Parameter values for lot size and quality level via CAE simulation, statistical method, and mathematical programming

2021 ◽  
pp. 095440542093787
Author(s):  
Angus Jeang ◽  
Chien-Ping Chung
Keyword(s):  
Inventory Management ◽  
Quality Level ◽  
Response Functions ◽  
Lot Size ◽  
Optimal Process ◽  
Production Inventory ◽  
Production Quantity ◽  
Parameter Values ◽  
Back Order ◽  
Order Intervals

Because of the stochastic nature of production systems, it is necessary to first build an uncertainty model for subsequent real applications. Moreover, process parameter planning, quality design, and production inventory management are interdependent elements. In this research, a computer simulation model via computer-aided engineering (CAE) was developed to determine the optimal process parameters, lot size, and back order intervals for an integrated process design and inventory management system with simultaneous quality and cost considerations. Based on the estimated process time and costs obtained using CAE, the derived production rate and unit cost were then used for production inventory applications. In consideration of the uncertainty factor, the response surface method (RSM) was employed to analyze the output, namely the total costs incurred in employing the proposed approach, as well as the inputs, which include the cutting parameters, production quantity, and back order intervals. After the RSM was used to obtain the response functions, which represent the output of the collective interests, the mathematical programming (MP) was formulated based on the response functions to determine the optimal process parameters, process quality levels, production order quantities, and back order intervals. The total cost per set time unit was minimized by determining the required quality level, process parameter values, Economic Production Quantity (EPQ), and back order intervals. A cutting example was chosen to demonstrate the proposed approach. Two cases were used for comparison: the Integrated Case (the proposed approach herein) and the Disintegrated Case.

scholarly journals Random machine breakdown and stochastic corrective maintenance period on an economic production inventory model with buffer machine and safe period

2020 ◽  
Author(s):  
Brojeswar Pal ◽  
Subhankar Adhikari
Keyword(s):  
Lot Size ◽  
Maintenance Policy ◽  
Corrective Maintenance ◽  
Economic Production Quantity ◽  
Economic Production ◽  
Production Inventory ◽  
Epq Model ◽  
Production Quantity ◽  
Production Lot Size ◽  
Period Duration

In this paper, we have developed an economic production quantity (EPQ) model in which production is executed mainly by the original machine. But when the system faces disruption, the buffer of it continues the production. Here, we incorporate a fixed Safe Period running policy, in which the machine runs interruptedly, whenever production commences.  The disruption of the system may occur at any moment of the time horizon over the safe period, and then, it will go under the corrective maintenance policy. Here, we take that both of the time of disruption and period of maintenance are continuous random variables. We have discussed the model under different safe period duration with corresponding disruption situations. Our main objective is to minimize the expected average total cost for all the cases concerning the production lot size. The model has also been illustrated numerically with some examples. To examine the robustness of the solution of this model, we discuss the sensitivity analysis for the parameters.

Note on “Effects of Random Defective Rate and Imperfect Rework Process on Economic Production Quantity Model”

2005 ◽  
Vol 128 (1) ◽  
pp. 375-377 ◽  
Author(s):  
Yuan-Shyi Peter Chiu ◽  
Singa Wang Chiu
Keyword(s):  
Algebraic Approach ◽  
Lot Size ◽  
Inventory Cost ◽  
Economic Production Quantity ◽  
Economic Production ◽  
Production Inventory ◽  
Epq Model ◽  
Production Quantity ◽  
Production Lot Size ◽  
Rework Process

Conventional approaches for deriving optimal production lot size are by using the differential calculus on the production-inventory cost function with the need to prove optimality first. Recent articles proposed the algebraic approach to the solution of classic economic order quantity and economic production quantity (EPQ) model without reference to the use of derivatives. This note extends it to an EPQ model taking the random defective rate and imperfect rework process into consideration. We demonstrate that the optimal lot size can be solved algebraically and the expected inventory cost can be derived immediately as well.

