Determining the Trade-offs of Inventory Management Approaches in the Face of Covid-19

2021 ◽  
Author(s):  
Valentas Gružauskas ◽  
Aurelija Burinskienė
Keyword(s):  
Supply Chain ◽  
Inventory Management ◽  
Scientific Study ◽  
Inventory Level ◽  
Delivery Times ◽  
Supply Policy ◽  
Trade Offs ◽  
Wide Range ◽  
Management Approaches ◽  
Key Aspects

The COVID-19 pandemic has left a clear mark on virtually every area of human activity. Arguably, most prominent changes may be observed in the global supply chain where the delivery times have changed, and even minor outbreaks of the pandemic pose ever-increasing risks in logistics, supply, and infrastructure. The authors of the scientific study analyze supply chain management approaches and deal with the key aspects which are most suitable to tackle the COVID-19 crisis. This study identifies which area caused by the pandemic has been most problematic and proposes strategies and methods to help companies properly manage their stockpiles. Another important aspect of this scientific study is that the analysis of inventory management methods in a critical environment is performed by developing an agent-based model. Thus, the data from this study are completely new and allow a closer look and understanding of how the COVID-19 pandemic has affected the supply and stockpiling/storage issues. In this research, the authors focus on the supplier relationship and inventory level management. Here, they examine several different business scenarios, such as: central vs. distributed warehouses, local vs. global suppliers, etc. Due to the wide range of information, this book should attract not only those who are profoundly interested in the field but also inquisitive newcomers with an interest in the trade and product supply policy.

Inventory Management in Competition Environment Based on Customer Service

2011 ◽  
Vol 58-60 ◽  
pp. 2141-2146
Author(s):  
Xiao Di ◽  
Bao Xing
Keyword(s):  
Supply Chain ◽  
Supply Chains ◽  
Customer Service ◽  
Inventory Management ◽  
Demand Uncertainty ◽  
Management Policy ◽  
Management Decision ◽  
Inventory Level ◽  
On Demand ◽  
Optimal Policies

Based on demand uncertainty, the paper studies inventory management decision of two competing supply chains from the perspective of customer service. The paper mainly discusses two different inventory strategies, which are widely used, that is, consignment stock and VMI, and analyzes the optimal policies under three competitive scenarios, which consist of using consignment stock in both supply chains (CC mode), using VMI in both supply chains (DD mode), and using consignment stock in one supply chain but VMI another (VC mode). The paper compares equilibrium inventory level and profit of supply chain in different competitive modes, and concludes that both supply chains use VMI is equilibrium, which means that when manufacturers have right to choose inventory management policy, they prefer VMI. But it isn’t paradoxical with the phenomenon that consignment stock is common in reality, because manufacturers are forced to use consignment by retailer’s channel power.

An Exploratory Study on Blockchain Application in a Food Processing Supply Chain to Reduce Waste

2019 ◽  
pp. 61-85
Author(s):  
Emily Anne Carey ◽  
Nachiappan Subramanian
Keyword(s):  
Supply Chain ◽  
Inventory Management ◽  
Single Case ◽  
Food Product ◽  
Secondary Source ◽  
Cross Sectional ◽  
Study Approach ◽  
Delivery Times ◽  
Beef Supply Chain

This chapter aims to explore the feasibility of using blockchain in the beef supply chain to reduce waste. A mono-method, qualitative, inductive, single case study approach was taken on a cross-sectional scale from June 2018 to August 2018, with two individuals interviewed: a beef and a blockchain expert. The case study also involved observations, a field visit, and other secondary source data. Beef is a high demand, valuable food product with a limited shelf life. By using blockchain in conjunction with RFID and sensor technologies, farming and processing stages in the beef supply chain can be streamlined. Firstly, using the technology to monitor the animals on the farm and during transportation can reduce the amount of water and energy wasted. Secondly, blockchain can be used to establish exactly when and where the meat is cut and packaged, improving the accuracy of information between supply chain entities, resulting in improved inventory management, specifically more accurate delivery times and lengthened product shelf lives.

