, ordering and transportation costs of distributors and retailers are used as inputs. The decisions like choosing correct set of Distribution centres, keeping optimal regular and safety stock inventory levels are arrived at by applying practical constraints in the supply chain. Model developed assists in effective and efficient distribution of the products manufactured from the optimal location at minimal cost.     

周期可变下考虑多车型的库存路径优化研究

目的 研究基于供应商管理库存模式下，由一个供应商和多个零售商组成的二级供应链配送系统的库存路径优化问题，确定计划期内各配送周期的长度、数量，对供应链中库存策略与配送方案问题进行协调优化，使系统总成本最低。方法 考虑的成本包括库存持有成本、缺货成本和配送成本。为降低供应链系统的总成本，提出全新的周期可变策略。结果 采用遗传算法求解得出最佳方案。将不同策略的算例结果进行对比分析，结果表明周期可变策略与周期固定策略相比，系统总成本最低节约比例为1.7%，最高节约比例为42.3%。结论 通过对计划期内各配送周期的长度及数量进行划分调整可以有效地节约系统总成本，同时，采用多车型的配送方案明显优于采用同车型的配送方案。     

Optimal design methodologies for configuration of supply chains

This paper describes a methodology developed for designing an optimal configuration for a supply chain. A typical configuration for a supply chain consists of defining components of the system, assigning values to characteristics parameters of each component and setting operation policies for governing the interrelationships among these components. As such, each configuration will be defined by a set of values for quantitative parameters of the system as well as a set of policy and qualitative characteristics. Examples of quantitative variable include inventory levels and frequency of ordering where as location of distribution centres and mode of transportation between suppliers and the original equipment manufacturers (OEM) are the decision variables of policy and qualitative nature. The methodology presented here consists of a supply chain model builder coupled with two optimisation algorithms that automatically build a sequence of configurations that systematically move towards an optimum design. A combination of mixed integer programming and a genetic algorithm is used to determine simultaneously the values of quantitative as well as policy variables. The solution consists of strategic decisions regarding facility locations, stocking locations, supplier selection, production policies, production capacities, and transportation modes.     

Supply chain network design with unreliable suppliers: a Lagrangian relaxation-based approach

This paper deals with the integrated facility location and supplier selection decisions for the design of supply chain network with reliable and unreliable suppliers. Two problems are addressed: (1) facility location/supplier selection; and (2) facility location/supplier reliability. We first consider the facility location and supplier selections problem where all the suppliers are reliable. The decisions concern the selection of suppliers, the location of distribution centres (DCs), the allocation of suppliers to DCs and the allocation of retailers to DCs. The objective is to minimise fixed DCs location costs, inventory and safety stock costs at the DCs and ordering costs and transportation costs across the network. The introduction of inventory costs and safety stock costs leads to a non-linear NP-hard optimisation problem. To solve this problem, a Lagrangian relaxation-based approach is developed. For the second problem, a two-period decision model is proposed in which selected suppliers are reliable in the first period and can fail in the second period. The corresponding facility location/supplier reliability problem is formulated as a non-linear stochastic programming problem. A Monte Carlo optimisation approach combining the sample average approximation scheme and the Lagrangian relaxation-based approach is proposed. Computational results are presented to evaluate the efficiency of the proposed approaches.     

基于供应链管理的供应商选择问题初探

首先讨论了供应链环境下选择供应商的意义，然后建立了供应商选择的评价指标体系，重点讨论了应用TOPSIS 方法对供应商选择的模型及算法，最后用一个简单的案例进行了说明。     

双层延期支付及价格折扣条件下零售商的最优库存策略

研究了在双层延期支付及价格折扣条件下零售商的库存策略。假设零售商在供应链中占据决策的主导地位,即零售商从供应商处得到延期支付及相应的现金折扣,而零售商仅为其顾客提供延期支付。建立了以零售商利润最大化为目标的EOQ（经济订购批量）模型,对模型分析求解,得到零售商的最优订货周期,并且通过一个简易算法,使得模型更容易运用于实践。通过数值算例验证了结论,并为企业物流管理实践提供了建议。     

Enhanced joint pricing and lotsizing problem in a two-echelon supply chain with logit demand function

In retail supply chains, ordering and pricing policies for the retailer and shipment plan for the supplier are the most important decisions. These policies are often conducted either individually or sequentially with poor overall performance for the whole supply chain resulting to extra inventory and other deficiencies. In this paper, an integrated marketing-inventory model in a two-echelon supply chain model is developed involving discount promotion, customer behaviour more realistically and operations aspects to determine optimal ordering, shipping and pricing quantities simultaneously. An efficient analytical solution procedure and a Particle Swarm Optimisation solution algorithm are also developed. Finally, a number of numerical tests are conducted to approve the interesting theoretical results of the analytical approach.     