DEMAND CATEGORIZATION DAN INVENTORY MANAGEMENT PRODUK OBAT: STUDI KASUS PADA SUATU TOKO OBAT DI KOTA SOLOK, SUMATERA BARAT

2020 ◽  
Vol 4 (2) ◽  
Author(s):  
Inna Kholidasari ◽  
◽  
Lestari Setiawati ◽  
Ramanda Ramanda ◽  
◽  
...  
Keyword(s):  
Inventory Management ◽  
Control Method ◽  
Moving Average ◽  
Drug Group ◽  
Linear Regression Method ◽  
Lot Size ◽  
Drug Store ◽  
Control Drug ◽  
West Sumatra ◽  
Abc Method

Abstract This research raises the problem of controlling the inventory of medicinal products. The case study was conducted at a drug store in the city of Solok, West Sumatra, where the drug store still has stock out and over stock of various types of drugs it manages. The purpose of this study was to control drug supplies that have a high demand value. Demand data for the period February-April 2020 are grouped with demand value criteria using the Pareto Classification ABC Method. From the demand categorization process, 45 types of drugs were included in A group, which means that this drug group provides a large revenue contribution for the drugstore. Thus, the type A drug group needs more attention from drug store manager in terms of controlling drug supplies. Furthermore, forecasting is carried out for drugs belonging to A drugs group using the Moving Average Method, the Single Exponential Smoothing Method and the Linear Regression Method. The inventory control method adopted is the Economic Order Quantity (EOQ) method. The results showed that the lot size of each type of drug per order ranged from 10-122 units with a safety stock ranging from 5-50 units of product. Then the reorder points obtained ranged from 8-81 units. With the control of drug supplies in place, it is hoped that the stock out and over stock that occurs in drug stores can be minimized.

A NOTE ON EPQ MODEL FOR SEASONAL PERISHABLE PRODUCTS WITH STOCK DEPENDENT DEMAND

2008 ◽  
Vol 25 (03) ◽  
pp. 301-315 ◽  
Author(s):  
S. PANDA ◽  
S. SAHA ◽  
M. BASU
Keyword(s):  
Perishable Products ◽  
Programming Technique ◽  
Economic Production Quantity ◽  
Economic Production ◽  
Time Period ◽  
Capacity Level ◽  
Proposed Model ◽  
Production Inventory ◽  
Epq Model ◽  
Production Quantity

A single item, single cycle economic production quantity model for perishable products is proposed where the demand is two-component and stock dependent. The production inventory scenario of products like cake, bread, fast foods, fishes, garments, cosmetics etc in the festival season is considered. The profit function is formulated under the assumption that the time period of the festival seasons is fixed and the display capacity of the produced item is limited. In the formulation process, to introduce more flexibility, a goal programming technique is incorporated to achieve the producer's desired profit and stock of as much inventory as possible below the display capacity level. A numerical example is presented to illustrate the proposed model. A sensitivity analysis of the model is also carried out.

scholarly journals Pengembangan Model Economic Production Quantity (EPQ) dengan Sinkronisasi Demand Kontinu dan Diskrit Secara Simultan

2016 ◽  
Vol 15 (1) ◽  
pp. 78 ◽  
Author(s):  
Nurike Oktavia ◽  
Henmaidi Henmaidi ◽  
Jonrinaldi Jonrinaldi
Keyword(s):  
Total Production ◽  
Lot Size ◽  
Inventory Cost ◽  
Economic Production Quantity ◽  
Economic Production ◽  
Holding Cost ◽  
Epq Model ◽  
Production Quantity ◽  
Set Up ◽  
Production Lot Size

