INDEPENDENT DEMAND SYSTEMS: PROBABILISTIC MODELS Introduction
|
|
- Gyles Watson
- 6 years ago
- Views:
Transcription
1 INDEPENDENT DEAND SYSTES: PROAILISTIC ODELS Introduction Demand and Lead-Time are treated as random variable The models in this section assumes that the average demand remains approximately constant with time Probability distribution o demand is available The inventory can be divided into working stock and saety stock see ig. 1 S UANTITY S Working Stock Working Stock Saety Stock TIE Fig. 1 Working stock and saety stock in a -system o inventory control Working stock is the quantity expected to be used during a given time period The average working stock is one-hal the order quantity lot size, which may be determined by the EO ormula or some variant o it. Saety stock is the quantity used to protect against higher than expected demand levels It is the extra inventory kept on hand as a cushion against stockouts due to random perturbations o nature or the environment It has two eects on a irm s cost: decreases the cost o stockouts or increases holding costs Organisation can use countermeasures to prevent, avoid or mitigate stockouts. Typical countermeasures are expediting, emergency shipping, special handling, rescheduling, overtime, and substitution The prevention cost can be considered as stockout costs even though the stockout does not occur Customer s reaction to a stockout condition can result in a back order or a lost sales 1 Department o echanical Engineering
2 Saety stock determination is depends on the type o inventory control system used FIXED ORDER UANTITY SYSTE OR -SYSTE Inventory variation in an ideal and realistic situation is given in igs and 3 respectively S UANTITY S Lead Time Reorder Point Saety Stock Order Lot Order Lot Placed Received Placed Received TIE Fig. Ideal inventory model S UANTITY S Lead Time Stockout Lead Time Lead Time TIE Fig. 3 Realistic inventory model Department o echanical Engineering
3 Saety stock is needed to protect against a stockout ater the reorder point is reached and prior to receipt o an order This period is usually called lead-time The reorder point is composed o the mean lead-time demand plus saety stock Average inventory level on hand just beore the receipt o a replenishment order is the saety stock. Over many cycles, the inventory level will sometimes be more than the saety stock and sometimes less, but it should average to the saety stock Larger the order quantity, ewer the annual orders, which means the ewer opportunities or stockout to occur Saety stocks are dependent on stockout cost or service level, holding cost, demand variation and lead-time variation Working stock quantity is determined beore considering saety stock In the order quantity ormulations, it is assumed that the order quantity can be determined by an economic balance o the relevant cost, and that it is independent o the reorder point Average inventory Expected level o inventory beore receiving the order is saety stock Expected level o inventory immediately ater receiving the order is Saety stock Average inventory is the average o these two values is There are two approaches or saety stock calculation: 1 Known stockout cost explicit costs can be allocated to shortages Saety stock Unknown stockout costs management speciies a service level based on some probability distribution o demand during the lead-time Statistical considerations Notations lead-time demand in units a random variable - mean lead-time demand - standard deviation o lead-time demand probability density unction o lead time demand P probability o a lead-time demand o units max - maximum lead-time demand reorder points in units P> probability o stockout 3 Department o echanical Engineering
4 Department o echanical Engineering 4 E> expected stockout in units during lead-time Continuous Distribution 0 d 0 d > d P > d E Discrete Distribution max 0 P max 0 m P > max 1 P P > max 1 P E Lead-time demand distribution is required or inventory analysis in -system Frequently the demand distribution is expressed on a time basis that is dierent rom the lead-time Convolution technique is used to get the demand distribution or varying length o time Standard Distributions Normal Distribution Parameters are mean and standard deviation π e or < < - > d E
