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Inventory Management
Published in N.V.S. Raju, Operations Research, 2019
The main objective of inventory control is to minimise the overall investment (or costs) or inventory carrying at lowest possible level and consistency in operating requirements. However, the following may also be considered as sub-objectives of inventory control. To minimise carrying cost of inventory.To supply the finished product/raw material/sub-assemblies/semi-finished goods etc., to the users as per their requirements at right time and at right price.To minimise the inactive, surplus, waste, scrap, obsolete, spoilage materials.To reduce the shortage costs.To minimise replacement costs.To maximise production efficiency and distribution effectiveness.To maximise the reutrn on investment (ROI).
The Operations Plan
Published in David C. Kimball, Robert N. Lussier, Entrepreneurship Skills for New Ventures, 2020
David C. Kimball, Robert N. Lussier
While looking at the question of inventory control, it is necessary to understand the concept of EOQ, the amount of goods whose total costs, procurement, and carrying costs are at a minimum. You arrive at this quantity by balancing carrying cost, those costs associated with ordering material from the ordering cost through receiving, inspection, and storage, and procurement costs, which are made up of requisition costs, follow-up tasks, and payment of invoices.
Balanced Scorecard and Project Risk
Published in Jessica Keyes, Implementing the Project Management Balanced Scorecard, 2010
Table 10.6 and Figure 10.2 show the resulting project chart and priority graph, respectively, that diagram this PQM technique. The team's mission, in this example, is to introduce just-in-time (JIT) inventory control, a manufacturing technique that fosters greater efficiency by promoting stocking inventory only to the level of need. The team, in this example, identified six CSFs and eleven business processes labeled P1 through P11.
Applying the zero-inflated Poisson regression in the inventory management of irregular demand items
Published in Journal of Industrial and Production Engineering, 2022
Serena Finco, Daria Battini, Giuseppe Converso, Teresa Murino
Nowadays, from the companies’ side perspective, most of them (mainly medium and large size companies) use inventory models through either specific inventory control software or more general ERP software. However, inventory models rely on a complete certainty of the future demand distribution [2]. For this reason, when demand is unknown, or it is irregular, traditional approaches cannot work efficiently. Consequently, managers are facing higher or wrong inventory levels with increasing costs. This is particularly true when products have a short life cycle, such as electronic items or furniture. In such contexts, a good inventory management system could help in reducing costs while improving efficiency and customer satisfaction [3]. Inaccurate forecasts can be costly for company operations, in terms of stock-outs and lost sales, or over-stocking, while not meeting service-level targets [4]. Two are the main features of irregular demand items: their demand is drastically variable and extremely unstable, and then, their demand interval time is long and uncertain [5].
Simulation optimisation methods applied in reverse logistics: a systematic review
Published in International Journal of Sustainable Engineering, 2021
Salma Abid, Fatima Zahra Mhada
Managing inventory is always a complicated and crucial task, whether it is in the forward supply chain or the reverse supply chain. Inventory control determines when to check the quantity of the stock,,hen to replenish it and how many goods to order. However, the customer demand is uncontrollable. For this purpose, various classical inventory control models have been established to maintain a proper inventory level while keeping the cost minimum (Vats, Soni, and Rathore 2018), such as economic order quantity (EOQ) which determines the optimal order quantities for purchasing and manufacturing (Alfares and Ghaithan 2019), just-in-time inventory control, ABC analysis, and safety stock levels. The inventory control (IC) problem has been tackled using simulation optimisation techniques. The most dominant SO methods used to tackle CLSC problems are: DES & GA, DES & Heuristic, MCS & Heuristic, DES & TS, and TS ANNs SS & DES, NS & meta-heuristics.
AI-enabled Enterprise Information Systems for Manufacturing
Published in Enterprise Information Systems, 2022
Milan Zdravković, Hervé Panetto, Georg Weichhart
Inventory control is the process of managing supply, storage, and distribution of stock, with the appropriate quantities to meet customer demand and still maintain minimum stock levels so as to reduce inventory costs and minimise risk of excess items. Internal and external logistics, namely the movement of materials and the support operations that occur within a company are crucial for the effective inventory control. The most of AI applications in this domain are classified into the inventory control function, including stock-taking and tracking, forecasting, back-orders, replenishment rules and ordering policies. Other uses of ML are notable in autonomous robotic operations in internal logistics and transport.