Impact of vendor-managed inventory on supply chain performance

v\:* {behavior:url(#default#VML);} o\:* {behavior:url(#default#VML);} w\:* {behavior:url(#default#VML);} .shape {behavior:url(#default#VML);}  Bu çalışmada, Tedarikçi Yönetimli Envanter (VMI) yaklaşımının tedarik zinciri performansına sağladığı faydaları incelemek için bir benzetim modeli kurgulanmıştır. Benzetim modelinde kapasite sınırı olan bir üretici, bir distribütör, bir toptancı ve bir perakendeciden oluşan dört kademeli bir tedarik zinciri yapısı dikkate alınmıştır. Durağan ve durağan olmayan talep yapılarının dikkate alındığı benzetim modelinde iki farklı yapıda tedarik zinciri canlandırılmıştır. İlk yapı, geleneksel anlayışla yönetilen (TSS) tedarik zinciri yapısıdır. TSS yaklaşımında, tedarik zinciri üyeleri arasında herhangi bir bilgi paylaşımı olmamakta ve birbirleriyle sadece siparişler vererek iletişim kurmaktadırlar. İkinci yapı ise, VMI yaklaşımının uygulandığı tedarik zinciri yapısıdır. VMI yaklaşımı çerçevesinde, perakendecideki envanterin yönetiminden toptancı sorumludur. Benzetim modeli sonucunda elde edilen sonuçlar, VMI yaklaşımının, tedarik zinciri maliyetini önemli ölçüde düşürürken, müşteri gereksinimlerini daha yüksek oranda karşıladığını göstermektedir. Ayrıca elde edilen bulgular; üretim kapasitesinin ve müşteri talebinde gözlenen belirsizliklerin VMI yaklaşımından elde edilen fayda üzerinde anlamlı etkileri olduğunu göstermektedir. İlk olarak üretim kapasitesi dikkate alındığında, üretim kapasitesinin çok sınırlı olmasının VMI yaklaşımlarından elde edilen faydayı düşürdüğü görülmektedir. Müşteri talebinde gözlenen belirsizlikler dikkate alındığında ise, müşteri talebindeki belirsizliğin artmasının VMI yaklaşımından elde edilen faydayı önemli düzeylerde azalttığı görülmektedir. Sonuç olarak, VMI yaklaşımının üretim kapasitesinin yüksek olduğu ve müşteri talebinde gözlenen belirsizliklerin düşük olduğu durumlarda daha faydalı olduğu görülmektedir.    <!--[if !vml]--><!--[endif]-->Anahtar Kelimeler: Tedarikçi yönetimli envanter, bilgi paylaşımı, tedarik zinciri yönetimi, benzetim modeli.In this study, a comprehensive simulation model is built to explore the benefits of a supply chain initiative called ?vendor-managed inventory (VMI)? where the vendors are authorized to manage inventories at retail locations. Under VMI, retailer shares demand information to the vendor who should then make stock level decisions for its own organization and the retailer. In this article, specifically, we are interested in finding answers to the following questions: Under what conditions will VMI more beneficial? How will lead times, demand uncertainty, and manufacturing capacity affect the benefits gained from VMI? From this investigation, we will better understand the required conditions for more successful VMI applications. For this purpose, we consider a four-echelon supply chain consisting of a capacitated manufacturer, a distributor, a wholesaler, and a retailer. The retailer realizes customer demands from gamma distribution. Stationary and non-stationary customer demand structures considered to explore the impacts of different levels of demand uncertainty on the benefits obtained from VMI. In order to evaluate VMI benefits, two stylized supply chain structures are considered. The first situation is a traditional model (TSS) where there is no information sharing and all members in the supply chain independently plan and operate the supply chain. The second situation is a supply chain model with vendor-managed inventory (VMI) where the wholesaler (as being the vendor for the retailer for the given supply chain) takes the full responsibility of managing the retailer?s inventory. Order-up to policy is used for ordering decisions for both systems. A full factorial design of experiment constructed to analyze the impacts of different factors on the benefits obtained from VMI. These factors are demand uncertainty, manufacturing capacity restrictions, and lead times. The levels of the each factor determined as follows: Three levels for the demand uncertainty; demand structure with no seasonal swings, moderate degree of seasonal swings, and high degree of seasonal swings. Manufacturing capacity restrictions measured by ?capacity ratio? which corresponds to the ratio of total capacity to total demand. The levels for the manufacturing capacity restrictions are capacity ratio of 1.10, 1.30, and 1.50. Two levels of lead-time; which is 1 and 4 periods Moreover, total cost for entire supply chain, and customer service level are the dependent variables considered as performance metrics in experimental design. Customer service level is the percentage of customer demand satisfied through the available inventory of the retailer. Since there is more than one dependent variable in the experimental design, MANOVA is conducted to analyze the experimental simulation output. The results of the experimental simulation output indicate that when compared to traditional supply chain, VMI decreases supply chain cost substantially. Compared to the traditional settings, the reduction in the total supply chain cost varies from 6.5% to 43.3% with an average around 17.4%. When the service level is considered, we see that in all cases VMI reaches higher levels of service levels. For example, customer service level increased from 94.4% to 95.9% on the average. These results lead us to conclude that VMI is always beneficial to the supply chain performance. Moreover, the results of the MANOVA indicate that manufacturer?s capacity restrictions and customer demand uncertainty have significant impacts on the benefits obtained from VMI. Firstly, when manufacturing capacity is considered, the findings indicate that as the available manufacturing capacity is higher, the benefits obtained from VMI are also higher. Secondly, when the demand uncertainty is considered, the results indicate that as the uncertainty in customer demand increases, the performance of supply chain with VMI decreases substantially. In conclusion, through comprehensive simulation experiment and subsequent analysis of the simulation outputs, we made the following important findings: VMI significantly improve the performance of the supply chain under all conditions. The benefits gained from VMI significantly influenced by the manufacturing capacity and demand uncertainty. Therefore, VMI is more beneficial under the conditions with is high level of manufacturing capacity and low degree of demand uncertainty. Keywords: Vendor-managed inventory, information sharing, supply chain management, simulatio