5 Z 1 F Z [1] S Z [] Equations [1] and [] uses standard normal variate values Another way o estimating E > is given below Standard Normal Distribution ean 0, standard deviation 1 Z Z e Z π Transormations or normal distribution to standard normal distribution Let a value o normal random variable be and the corresponding value or standard normal random variable Z be t. t t E Z > t E t E > For standard normal random variate, the values or the ollowing are tabulated Z, FZ, Z, and EZ Note: Poisson Distribution Single parameter P > d P Z > t Z dz Normal random variate representing lead time demand average lead time demand in units S saety stock in units Z standard normal variate - standard deviation o lead time demand Poisson distribution is not symmetrical with respect to mean when mean is small - Skewed to the right t 5 Department o echanical Engineering
6 Normal approximation to Poisson is usually adequate when mean is 1 or greater Negative Exponential Distribution Single parameter SAFETY STOCK ESTIATION: KNOWN STOCKOUT COST AND LEAD TIE Stockout cost is expressed as backorder cost per unit, backorder cost per outage, lost sales cost per unit or lost sales per outage ackorder cost per outage is a ixed amount and can occur at most once during a replenishment cycle Assumed a variable demand and constant lead-time ackorder Case: Stockout Cost per unit No lost sales Notations reorder points in units S saety stock in units H holding cost per unit o inventory per year A backordering cost per unit R average annual demand in units lot size or order quantity in units lead-time demand in units a random variable average lead time demand in units probability density unction o lead time demand TC s expected annual saety stock cost s 0 d d 0 0 d E > d Expected annual saety stock cost holding cost stockout cost AR TC s SH d 6 Department o echanical Engineering
7 AR H d dtc s To get optimum value o, 0 d LEINIZ S RULE G x k x h x g x, y dy k x dg x dx h x g x, y dk x dh x dy g x, k x g x, h x x dx dx d b d d d P > dtc s d P > RA H H AR [ P > ] 0 This ormula can be applied to both discrete and continuous probability distribution o lead-time demand When discrete distributions are used, the exact optimum stockout probability is usually unattainable When the optimum stockout probability cannot be attained, the next lower attainable stockout probability is selected Problem What is the optimal reorder point or the inventory problem speciied below? The lead time demand distribution is given in table below. 7 Department o echanical Engineering
8 R 1800 units per year C Rs 300 per order F 15 % P Rs 0 per unit A Rs 10 per unit backordered Also, calculate the expected stockout quantity, saety stock and expected annual saety stock cost. Determine the standard deviation and mean o the distribution. Consider this mean and standard deviation as the parameters o normal distribution and determine reorder level and saety stock. ackorder Case: Stockout Cost Per Outage G backorder cost per outage R TC s SH G d For Normal distribution For discrete distribution GR P H dtc s 0 d Z H GR > When the optimum reorder point lies between two integer values select the integer with the lower Lost sales case: Stockout cost per Unit All stockouts are lost and not recovered Saety stock is zero whenever Average number o annual cycles is R E > Expected stockout quantity per cycle is E> R Lead time demand Probability Department o echanical Engineering
9 Department o echanical Engineering 9 d S 0 0 d d d > d E s d AR SH TC d H AR H H AR H P d dtc s > 0 Lost Sales Case: Stockout Cost Per Outage s d GR SH TC d GR d F H H GR H F P d dtc s > 1 0 SAFETY STOCK ESTIATION: KNOWN STOCKOUT COST AND VARIALE LEAD TIE Constant Demand and Variable Lead Time Solution technique similar to variable demand, constant lead time case Demand distribution during lead time is obtained by multiplying the constant demand by probability distribution or the lead time I lead time ollows normal L ZD DL Z Where D constant demand rate per period standard deviation o demand during lead time
10 L standard deviation o lead time L average lead time in periods Variable Demand and Variable Lead Time Joint probability distribution JPD is required Range o the JPD is rom the level indicated by the product o the smallest demand and shortest lead time to level indicated by the product o largest demand and largest lead time JPD is used to analyse the appropriate stockout cost situation as previously discussed When demand and lead time distributions are independent DL L D D L Where L average lead time length in periods D average demand per period D standard deviation o demand distribution L standard deviation o lead time distribution standard deviation o demand during lead time When demand