Abstract
Goal: The main goal of this research is to analyse the behaviour of a set of ten lot-sizing methods applied to different application scenarios, within the context of more traditional MRP-based manufacturing environments and on JIT/ Kanbans oriented ones.
 Design/Methodology/Approach: After an extended literature review, a quantitative research method is used to provide a comparative analysis on the performance of the lot-sizing methods under different simulated application scenarios, with variations in demand and peaks of seasonality. Moreover, a final summary provides the error deviations for lot-sizing methods regarding increases in demand variations and seasonality indexes.
 Results: The study analyses lot-sizing methods and discusses benefits and risks associated to its use in application scenarios marked by a considerable variation in demand or peaks in seasonality.
 Limitations of the investigation: As the application scenarios did not explore variations in the ordering and stock holding costs, further analysis including these kinds of variations is encouraged.
 Practical implications: The findings of this research enable the enhancement of the conscience of industrial practitioners, regarding the selection of best suited lot-sizing methods for being applied on each kind of manufacturing scenario, regarding MRP or JIT/ Kanban environments.
 Originality/Value: Given the diversity of the existing lot-sizing methods, for instance, the heuristic ones, authors can find it quite difficult to select appropriate methods for solving their problems for each kind of application scenario. Therefore, the present study can provide useful knowledge to better support decision making in the lot-sizing domain.
Highlights
The industry has been increasingly stimulated to become more efficient due to the exponential growth of the market, and its endless competition
Material Requirements Planning (MRP)-based Material Management Policies (M&MMP) and systems materials are usually planned in advance to guarantee typically a more or less widened amount of materials regarding some possible range of variation in relation to material types and quantities, but which intend to be predictable and preferably known and established in advance, to be able to accurately carry out production and/or material acquisition plans
With index 33, variable Economic Order Quantity (EOQ) performed a 2.4% error and decreased to 0.3% with index 233. Through this information analysis it is possible to conclude that the methods EOQ, LFL, var-EOQ, and Least Unit Cost (LUC) clearly present higher application risk in scenarios marked by a considerable deviation in demand, and the methods EOQ, LFL, S-M, and LUC, regarding an increase in seasonality peaks, which typically occur more frequently in the context of MPRbased manufacturing contexts, associated with higher variation of materials, lot sizes or orders batches, and are typically produced in function or process-oriented manufacturing systems
Summary
The industry has been increasingly stimulated to become more efficient due to the exponential growth of the market, and its endless competition These factors compel companies to improve their profitability by optimizing their production and planning systems. The remaining sections of this paper are organized as follows: the section revises lot sizing in Material Requirements Planning (MRP) and JIT/Kanban (Wang et al, 2017, Ani et al, 2018) Manufacturing and Material Management Policies (M&MMP). A section is dedicated to shortly present several lot-sizing methods considered in this research. A more detailed analysis about the suitability of the considered methods regarding MRP and JIT/Kanban manufacturing scenarios is described. Some main conclusions and future work intentions are presented
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