Integrated optimisation for production capacity, raw material ordering and production planning under time and quantity uncertainties based on two case studies

Abstract This paper develops a supply chain (SC) model by integrating raw material ordering and production planning, and production capacity decisions based upon two case studies in manufacturing firms. Multiple types of uncertainties are considered; including: time-related uncertainty (that exists in lead-time and delay) and quantity-related uncertainty (that exists in information and material flows). The SC model consists of several sub-models, which are first formulated mathematically. Simulation (simulation-based stochastic approximation) and genetic algorithm tools are then developed to evaluate several non-parameterised strategies and optimise two parameterised strategies. Experiments are conducted to contrast these strategies, quantify their relative performance, and illustrate the value of information and the impact of uncertainties. These case studies provide useful insights into understanding to what degree the integrated planning model including production capacity decisions could benefit economically in different scenarios, which types of data should be shared, and how these data could be utilised to achieve a better SC system. This study provides insights for small and middle-sized firm management to make better decisions regarding production capacity issues with respect to external uncertainty and/or disruptions; e.g. trade wars and pandemics.</jats:p

Benefits of retailer-supplier partnership initiatives under time-varying demand:a comparative analytical study

This paper aims to help supply chain managers to determine the value of retailer-supplier partnership initiatives beyond information sharing (IS) according to their specific business environment under time-varying demand conditions. For this purpose, we use integer linear programming models to quantify the benefits that can be accrued by a retailer, a supplier and system as a whole from shift in inventory ownership and shift in decision-making power with that of IS. The results of a detailed numerical study pertaining to static time horizon reveal that the shift in inventory ownership provides system-wide cost benefits in specific settings. Particularly, when it induces the retailer to order larger quantities and the supplier also prefers such orders due to significantly high setup and shipment costs. We observe that the relative benefits of shift in decision-making power are always higher than the shift in inventory ownership under all the conditions. The value of the shift in decision-making power is greater than IS particularly when the variability of underlying demand is low and time-dependent variation in production cost is high. However, when the shipment cost is negligible and order issuing efficiency of the supplier is low, the cost benefits of shift in decision-making power beyond IS are not significant

Mitigating the Bullwhip Effect and Enhancing Supply Chain Performance through Demand Information Sharing: An ARENA Simulation Study