and lead time distributions are not independent DL L D D L With variable demand and variable lead time, the solution procedures are the same as those previously discussed SAFETY STOCK ESTIATION: SERVICE LEVELS When stockout costs are not known or eels very uneasy about estimating them, it is customary or management to set service levels or which reorder points can be ascertained. A service level indicates the ability to meet customer demands rom stock, or in some other timely manner For make-to-stock or order-to-stock environment, service implies illing demand rom inventory and better service involves excess inventory investment For make-to-order or assemble-to-order, service implies providing in time and better service involves excess capacity As the service level approaches 100 %, the investment in saety stock oten increases drastically The principle o diminishing return applicable here D L 10 Department o echanical Engineering
11 While perect customer service might not be attainable, lead time reduction and just-in-time approaches can substantially improve the customer service A production line where ailure to provide needed parts can bring the line to halt and in such environment RP system is suitable than a ixed order size system The service level takes on dierent meaning, depending upon how it is stated as a decision criterion Service per order cycle Service per units demanded Saety stocks under dierent service concepts will be dierent Service per Order Cycle It indicates the probability o not running out o stock during the replenishment lead time It is not concerned with how large the shortage is, but with how oten it can occur Service level per order cycle SLc is deined as the raction o replenishment cycles without depletion o stock number o cycles with a stockout SL c 1- total number o order cycles 1- P > P> is the probability o a stockout during the lead time or the stockout probability per order cycle. It represents the probability o at least one stockout during the lead time or the raction o lead time periods during which the demand will exceed the reorder point Service per order cycle does not indicate how requently stockout will occur over a given time period or all products This is because the order cycle will vary rom product to product ore requently stock is replenished, the greater the number o expected stockout cycles This indicates that service per order cycle does not allow or uniorm treatment o dierent products It also does not indicates the percentage o demand that will be satisied Service per Units Demanded It does indicate the percentage o demand that will be satisied and allow uniorm treatment o dierent products 11 Department o echanical Engineering
12 number o units stockouts SL u 1 - total number o units demanded E > 1 - or backorder E > 1 - E > or Imputed Stockout Costs or the given Service Level Service level really does impute a stockout cost lost sales For a given service level the imputed stockout cost can be calculated rom previously developed optimum ormulations or the probability o a stockout Imputed stockout cost is a convenient way to determine i the value chosen or the service level is appropriate FIXED ORDER INTERVAL SYSTE: PROAILISTIC ODELING Inventory position is monitored at discrete point in time Once an order is placed at time t, another order can not be placed until t T, and the second order will not be illed until the lead time period has elapsed, at t T L Thus saety stock protection is needed or the lead-time L plus the order interval T In the ixed order size system, saety stock is needed only or the lead-time period, because the inventory position is monitored with each transaction In the ixed order size system, a higher than normal demand causes a shorter time between orders whereas in the ixed order interval system, the result would be a larger order size Predetermined inventory level E E S RT LR Where S is the saety stock considering demand variation during T L period RT LR is the average demand during T L period 1 Department o echanical Engineering
13 Realistic inventory model or P system Predetermined inventory level E UANTITY Inventory level o expected demand during lead-time and saety stock Saety stock level L L L T T TIE Stockout Fig: P-System: Inventory variation with time Saety stock is determined based on the inventory variation during lead time and review period. That is, or saety stock calculation, the demand variation during the TL period is considered. The order interval T T C HR 0 EOI in years Determination o Saety Stock when stockout cost known Approach is same that o -system is a random variable shows the demand distribution or T L period 13 Department o echanical Engineering
