Decision support system for vendor managed inventory supply chain: a case study

Abstract

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 organizations 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, stock-outs 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)..

Decision support system in supply chain: A systematic literature review

“…The use of DSS also covers various problems in the supply chain, including transactions (Brauner et al, 2019;Moynihan & Wang, 2015), cost efficiency (Yan et al, 2014;Borade & Sweeney, 2015), monitoring (Singh & Randawa, 2015), decision making (Brauner et al, 2016), environment sustainability and hazard (Lenny Koh et al, 2013), inventory , operation management (Osoriuo Gomez et al, 2017), performance measurement (Marimin et al, 2017), planning (López-Milán & Plà-Aragonés, 2014), location (Zang et al, 2016, risk management (Benazzouz et al, 2017), entire supply chain (Kristianto et al, 2012), forecasting (Monteleone et al, 2015), orders (Guo & Guo, 2014), for instance for sustainable competitive advantage by Karthik et al (2015) and genetic identification by Bohanec et al (2017). Based on the results it can be seen that the use of DSS in the supply chain is influenced by factors of interest for the industry explicitly and comprehensively such as relations with suppliers, production and distributors compared with the complexity of particular cases of problems in the supply chain.…”

“…The use of DSS in the supply chain has also been carried out by researchers in various sectors although there are not too many sectors, for example automotive (Park & Yoon, 2013), computers (Lin et al, 2011), construction (Guerlain, et al, 2019), e-Commerce (Yan et al, 2019), fisheries (Teniwut et al, 2013), food (Fikar, 2018), forestry (Gerasimov et al, 2013), humanitarian (Saksrisathaporn et al, 2012 ), logistics (Biswas & Samanta, 2016), medical (Benazzouz et al, 2017), petroleum (Buhulaiga & Telukdarie, 2018), ports (Lara Garcia & Vangampler, 2012), retailers (Borade & Sweeney, 2015), textile (Rabenasolo & Zeng, 2012), tourism (Monteleone et al, 2015) and wind farm (Lange et al, 2012). This empirical condition indicates that DSS used in the supply chain has been implemented in various industries even though in its implementation, the focus is on one sector but taking into account the distribution of its scope this shows the strength of DSS in helping to improve the supply chain performance.…”

A coordinated scheduling of delivery and inventory in a multi-location hospital supplied with a central pharmacy

“…Tables (7,8,9) show the solution time obtained for each method. In the computational study, the following parameters are used: the vehicle's capacity is randomly generated from the uniform distribution with range [n/5, 2n / 5]; further, its round-trip delivery time for each hospital is randomly generated from the uniform distribution with range ½3; 5 h. The due dates ðd j Þ j¼1...n are uniformly separated with values randomly generated.…”

“…The delivery-inventory problem is denoted as Vendor-Managed Inventory (VMI) problem. The VMI problem is a widely used collaborative inventory management policy in which manufacturers manage the inventory of retailer and take responsibility for making decisions related to the timing and extent of inventory replenishment [7]. VMI partnerships help organizations to reduce demand variability, inventory holding and distribution costs.…”

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

“…As a result of the immediate response to customers' fluctuating demands, VMI systems increase operational flexibility, customer service level, and market visibility. VMI also leads vendors to smooth production, distribution plans, supply chain cost reduction, inventory optimization, better risk management, and increase profit and competitiveness [3,13,14]. VMI systems achieve these goals through more accurate sales forecasting methods and more effective inventory distribution in the supply chain [12].…”