The supply chain is a network of organizations that collaborate and leverage their resources to deliver products or services to end-customers. In today's globalized and competitive market, organizations must specialize and form partnerships to gain a competitive edge. To thrive in their respective industries, organizations need to prioritize supply chain coordination, as it is integral to their business processes.   Supply chain management focuses on the collaboration of organizations within the supply chain. However, when each echelon member optimizes their goals without considering the network's impact, it leads to suboptimal performance and inefficiencies. This phenomenon is known as the Bullwhip effect, where order variability increases as it moves upstream in the supply chain. The lack of coordination, unincorporated material and information flows, and absence of ordering rules contribute to poor supply chain dynamics. To improve supply chain performance, it is crucial to align organizational activities. Previous research has proposed solutions to mitigate the Bullwhip effect, which has been a topic of intense study for many decades. This research aims to investigate the causes and mitigations of the Bullwhip effect based on existing research. Additionally, the paper utilizes ARENA simulation to examine the impact of sharing end-customer demand information. As far as we are aware, no study has been conducted to deeply simulate the bullwhip effect using the ARENA simulation. Previous studies have investigated this phenomenon, but without delving into its intricacies. The simulation results offer potential strategies to mitigate the Bullwhip effect through demand information sharing. Keywords: Supply Chain Management, Bullwhip effect, Inventory management, ARENA simulation, Information sharing, forecasting technique, Demand variability. DOI: 10.7176/JESD/14-14-07 Publication date:August 31st 202

Information Sharing for improved Supply Chain Collaboration – Simulation Analysis

Collaboration among consumer good’s manufacturer and retailers is vital in order to elevate their performance. Such mutual cooperation’s, focusing beyond day to day business and transforming from a contract-based relationship to a value-based relationship is well received in the industries. Further coupling of information sharing with the collaboration is valued as an effective forward step. The advent of technologies naturally supports information sharing across the supply chain. Satisfying consumers demand is the main goal of any supply chain, so studying supply chain behaviour with demand as a shared information, makes it more beneficial. This thesis analyses demand information sharing in a two-stage supply chain. Three different collaboration scenarios (None, Partial and Full) are simulated using Discrete Event Simulation and their impact on supply chain costs analyzed. Arena software is used to simulate the inventory control scenarios. The test simulation results show that the total system costs decrease with the increase in the level of information sharing. There is 7% cost improvement when the information is partially shared and 43% improvement when the information is fully shared in comparison with the no information sharing scenario. The proposed work can assist decision makers in design and planning of information sharing scenarios between various supply chain partners to gain competitive advantage

Optimal Supply Network with Vendor Managed Inventory in a Healthcare System with RFID Investment Consideration

Supply Chain Management in the healthcare sector faces several significant challenges, including complexity in healthcare systems, high supply chain costs, balancing quality and costs, delay in delivery, product availability from vendors, inventory waste, and unpredictability and uncertainty. Among those challenges, having an effective inventory management system with an optimal supply network is important to improve the match between supply and demand, which would improve the performance of for healthcare firms. Vendor Managed Inventory (VMI) system is a replenishment solution in which the vendor monitors and decides the time and the quantity of the inventory replenishment of their customers subject to their demand information exchange. A VMI contract in the location-inventory assignment problem is a decision tool for management in the healthcare industry, in which it enables the management to have a cost and service effective decision tool to critically re-evaluate and examine all areas of operations in a SC network looking for avenues of optimization. This dissertation is based on a real-world problem arising from one of the world\u27s leading medical implant supply company applied to a chain of hospitals in the province of Ontario. The chain of hospitals under study consists of 147 hospitals located in Ontario, Canada. The vendor is a supplier of three types of medical implants (a heart valve, an artificial knee, and a hip). In Chapter 2 of this dissertation, we present an optimal supply healthcare network with VMI and with RFID consideration, in which we shed light on the role of the VMI contract in the location-inventory assignment problem and integrate it with both the replenishment policy assignment and the Radio Frequency Identification (RFID) investment allocation assignment in healthcare SC networks using both VMI and direct delivery policies. A numerical solution approach is developed in the case of the deterministic demand environment, and we end up with computational results and sensitivity analysis for a real-world problem to highlight the usefulness and validate the proposed model. We extend our research of integrating the VMI contract in the location-inventory assignment problem with the replenishment policy assignment under a deterministic demand environment to include the stochastic demand environment. The impact of the uncertainty of the demand as a random variable following two types of distributions, normal and uniform distributions, is studied in Chapter 3. Motivated by the lack of investigations and comparative studies dealing with the preference of dealing with VMI contracts to other traditional Retailer Managed Inventory (RMI) systems, we provide in Chapter 4 of this dissertation a comparative study in which we compare the total cost of the VMI system with another two situations of traditional RMI systems: first, a traditional RMI system with a continuous replenishment policy for all hospitals and with assigned storage facilities and second, a traditional RMI system with a direct delivery policy for all hospitals without assigning a storage facility. Computational results, managerial insights, sensitivity analysis, and solution methodologies are provided in this dissertation. Keywords: Vendor Managed Inventory, healthcare system, location-inventory, RFID technology, supply-chain network, stochastic demand, location-inventory assignment problem, and retailer managed Inventory