14 Table. Formula or determining saety stock Stockout case Stockout cost per unit Stockout cost per outage ackorder P > E HT A E HT G Lost sale HT P E > E A HT 1 P > E G HT Determination o Saety Stock when service level known Service level per order interval SL c number o order intervals with a stockout SL c 1- total number o order intervals 1- P > E Service levels per units demanded SL u A worked out Problem number o units stockouts SL u 1 - total number o units demanded E > E 1 - TR Daily demand or hamburger buns at a city centre production plant is as ollows: Daily Demand Probability The plant can produce several other products using the same acility. The lie o the product is 5 days. Consider 5 days as planning horizon and -system o inventory control, determine the batch size. The ollowing data is also available or the above problem. Carrying cost Rs 1 per unit day Setup cost Rs 450 per setup What should be the reorder point with a lead-time o 1 day and the service level per order cycle o 85 %? What is the saety stock? What is the stockout cost per unit imputed by this service level? Assume that the daily demand is normally distributed with parameters that can be determined rom the daily demand distribution given above. Determine the reorder point and saety stock i the above service level is applicable. 14 Department o echanical Engineering
15 I P-system o inventory control is used, what are the review period, predetermined inventory level and saety stock? Compare the total cost o expected saety stock o both the system o inventory controls. 1. Given data: Daily demand distribution Daily demand Probability Total 1.0 Service level per order cycle SL c 85 %. Lead time 1 day Planning horizon 5 days Carrying cost H Rs. 1 per unit per day. Set up cost C Rs. 450 per set up. atch size RC H Where, R average demand in the planning horizon average demand per day Number o days in a planning horizon Average demand per day Units. Now R Units Reorder Point: SL c 1- P> P > P > Units. 15 Department o echanical Engineering
16 Daily demand Probability Cumulative probability Stock out probability P > 1- cumulative probability From the above table, Reorder point, or 0.15 stockout probability is 550 units. Reorder point S S Thereore, Saety stock, S Units. Stock out cost per unit A imputed by given service level : H P > AR A 450 A 9.0 Rupees per unit. I the daily demand is normally distributed with parameters that can be determined rom the daily demand distribution given above: ean, 490 Units, Variance, n i i 1 P Standard normal deviate, Z From statistical tables, Z or P> 0.15, is Units Department o echanical Engineering
17 Reorder point S S Thereore, Saety stock, S Units. I P system o inventory control is used: Economic order interval, T o days 1.35 days 1 day approximately C RH horizon o the planning Service level per order cycle SL c 85 %. SL c 1- P>E P >E P >E 0.15 For inding, maximum inventory level E, we need demand distribution or T o Lead time period. T o Lead time 11 days. Preparation o demand distribution or days First day Second day Demand Probability Demand Probability Total demand probability Department o echanical Engineering
18 Demand distribution or days Demand Calculation o probability Probability Cumulative probability Total 1.0 Stock out probability P >E 1- cumulative probability From the above table, aximum inventory level, E or 0.15 stock out probability is 1050 Units. aximum inventory level E S Units. Thereore, Saety stock, S E Units. 18 Department o echanical Engineering
19 Comparison o total cost o maintaining expected saety stock in and P systems, i stock out cost is Rs. 6 per unit: System: max R TC SH A P s P-System: s Rupees. 1 max T o E 1 TC SH A E P Rupees. 19 Department o echanical Engineering
Johan Oscar Ong, ST, MT
INVENTORY CONTROL Johan Oscar Ong, ST, MT I.1 DEFINITION Inventory are material held in an idle or incomplete state awaiting future sale, use, or transformation. (Tersine) Inventory are a stock of goods.
More informationECONOMIC PRODUCTION QUANTITY MODEL WITH CONTINUOUS QUALITY INSPECTION
CONOIC PRODUCTION UANTITY ODL WITH CONTINUOU UALITY INPCTION Jia-Chi TOU and Jen-ing CHN Abstract. In this paper, we consider a continuous product quality inspection process in a production line. Three
More informationQuestion 2: How do we make decisions about inventory?