Justifying RFID Investment to Enable Mobile Service Applications in Manufacturing and Supply Chain

2013 ◽  
pp. 325-348 ◽  
Author(s):  
In Lee
Keyword(s):  
Supply Chain ◽  
Inventory Management ◽  
Radio Frequency Identification ◽  
Business Case ◽  
Mobile Service ◽  
Wide Range ◽  
High Investment ◽  
Potential Benefits ◽  
Frequency Identification ◽  
Investment Justification

Radio Frequency Identification (RFID) technology is rapidly expanding its application area from simple inventory management to advanced location tracking and supply chain management in a wide range of industries. Because of the potential benefits gained and high investment costs incurred by RFID, firms need to carefully assess every RFID opportunity and challenge to ensure that their resources are spent judiciously. Because of the lack of analytical methods for measuring the benefits and costs, this chapter presents a mathematical model for the evaluation of RFID investment in manufacturing and supply chain. This model provides a basis for the authors’ understanding of RFID value creation and ways to build an RFID business case for an RFID investment justification.

A Mechanism Design Approach to Vendor Managed Inventory

2020 ◽  
Vol 66 (6) ◽  
pp. 2628-2652 ◽  
Author(s):  
Bharadwaj Kadiyala ◽  
Özalp Özer ◽  
Alain Bensoussan
Keyword(s):  
Supply Chain ◽  
Inventory Management ◽  
Operations Management ◽  
Inventory Level ◽  
Local Market ◽  
Management Problem ◽  
Vendor Managed Inventory ◽  
Market Conditions ◽  
Market Signals ◽  
Pos Data

This paper studies an inventory management problem faced by an upstream supplier that is in a collaborative agreement, such as vendor-managed inventory (VMI), with a retailer. A VMI partnership provides the supplier an opportunity to manage inventory for the supply chain in exchange for point-of-sales (POS)- and inventory-level information from the retailer. However, retailers typically possess superior local market information and as has been the case in recent years, are able to capture and analyze customer purchasing behavior beyond the traditional POS data. Such analyses provide the retailer access to market signals that are otherwise hard to capture using POS information. We show and quantify the implication of the financial obligations of each party in VMI that renders communication of such important market signals as noncredible. To help institute a sound VMI collaboration, we propose learn and screen—a dynamic inventory mechanism—for the supplier to effectively manage inventory and information in the supply chain. The proposed mechanism combines the ability of the supplier to learn about market conditions from POS data (over multiple selling periods) and dynamically determine when to screen the retailer and acquire his private demand information. Inventory decisions in the proposed mechanism serve a strategic purpose in addition to their classic role of satisfying customer demand. We show that our proposed dynamic mechanism significantly improves the supplier’s expected profit and increases the efficiency of the overall supply chain operations under a VMI agreement. In addition, we determine the market conditions in which a strategic approach to VMI results in significant profit improvements for both firms, particularly when the retailer has high market power (i.e., when the supplier highly depends on the retailer) and when the supplier has relatively less knowledge about the end customer/market compared with the retailer. This paper was accepted by Gad Allon, operations management.

scholarly journals Graph Database to Enhance Supply Chain Resilience for Industry 4.0

2022 ◽  
Vol 15 (1) ◽  
pp. 0-0
Keyword(s):  
Supply Chain ◽  
Data Analysis ◽  
Inventory Management ◽  
Industry 4.0 ◽  
Query Language ◽  
Graph Model ◽  
Modeling Framework ◽  
Supply Chain Resilience ◽  
Graph Data ◽  
Wide Range

Supply chain network in the automotive industry has complex, interconnected, multiple-depth relationships. Recently, the volume of supply chain data increases significantly with Industry 4.0. The complex relationships and massive volume of supply chain data can cause visibility and scalability issues in big data analysis and result in less responsive and fragile inventory management. The authors develop a graph data modeling framework to address the computational problem of big supply chain data analysis. In addition, this paper introduces Time-to-Stockout analysis for supply chain resilience and shows how to compute it through a labeled property graph model. The computational result shows that the proposed graph data model is efficient for recursive and variable-length data in supply chain, and relationship-centric graph query language has capable of handling a wide range of business questions with impressive query time.