The most popular inventory model to determine production lot size is Economic Production Quantity (EPQ). It shows enterprise how to minimize total production cost by reducing inventory cost. But, three main parameters in EPQ which are demand, machine set up cost, and holding cost, are not suitable to solve issues nowadays. When an enterprise has two types of demand, continue and discrete demand, the basic EPQ would be no longer useful. Demand continues comes from a customer who wants their needs to be fulfilled every time per unit time, while the fulfillment of demand discrete is at a fixed interval of time. A literature review is done by writers to observe other formulation of EPQ model. As there is no other research can be found which adopt this topic, this study tries to develop EPQ model considering two types of demand simultaneously.

scholarly journals Inventory control in a two-level supply chain with learning, quality and inspection errors

2021 ◽  
Author(s):  
Mehmood Khan
Keyword(s):  
Supply Chain ◽  
Product Quality ◽  
Inventory Management ◽  
Human Error ◽  
Threshold Value ◽  
Process Quality ◽  
Lot Size ◽  
Defective Items ◽  
Inspection Errors ◽  
Screening Cost

A common measure of quality for a buyer or a vendor is the defect rate. Defects may represent an attribute, a dimension or a quantity. They may be classified as product quality defects or process quality defects. Product quality defects may be caused by human error which can de due to fatigue, lack of proper training, or other reasons. For example, an inspector may misclassify a defective fuel tank of a car as good. On the other hand, process quality defects maybe caused by a machine going out-of-control. While many researchers assume that the screening processes which separate the defective items are error-free, it would be realistic to consider misclassification errors in this process. Beside inspection errors, learning is another human factor that brings in enhancement in the overall performance of a supply chain. Learning is inherent when there are workers involved in a repetitive type of production process. Learning and forgetting are even more important in manufacturing environments that emphasize on flexibility where workers are cross-trained to do different tasks and where products have a short life cycle. Inventory management with learning in quality, inspection and processing time will be the focus of this thesis. A number of models will be developed for a buyer and/or a two level supply chain to incorporate these human factors. The key findings of this work may be summarized as 1. Inspection errors significantly affect the annual profit. 2. An increase in the unit screening cost reduces the annual profit to a great extent at slower rates of learning. 3. For the two-level supply chain we investigated, learning in production drops the annual cost significantly while the learning in supplier's quality results in a situation where there are no defectives from the suppliers. 4. Type II error may seem to be beneficial for a two level supply chain as the order/lot size goes down and thus affects the costs of ordering, production and screening. 5. Consignment stocking policy performs better than conventional stocking when holding costs go higher than a threshold value.

An optimal inventory model with interaction of lot size, production rate and lead-time in a fuzzy back-order system

2019 ◽  
Vol 53 (2) ◽  
pp. 517-538
Author(s):  
S. Priyan ◽  
P. Mala ◽  
S. Tiwari
Keyword(s):  
Decision Making ◽  
Production Rate ◽  
Lead Time ◽  
Trade Credit ◽  
Strategic Decision ◽  
Sensitivity Analyses ◽  
Order System ◽  
Lot Size ◽  
Order Rate ◽  
Back Order

This paper examines the decision-making about the interaction of lot size, production rate and lead time between a vendor and a buyer with the consideration of trade credit and fuzzy back-order rate. We assume that the lead time demand is distribution free and the back-order rate is triangular fuzzy number. An economic model is design to determine the optimal lot-size, production rate and lead time while minimizing system total cost. A minimax approach is applied to tackle the model and designed an iterative algorithm to obtain the optimal strategy. Numerical example and sensitivity analyses are given to demonstrate the performance of the proposed methodology and to highlight the differences between crisp and the fuzzy cases. This paper provides optimal decision support tools for managers in the form of mathematical model that improve operational, tactical, and strategic decision making in the fuzzy system. This paper aims to raise the awareness of managers with regard to realistic inventory problems.