uestion : How do we make decisions about inventory? Most businesses keep a stock of goods on hand, called inventory, which they intend to sell or use to produce other goods. Companies with a predictable
More informationReorder Quantities for (Q, R) Inventory Models
International Mathematical Forum, Vol. 1, 017, no. 11, 505-514 HIKARI Ltd, www.m-hikari.com https://doi.org/10.1988/imf.017.61014 Reorder uantities for (, R) Inventory Models Ibrahim Isa Adamu Department
More informationInventory Control Models
Chapter 6 Inventory Control Models uantitative Analysis for Management, Tenth Edition, by Render, Stair, and Hanna 2008 Prentice-Hall, Inc. Introduction Inventory is any stored resource used to satisfy
More informationPlanned Shortages with Back-Orders
Planned Shortages with Back-Orders Shortage: when customer demand cannot be met Planned shortages could be beneficial Cost of keeping item is more expensive than the profit from selling it Ex: car 1 Customer
More informationA Dynamic Rationing Policy for Continuous-Review Inventory Systems
A Dynamic Rationing Policy for Continuous-Review Inventory Systems Mehmet Murat Fadıloğlu, Önder Bulut Department of Industrial Engineering, Bilkent University, Ankara, Turkey E-mail: mmurat@bilkent.edu.tr
More informationInventory Management Reasons for maintaining Inventory Types of Inventory Calculation of Inventory stock consumption value
Inventory Management Reasons for maintaining Inventory To counter uncentainity in supply and demand patterns to counter the uncertainities in lead time purchases made in bulk can provide discounts to meet
More informationNotes for Production and Operations Management- I
Notes for Production and Operations Management- I Factors affecting Process Design Decisions Nature of product/service demand Degree of vertical integration Production Flexibility Degree of automation
More informationVII. THE DUCTILE/BRITTLE TRANSITION, GAGING DUCTILITY LEVELS
VII. THE DUCTILE/BRITTLE TRANSITION, GAGING DUCTILITY LEVELS In setting out to theoretically characterize and dierentiate ductile behavior rom brittle behavior it is necessary to irst have a credible theory
More informationInventory Control Model
Inventory Control Model The word 'inventory' means simply a stock of idle resources of any kind having an economic value. In other words, inventory means a physical stock of goods, which is kept in hand
More informationPrinciples of Inventory Management
John A. Muckstadt Amar Sapra Principles of Inventory Management When You Are Down to Four, Order More fya Springer Inventories Are Everywhere 1 1.1 The Roles of Inventory 2 1.2 Fundamental Questions 5
More informationSven Axsäter. Inventory Control. Third Edition. Springer
Sven Axsäter Inventory Control Third Edition Springer Contents 1 Introduction 1 1.1 Importance and Objectives of Inventory Control 1 1.2 Overview and Purpose of the Book 2 1.3 Framework 4 2 Forecasting
More informationDetermining order-up-to level considering backorder cost per unit short on the base-stock inventory system under intermittent demand
Determining order-up-to level considering backorder cost per unit short on the base-stock inventory system under intermittent demand Keisuke agasawa Faculty of Engineering, Hiroshima University 1-4-1,
More informationThree-echelon Inventory Model with Defective Product and Rework Considerations under Credit Period
Proceedings of the International MultiConference of Engineers and Computer Scientists 015 Vol II, IMECS 015, March 18-0, 015, Hong Kong Three-echelon Inventory Model with Defective Product and Rework Considerations
More informationA PERIODIC REVIEW INVENTORY MODEL WITH RAMP TYPE DEMAND AND PRICE DISCOUNT ON BACKORDERS Sujan Chandra 1
AMO - Advanced Modeling and Optimization, Volume 18, Number 1, 16 A PERIODIC REVIEW INVENTORY MODEL WITH RAMP TYPE DEMAND AND PRICE DISCOUNT ON BACKORDERS Sujan Chandra 1 Department of Statistics, Haldia
More informationChapter 4. Models for Known Demand
Chapter 4 Models for Known Demand Introduction EOQ analysis is based on a number of assumptions. In the next two chapters we describe some models where these assumptions are removed. This chapter keeps
More informationLecture 10: Managing Uncertainty in the Supply Chain (Safety Inventory) INSE 6300/4-UU. Overview
ecture 10: Managing Uncertainty in the Supply Chain (Safety Inventory) INSE 6300/4-UU Quality Assurance In Supply Chain Management Quality Assurance in Supply Chain Management (INSE 6300/4-UU) Winter 011
More informationSLIDES BY. John Loucks. St. Edward s Univ.