A Colored Petri Net-based modeling method for supply chain inventory management

SIMULATION ◽  
2021 ◽  
pp. 003754972110387
Author(s):  
Maria Drakaki ◽  
Panagiotis Tzionas
Keyword(s):  
Supply Chain ◽  
Inventory Management ◽  
Petri Net ◽  
Simulation Modeling ◽  
Modeling Method ◽  
Operating Conditions ◽  
Supply Chain Performance ◽  
Colored Petri Net ◽  
Wide Range ◽  
The Impact

Supply chain planning and control approaches need to include a wide range of factors in order to optimize production. Supply chain simulation modeling has been identified as a potential methodology toward increasing the efficiency of current systems to this end. The purpose of this paper is to evaluate the impact of inventory management decisions on supply chain performance using a Colored Petri Net based simulation modeling method. The presented method uses hierarchical timed Colored Petri Nets to model inventory management in a multi-stage serial supply chain, under normal operating conditions, and under the presence of disruptions, for both traditional and information sharing configurations. Disruptions are introduced as canceled orders and canceled deliveries, in a time period. Supply chain performance has been evaluated, in the context of order variance amplification and stockout amplification. Validation of the method is done by comparing results obtained for the bullwhip effect with published literature results, as well as by state space analysis results.

scholarly journals Development of a hybrid framework for inventory leanness in Technical Services Organizations

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0247144
Author(s):  
Khurram Rehmani ◽  
Afshan Naseem ◽  
Yasir Ahmad ◽  
Muhammad Zeeshan Mirza ◽  
Tasweer Hussain Syed
Keyword(s):  
Comparative Analysis ◽  
Inventory Management ◽  
High Price ◽  
Inventory Level ◽  
Forecasting Model ◽  
Optimum Level ◽  
Annual Budget ◽  
Wide Range ◽  
Hybrid Framework ◽  
Technical Services

Inherent uncertainties in demand and supply make it problematic for supply chains to accomplish optimum inventory replenishment, resulting in loss of sales or keeping excessive inventories. To cope with erratic demands, organizations have to maintain excessive inventory levels, sometimes taking up to one-third of an organization’s annual budget. The two most pressing concerns to handle in inventory management are: how much to order and when to order. Therefore, an organization ought to make the correct and timely decisions based on precise demand information to avoid excessive inventory accumulation resulting in enhanced competitive advantage. Owing to the significance of inventory control and analysis, this paper reports on developing and successfully implementing a hybrid framework for optimum level inventory forecasting in Technical Services Organizations. The proposed framework is based on a case study of one of Pakistan’s leading Technical Services Organization. The paper presents a statistical analysis of historical data and a comprehensive fault trend analysis. Both these analyses set a solid foundation for the formulation of a comparative analysis matrix based upon price and quantity based analysis of inventory. Finally, a decision criterion (Forecasting Model) is proposed using three primary forecasting techniques with minimum error calculations. The study’s finding shows a forecast error of 142.5 million rupees in the last five years, resulting in the accumulation of more than 25 thousand excessive inventory stock. Application of price and quantity based analysis identifies that 65% of the annual budget is significantly dependent upon only 9% (in terms of quantity) of "High Price and Small Quantity" Items (HS). These HS items are forecasted through three different forecasting methods, i.e., Weighted Moving Average, Exponential Smoothing, and Trend Projection, with Minimum Absolute Deviation to significantly reduce the forecasting error while predicting the future required quantity. The research work aims to contribute to the inventory management literature in three ways. First, a new comparative analysis matrix concept for identifying the most critical items is introduced. Second, a Multi-Criteria Forecasting Model is developed to capture a wide range of operations. Third, the paper suggests how these forecasting criteria can be integrated into a single interactive DSS to maintain optimum inventory level stock. Even though the DSS framework is based on data from a single organization, the application is expected to manage inventory stock in a wide range of manufacturing and services industries. This study’s proposed hybrid framework is the first of its kind that encapsulates all four dimensions of inventory classification criteria, forming a multi-criteria hybrid model within a DSS framework.

