Determining Lot Sizes in Production Areas – Exact Calculations versus Research Based Estimation

Schmidt, Matthias; Münzberg, Ben; Nyhuis, Peter

doi:10.1016/j.procir.2015.04.024

Cited by 14 publications

(5 citation statements)

References 8 publications

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“…Mathematical programming in warehouses was also investigated in a study by Bolanos Zuniga et al [9]. Some studies concentrated on the literature review of economic order quantity such as Holmbom and Segerstedt [4] and Schmidt et al [10]. The first attempt to include the trend in demand was by Donaldson [11], who used substantial calculations.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimizing Inventory Replenishment for Seasonal Demand with Discrete Delivery Times

2021

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This study investigates replenishment planning in the case of discrete delivery time, where demand is seasonal. The study is motivated by a case study of a soft drinks company in Germany, where data concerning demand are obtained for a whole year. The investigation focused on one type of apple juice that experiences a peak in demand during the summer. The lot-sizing problem reduces the ordering and the total inventory holding costs using a mixed-integer programming (MIP) model. Both the lot size and cycle time are variable over the planning horizon. To obtain results faster, a dynamic programming (DP) model was developed, and run using R software. The model was run every week to update the plan according to the current inventory size. The DP model was run on a personal computer 35 times to represent dynamic planning. The CPU time was only a few seconds. Results showed that initial planning is difficult to follow, especially after week 30, and the service level was only 92%. Dynamic planning reached a higher service level of 100%. This study is the first to investigate discrete delivery times, opening the door for further investigations in the future in other industries.

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Section: Literature Reviewmentioning

confidence: 99%

Optimizing Inventory Replenishment for Seasonal Demand with Discrete Delivery Times

2021

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“…Besarnya jumlah kebutuhan tidak berdasarkan ramalan, tetapi dengan cara menjumlahkan kebutuhan bersih pada periode yang akan datang. Dalam FPR ini, selang waktu antar pemesanan dibuat tetap dengan ukuran lot sesuai pada kebutuhan bersih (Schmidt et al, 2015).…”

Section: Gambar 2 Grafik Pengaruh Biaya Dalam Eoqunclassified

Perencanaan persediaan bahan baku amoxicillin menggunakan metode material requirement planning: studi kasus

Pratiwi¹,

Hasibuan

2020

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PT. XYZ is a pharmaceutical company that produces Amoxicillin. In managing its inventory, PT XYZ has not done a good plan to determine the size of raw material orders. The existence of these problems, a study was conducted with the aim of determining Amoxicillin raw material inventory planning to eliminate the accumulation of raw materials at future. The initial stage is forecasting using three methods, namely Linear Regression, Exponential Smoothing and Moving Average. Of the three forecasting methods, the Linear Regression method provides the smallest error accuracy. The chosen method of forecasting must be carried out in advance of verification test (Moving Range) to be used as a basis for planning future raw material requirements. Furthermore, calculations are carried out using the MRP method to determine the size of the order lot for each raw material and reduce the cost of saving. The lot size technique used includes Lot for Lot (LFL), Economic Order Quantity (EOQ), and Fixed Period Requirement (FPR). Of the three lot size techniques used, the LFL method provides the lowest total cost of inventory.

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“…The quantities requested by the customer directly transform into production orders. Thus, the realization of economic production orders is not possible [15]. As shown in ETO industry, real-time capable production planning and control can decrease complexity [16].…”

Section: Economic and Logistic Objectivesmentioning

confidence: 99%

Supporting the Decision of the Order Processing Strategy by Using Logistic Models: A Case Study

Maier¹,

Heuer²,

Nyhuis³

et al. 2020

IFIP Advances in Information and Communication Technology

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The selection of a suitable order processing strategy from an economic and logistic point of view plays a fundamental role in the achievement of efficient and waste-free production processes. Many factors influence the order processing strategy and the choice of the order processing strategy affects many variables. The problem for companies that has not yet been solved is the holistic selection of the best possible order processing strategy for each product or product group and, if necessary, subordinate components. The authors present an approach to analyze the effects of the choice of the order processing strategy on the economic and logistic objectives. The description and modeling of the interdependencies between the order processing strategies and the influenced objectives refer to existing logistic models. A case study to evaluate the impact of different order processing strategies on costs shows the practicality of the proposed approach. The exemplary application of the presented approach showed a potential of an average reduction of 30 percent of the variable costs resulting from the change of the order processing strategy. The savings varied between 1 percent and 62 percent depending on the order quantity and frequency for the individual products.

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Determining Lot Sizes in Production Areas – Exact Calculations versus Research Based Estimation

Cited by 14 publications

References 8 publications

Optimizing Inventory Replenishment for Seasonal Demand with Discrete Delivery Times

Optimizing Inventory Replenishment for Seasonal Demand with Discrete Delivery Times

Perencanaan persediaan bahan baku amoxicillin menggunakan metode material requirement planning: studi kasus

Supporting the Decision of the Order Processing Strategy by Using Logistic Models: A Case Study

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