. SLIDES BY John Loucks St. Edward s Univ. 1 Chapter 14, Part A Inventory Models with Deterministic Demand Economic Order Quantity (EOQ) Model Economic Production Lot Size Model Inventory Model with Planned
More informationPERFORMANCE-BASED SEISMIC DESIGN OF REINFORCED CONCRETE BRIDGE COLUMNS
PERFORMANCE-BASED SEISMIC DESIGN OF REINFORCED CONCRETE BRIDGE COLUMNS Dawn E LEHMAN 1 And Jack P MOEHLE 2 SUMMARY There is a new ocus in seismic design on the perormance o reinorced concrete bridges.
More information6.5. Traits and Probability. Punnett squares illustrate genetic crosses.
6.5 Traits and Probability VOCABULARY Punnett square monohybrid cross testcross dihybrid cross law o independent assortment probability Key Concept The inheritance o traits ollows the rules o probability.
More informationThe Training Material on Logistics Planning and Analysis has been produced under Project Sustainable Human Resource Development in Logistic Services
The Training Material on Logistics Planning and Analysis has been produced under Project Sustainable Human Resource Development in Logistic Services for ASEAN Member States with the support from Japan-ASEAN
More informationInventory Control Models
Chapter 6 Inventory Control Models To accompany uantitative Analysis for Management, Eleventh Edition, by Render, Stair, and Hanna Power Point slides created by Brian Peterson Introduction Inventory is
More informationInventory Management
Inventory Management Materials planning AGGREGATE PLANNING Master Production Schedule (MPS) MATERIALS PLANNING The planning of materials requirements consists of the determination of: What How much and
More informationOperations Research Models and Methods Paul A. Jensen and Jonathan F. Bard. Inventory Level. Figure 4. The inventory pattern eliminating uncertainty.
Operations Research Models and Methods Paul A. Jensen and Jonathan F. Bard Inventory Theory.S2 The Deterministic Model An abstraction to the chaotic behavior of Fig. 2 is to assume that items are withdrawn
More informationVolume 5, Issue 8, August 2017 International Journal of Advance Research in Computer Science and Management Studies
ISSN: 31-778 (Online) e-isjn: A437-3114 Impact Factor: 6.047 Volume 5, Issue 8, August 017 International Journal of Advance Research in Computer Science and Management Studies Research Article / Survey
More informationCore Drying Simulation and Validation
Paper 11-028.pd, Page 1 o 5 Core Drying Simulation and Validation A. Starobin and C.W. Hirt Flow Science, Inc., Santa Fe, NM H. Lang BMW Group, Landshut, Germany M. Todte CFD Consultants GmbH, Rottenburg,
More informationInventory systems for independent demand
Inventory systems for independent demand Roberto Cigolini roberto.cigolini@polimi.it Department of Management, Economics and Industrial Engineering Politecnico di Milano 1 Inventory systems for independent
More informationThe integration of online and offline channels
The integration of online and offline channels Zihan Zhou, Qinan Wang Nanyang Business School, Nanyang Technological University, Nanyang Avenue, Singapore 69798 zzhou6@e.ntu.edu.sg Abstract We study a
More informationReport for Handbook on large plastic pipes, published 2015, ISBN
Relining with large diameter Polyethylene Pipes Damaged and leaky pipes made out o concrete, GRP, steel, ductile iron can be relined and renovated with Proiled Polyethylene Pipes in almost every diameter.
More informationInventory Replenishment
Inventory Replenishment SIMMS Inventory Management Software 7.3 January 30, 2011 Contents Inventory Replenishment................ 1 Requirements to Use the Replenishment Manager........ 1 Assign a Default
More informationLOT SIZING IN MRP. Week Gross requirements Scheduled receipts Projected available balance Net Requirements
LOT SIZING IN MRP The net data is subjected lot sizing Lot sizes developed can satisfy the net for one or more weeks The basic trade-off involves the elimination of one or more setups at the expense of
More informationCover-Time Planning, An alternative to Material Requirements Planning; with customer order production abilities and restricted Work-In-Process *
International Conference on Industrial Engineering and Systems Management IESM 2007 May 30 - June 2, 2007 BEIJING - CHINA Cover-Time Planning, An alternative to Material Requirements Planning; with customer
More informationA numerical study of expressions for fill rate for single stage inventory system with periodic review.