An Exploratory Study on Blockchain Application in a Food Processing Supply Chain to Reduce Waste

2021 ◽  
pp. 376-394
Author(s):  
Emily Anne Carey ◽  
Nachiappan Subramanian
Keyword(s):  
Supply Chain ◽  
Inventory Management ◽  
Single Case ◽  
Food Product ◽  
Secondary Source ◽  
Cross Sectional ◽  
Study Approach ◽  
Delivery Times ◽  
Beef Supply Chain

This chapter aims to explore the feasibility of using blockchain in the beef supply chain to reduce waste. A mono-method, qualitative, inductive, single case study approach was taken on a cross-sectional scale from June 2018 to August 2018, with two individuals interviewed: a beef and a blockchain expert. The case study also involved observations, a field visit, and other secondary source data. Beef is a high demand, valuable food product with a limited shelf life. By using blockchain in conjunction with RFID and sensor technologies, farming and processing stages in the beef supply chain can be streamlined. Firstly, using the technology to monitor the animals on the farm and during transportation can reduce the amount of water and energy wasted. Secondly, blockchain can be used to establish exactly when and where the meat is cut and packaged, improving the accuracy of information between supply chain entities, resulting in improved inventory management, specifically more accurate delivery times and lengthened product shelf lives.

scholarly journals Analysis of the effects of Lead-Time variation and Lateral Transshipment insertion in a Supply Chain Network

2021 ◽  
Author(s):  
Mohamed Salim Amri Sakhri ◽  
Mounira Tlili ◽  
Ouajdi Korbaa
Keyword(s):  
Supply Chain ◽  
Inventory Management ◽  
Planning Horizon ◽  
Inventory Level ◽  
Inventory Routing ◽  
Routing Problem ◽  
Network Cost ◽  
Lateral Transshipment ◽  
The Impact ◽  
Number Of Customers

Abstract In a supply chain, inventory is the single largest source of costs for a company. This is due to the various physical and informational activities that accompany inventory management, primarily the holding and transportation of inventory. Companies are looking to streamline these activities and minimize the associated costs. One of the most coveted models to jointly solve these two problems is the Inventory Routing Problem (IRP), which will be the focus of this study. This paper addresses the case of a deterministic replenishment demand in a distribution network consisting of a supplier and a number of customers to be served by a single vehicle over a finite planning horizon. We will first study the impact of increasing supplier lead times on network costs. Then, we will study the effects of the Lateral Transshipment (LT) technique on the overall network cost. A mathematical model is developed and solved by an exact method. The results obtained will show that LT is an effective tool capable of improving the total network cost and balancing the customers’ inventory level.

scholarly journals Implications of additive manufacturing on supply chain and logistics

2020 ◽  
Vol 11 (4) ◽  
pp. 1279
Author(s):  
Daniel R. Tasé Velázquez ◽  
Alexandre Tadeu Simon ◽  
André Luís Helleno ◽  
Lorena Hernández Mastrapa
Keyword(s):  
Supply Chain ◽  
Additive Manufacturing ◽  
Literature Review ◽  
Inventory Management ◽  
Raw Materials ◽  
Lead Times ◽  
Manufacturing Strategy ◽  
Consumer Market ◽  
Key Issues ◽  
Key Aspects

Additive manufacturing (AM) technology has attracted the interest of industrial professionals and researchers in the last years. This interest lies primarily in understanding the trends, benefits, and implications of AM technology on supply chain (SC) and logistics, as it requires reconfiguring the supply chain based on a distributed manufacturing strategy, closer to the consumer market, with shorter lead times and less raw materials. It still is an emerging field, and needs further study. Therefore, a better understanding of main trends will contribute to the dissemination of knowledge about AM technology and its consolidation. This article seeks to investigate the implications of AM, as an advanced manufacturing model, on SC and logistics. A four-step research method was used to develop a systematic literature review and a bibliometric analysis on the AM implications in SC and logistics. The main implications of AM on SC and logistics were classified in seven key issues gathered as result of the literature review. Additionally, bibliometric study allowed understanding researches major trends in this field. The key aspects highlighted and characterized as major implications of AM on SC and logistic are: supply chain complexity reduction; more flexible logistics and inventory management; better spreading and popularization of mass customization; decentralization of manufacturing; greater design freedom and rapid prototyping; increasing of resource efficiency and sustainability, and the need to have clearly defined legal and safety aspects.

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