Incorporating supplier selection and order allocation into the vehicle routing and multi-cross-dock scheduling problem

In the vehicle routing problem with cross-docking (VRPCD), it is assumed that the selected suppliers and the quantity of the products purchased from each supplier are known. This paper presents an MILP model which incorporates supplier selection and order allocation into the VRPCD in a multi-cross-dock system minimising the total costs, including purchasing, transportation, cross-docking, inventory and early/tardy delivery penalty costs. The sensitivity of the model on the key parameters of the objective function is analysed and the supply decisions are evaluated when the coefficients of the distribution cost are changed. A two-stage solution algorithm (TSSA) is proposed and the results of the TSSA for small-sized instances are compared with the exact solutions. Finally, a large-sized real case of an urban freight transport is solved using the TSSA.     

一体化供应链下的项目采购管理与供应商管理战略

本文针对汽车行业讨论了产品项目开发中采购管理的重要作用,根据目前项目采购中暴露出的问题,总结出项目采购管理其实质即为对供应商战略管理的理念,从而提出在一体化供应链下项目采购管理中对供应商管理的战略模型。该模型包括四个路径:"构造供应商库,选择和评价供应商,供应商参与项目开发,定义目标供应商平台",并体现如何应用该模型以提高一体化供应链的竞争性。     

Joint pricing and inventory strategies in a supply chain subject to inventory inaccuracy

This paper focuses on pricing strategies, inventory policies for a supply chain when Radio Frequency Identification (RFID) technology is adopted to cope with inventory inaccuracy. The supply chain consists of one supplier and one retailer, in which the RFID tag price is shared between the supplier and the retailer. We present and compare the performance differences between a wholesale price contract and a consignment contract when the retailer is the Stackelberg leader and the supplier is the follower. Based on the optimal pricing and inventory decisions, an interesting observation of contract selection is that there are two critical values of inventory available rate such that when the inventory availability is less than the lower value, both the supplier and the retailer prefer a consignment contract; when the inventory availability is greater than the upper value, a wholesale price contract is their best choice; when the inventory availability is between the two values, the supplier prefers a wholesale price contract and the retailer prefers a consignment contract. Additionally, there exist threshold values of RFID tag price and sharing rate to determine the contract preference for the retailer. Furthermore, the profits of both the supplier and the retailer are independent of the RFID tag price sharing rate in a wholesale price contract, and the supplier has the incentive to invest in RFID tag cost in a consignment contract.     

VMI下需求受库存和努力水平影响的供应链协调

针对一个实施VM I(供应商管理库存)的两级供应链,市场需求受供应商和零售商双重努力影响的问题,建立了基于协作关系的库存和努力水平决策模型。通过分析得出在分散决策下供应商选择的库存水平和零售商选择的努力水平均低于集中决策下的最优水平,供应链效率也较低。设计了一个成本分摊契约协调供应链。结果表明,该契约能将供应商和零售商紧密地联系在一起,且通过供应商和零售商的共同努力能获得比分散式决策下更大的利润。     

A dynamic programming approach to integrated assembly planning and supplier assignment with lead time constraints

As manufacturers face fierce competition in the global market, responsiveness has become an important competitiveness factor in addition to quality and cost. One essential responsiveness strategy is to reduce product development and lead times by integrating assembly planning with supplier assignment. This paper addresses the problem of integrated assembly and supply chain design under lead-time constraints by formulating and solving an optimisation problem with minimal total supply chain costs. This new time-constrained joint optimisation problem belongs to an NP-hard resource-constrained scheduling problem. To model this problem effectively, we develop a novel Hyper AND/OR graph and apply it for integrating assembly and supply chain decisions. We also develop a dynamic programming model and associated algorithm in order to solve the integrated optimisation problem with pseudo-polynomial time complexity in practice. Numerical case studies validate that the methods developed can solve the integrated decision-making problem optimally and efficiently. This paper overcomes the limitations of previous studies on concurrent assembly decomposition and supplier selection, which optimises cost without time constraints. The models and results of this research can be applied to a variety of areas including assembly design, maintenance module planning and supply chain restructuring.