University of Louisville ThinkIR: The University of Louisville's Institutional Repository Electronic Theses and Dissertations 8-2013 A numerical study of expressions for fill rate for single stage inventory
More informationFUZZY INVENTORY MODEL FOR TIME DEPENDENT DETERIORATING ITEMS WITH LEAD TIME STOCK DEPENDENT DEMAND RATE AND SHORTAGES
Available online at http://www.journalijdr.com ISSN: 2230-9926 International Journal of Development Research Vol. 07, Issue, 10, pp.15988-15995, October, 2017 ORIGINAL RESEARCH ARTICLE ORIGINAL RESEARCH
More informationOPTIMIZATION AND OPERATIONS RESEARCH Vol. IV - Inventory Models - Waldmann K.-H
INVENTORY MODELS Waldmann K.-H. Universität Karlsruhe, Germany Keywords: inventory control, periodic review, continuous review, economic order quantity, (s, S) policy, multi-level inventory systems, Markov
More informationWe consider a distribution problem in which a set of products has to be shipped from
in an Inventory Routing Problem Luca Bertazzi Giuseppe Paletta M. Grazia Speranza Dip. di Metodi Quantitativi, Università di Brescia, Italy Dip. di Economia Politica, Università della Calabria, Italy Dip.
More informationRADON MIGRATION MODEL FOR COVERING U MINE AND ORE PROCESSING TAILINGS *
RADON MIGRATION MODEL FOR COVERING U MINE AND ORE PROCESSING TAILINGS * A. VÁRHEGYI, J. SOMLAI 2, Z. SAS 2 MECSEK-ÖKO Environment Protection Co. H-7633 Pécs, Estergár L. str. 9. Hungary, E-mail: varhegyiandras@mecsekoko.hu
More informationRRS Education Session #1
RRS Education Session #1 Patricio Rocha Garrido Sr. Engineer Resource Adequacy Planning 11/24/2015 IRM/FPR Basics - Rationale IRM/FPR are computed for future delivery years. And the future is uncertain
More informationHistory IE 376 PRODUCTION INFORMATION SYSTEMS. Course Notes Lecture #2.A Material Requirements Planning
IE PRODUCTION INFORMATION SYSTEMS Course Notes Lecture #.A Material Requirements Planning IE Bilkent University Manufacturing Planning and Control System Routing file Resource planning Bills of material
More informationProcedia - Social and Behavioral Sciences 189 ( 2015 ) XVIII Annual International Conference of the Society of Operations Management (SOM-14)
Available online at www.sciencedirect.com ScienceDirect Procedia - Social and ehavioral Sciences 189 ( 2015 ) 184 192 XVIII Annual International Conference of the Society of Operations Management (SOM-14)
More informationStrain limit dependence on stress triaxiality for pressure vessel steel
Journal o Physics: Conerence Series Strain limit dependence on stress triaxiality or pressure vessel steel To cite this article: Y-C Deng et al 9 J. Phys.: Con. Ser. 181 171 View the article online or
More informationEuropean Journal of Operational Research
European Journal of Operational Research 202 (2010) 675 685 Contents lists available at ScienceDirect European Journal of Operational Research journal homepage: www.elsevier.com/locate/ejor Production,
More informationThe Probability Model of Expectation Disconfirmation Process
Ep ert Jou rn al o Mark eting Vo lu m e 3 Is sue pp. -6 5 5 Th e Au thor. Publish ed b Sp rint In v esti. ISS N 3 4 4-6773 h ttp://mark eting.ep ertjou rn als.c om The Probabilit Model o Epectation Disconirmation
More informationSupply Chain Inventory Management. Multi-period Problems. