Analysis of lot-sizing methods’ suitability for different manufacturing application scenarios oriented to MRP and JIT/Kanban environments

Florim, Wilson; Dias, Paulo Sousa; Santos, Alex José Souza dos; Varela, Leonilde; Madureira, Ana; Putnik, Goran D.

doi:10.14488/bjopm.2019.v16.n4.a9

Cited by 4 publications

(6 citation statements)

References 41 publications

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“…This classification enables the researchers to focus on a certain class of the problem. The performance of one type may not be same for another as shown by Florim et al (2019).…”

Section: Literature Reviewmentioning

confidence: 98%

Evaluation of the Production Planning Policy Alternatives in a Pet Resin Production Plant: a Case Study From Turkey

Demirkan

Durmuşoğlu²

2020

BJO&PM

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Goal: The main purpose of this work is to develop several production planning models for one of bottle grade PET production plants (in Turkey) under different scenarios by considering different levels of inventory (no stock and different levels of safety stocks), different energy sourcing alternatives (natural gas/coal or both) and production throughput (different lot sizes). Design/Methodology/Approach: Deterministic multi-product multi-period single level mixed integer linear programming model is presented. The model is implemented for the above defined scenarios. We have also considered the total emission produced for different energy alternatives. Results: The models for the different scenarios have shown that significant performance improvements can be achieved by changing the parameters/policies of the production. The best model obtained has yielded 6.6% of profit improvement and 6.9% of cost reduction. Limitations of the investigation: Planning horizon of this work could cover the data of years 2014 to 2018. However, the actual data employed for this study is limited with one year due to unavailability of further data. In addition to this, improvement of current production planning approach of the plant by implementation of proposed model is one of the objectives of this study to see success rate of model. Though, this couldn’t be realized. We hypothetically tested the improvement provided by the proposed model, therefore we can’t analyze the actual improvement. Practical implications: Interactions among optimal results obtained by running model with different scenarios and their effect on selected performance variables are the main contribution of this work. Similar methodology could be used at other PET resin manufacturing plants or alternatively the production facilities having similar type of continuous manufacturing processes to find their optimal levels of inventory, production throughput and proper choice for energy sourcing alternative. Originality/Value: The research on the production planning for PET resin industry by considering the different scenarios (levels of inventory / energy sourcing alternatives/ production throughput) is novel as far as we know, and therefore the approach used in this study is expected to be useful for other similar plants.

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“…This classification enables the researchers to focus on a certain class of the problem. The performance of one type may not be same for another as shown by Florim et al (2019).…”

Section: Literature Reviewmentioning

confidence: 98%

Evaluation of the Production Planning Policy Alternatives in a Pet Resin Production Plant: a Case Study From Turkey

Demirkan

Durmuşoğlu²

2020

BJO&PM

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“…Lot sizing is one of the problems in production planning. Because of its complexity and importance, lot sizing has been widely studied as both an academic and real-life problem [5].…”

Section: Introductionmentioning

confidence: 99%

“…In MRP, there are wide variety of methods for approaching the problem of lot sizing [8]. Florim et al [5] examine the behavior of 9 different lot sizing methods regarding different manufacturing environments and underlying production philosophies and related application specifications. The 9 methods include Lotfor-Lot (LFL), Economic Order Quantity (EOQ), Periodic Order Quantity (POQ), Silver-Meal (S-M) or Least Period Cost (LPC), Least Unit Cost (LUC), Part-Period Balance (PPB), Freeland & Cooley, McLaren Order Moment, and Wagner-Within (WW).…”

Section: Introductionmentioning

confidence: 99%

Inventory Planning and Control Method for Cement Raw Material With Material Requirement Planning (Mrp)

Saptadi,

Aulia Zahra,

Arvianto

et al. 2023

IJASER

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The supply of raw materials is an important asset for the company. Inaccuracies in the preparation of raw material supplies can have an impact on the production process and incur greater inventory costs. Therefore, more accurate planning of raw material supplies is needed so that there are no excesses or shortages of stock. This research aims to find out inventory policies, control the availability of gypsum and trass raw materials, and minimize inventory costs. In planning gypsum and trass inventory control, the Material Requirement Planning (MRP) method will be used. MRP methods used in this paper are Lot-for-Lot (LFL), Economic Order Quantity (EOQ), and Periodic Order Quantity (POQ) as a comparison for companies in planning gypsum and trass inventory. Calculations are carried out theoretically as well as with a real approach to the terms of the quantity of purchases of the company. For gypsum material, the most minimum supply is generated by the LFL method, both theoretically and with a real approach. Theoretically, the resulting savings for gypsum are 2.80% and with a real approach the resulting savings of 1.88%. For trass materials, the most minimum supply is also generated by the LFL method. Theoretically, the resulting savings for trass are 0.78% and with a real approach the resulting savings are 0.11%. It can be proposed to the company to apply the LFL method to obtain the most minimum inventory costs with the amount of inventory corresponding to the number of net needs in the corresponding period.

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“…In production planning, making the proper decisions about the lot size is extremely important because it directly affects the system performance and productivity [7] and this is often key for any manufacturing firm that desires to compete on market. As this is often complex moreover as important, it's been highly studied although, there's still a niche about showing the contributions to clarify the suitability of these methods used concerning each quite underlying manufacturing environment (regarding variations in demand and peaks of seasonality) [28]. Therefore this study aimed toward the…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization of Manufacturing Lot Size Under Stochastic Demand

S.¹

2022

ijcsrr

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In many manufacturing problems, multi-objective optimizations are representative models, as objectives are considered a conflict with one another. In real-life applications, optimizing a specific solution concerning one objective may end up in unacceptable results concerning the other objectives. Many Manufacturing companies operate under uncertainties and this affects the system performance. Stochastic product demand is one of the challenges faced by manufacturing companies and often affects the manufacturing system’s performance and decision-making. Making the proper decisions regarding manufacturing lot-sizing problems is critical for any manufacturer because it makes the firm compete within the market. In this paper, Markov chains in conjunction with stochastic goal programming were used to develop an optimization model for the manufacturing lot size. The over-achievement or under-achievement of the manufacturing lot size was determined by defining the goal constraints, deviation variables, priorities, and objective function. The different states of demand for the product with stochastic demand were represented by states of a Markov chain. Using the applied mathematics solver in MATLAB TM, the optimization model was then solved, determining the quantity of product to be manufactured in a given quarter of the year as demand changes from one state to another.

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Analysis of lot-sizing methods’ suitability for different manufacturing application scenarios oriented to MRP and JIT/Kanban environments

Cited by 4 publications

References 41 publications

Evaluation of the Production Planning Policy Alternatives in a Pet Resin Production Plant: a Case Study From Turkey

Evaluation of the Production Planning Policy Alternatives in a Pet Resin Production Plant: a Case Study From Turkey

Inventory Planning and Control Method for Cement Raw Material With Material Requirement Planning (Mrp)

Multi-Objective Optimization of Manufacturing Lot Size Under Stochastic Demand

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