scholarly journals MATERIAL REQUIREMENT PLANNING UNTUK MEMENUHI PRODUKSI PADA CV. BANGUN CIPTA ARTHA DI BADUNG

2020 ◽  
Vol 9 (2) ◽  
pp. 426
Author(s):  
I Made Sugita Yasa ◽  
Kastawan Mandala
Keyword(s):  
Inventory Management ◽  
Lot Sizing ◽  
Raw Materials ◽  
Raw Material ◽  
Exact Order ◽  
Lot Size ◽  
Inventory Costs ◽  
Material Requirement Planning ◽  
Material Requirement ◽  
Total Inventory

Inventory management without Material Requirement systems in CV. Bangun Cipta Artha resulted in the lot size for each order of raw materials not optimal. One concept that can be used to plan and control raw materials is the Material Requirement Planning. This study is to determine the number of sizes of raw material orders, the exact order time, the method that produces the lowest cost for each raw material, and the effect of using MRP on inventory costs. This research conducted on 160x200cm spring bed products. Data was analyzed by making production master schedules, determining net requirements, determining lot size, and making MRP tables. Based on the results, the determination the best lot sizing is the order quantiy period which results in a total inventory cost of Rp. 26,475,220 where the total cost is lower, compared to lot for lot method which Rp. 43,464,000. part period balancing Rp. 33,106,576, and conventional method Rp.49,472,912. Keywords: Material Requirement Planning (MRP), Sizing Lot, Lot For Lot, Balancing Part Period, Period Order Quantiy

scholarly journals Optimal Pricing and Ordering Policy for Two Echelon Varying Production Inventory System

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Brojeswar Pal ◽  
Shib Sankar Sana ◽  
Kripasindhu Chaudhuri
Keyword(s):  
Supply Chain ◽  
Raw Materials ◽  
Chain Model ◽  
Selling Price ◽  
Lot Size ◽  
Ordering Policy ◽  
Supply Chain Model ◽  
Production Inventory ◽  
Test Feasibility ◽  
Optimal Ordering

The paper proposes a two-stage supply chain model for price sensitive demand in imperfect production system while manufacturer and supplier are the members of the chain. The supplier screens the raw materials first and supplies good materials to the manufacturer at a constant rate. The production rate varies randomly within a finite interval. The inventory cycle of the manufacturer starts with shortages and production and it finishes with shortages again, in which shortages are partially backlogged. We consider a mixture of LIFO (last in, first out) and FIFO (first in, first out) dispatching policies to fill the backlogged demand. Thus, the objective of the proposed paper is to determine the optimal ordering lot-size and selling price of the manufacturer such that the per unit average integrated expected profit of the supply chain model is maximized. A numerical example is provided to analyze and illustrate the behavior and application of the model. Finally, sensitivity analysis of the key parameters are presented to test feasibility of the model.

scholarly journals A Simulation Study Of Economic Production Quantity Lot Size To Kanban For A Single Line Production System Under Various Setup Times With Completion Time As Performance Metric

2011 ◽  
Vol 7 (10) ◽  
Author(s):  
Terrence J. Moran ◽  
Marvin Troutt
Keyword(s):  
Completion Time ◽  
Setup Time ◽  
Performance Measure ◽  
Total Completion Time ◽  
Lot Size ◽  
Line Production ◽  
Economic Production Quantity ◽  
Performance Metric ◽  
Economic Production ◽  
Production Quantity

<h1 style="page-break-after: auto; text-align: justify; line-height: normal; margin: 0in 0.5in 0pt; mso-pagination: none;"><span style="color: black; font-size: 10pt; font-weight: normal;"><span style="font-family: Times New Roman;">The Setup time variable was evaluated for the two systems (Kanban and EPQ) against the performance measure of total completion time.<span style="mso-spacerun: yes;">&nbsp; </span>EPQ outperforms Kanban on total completion time.<span style="mso-spacerun: yes;">&nbsp; </span>The research helped clarify for practitioners whether EPQ might be more suitable than Kanban for their given situations.<span style="mso-spacerun: yes;">&nbsp; </span></span></span></h1>

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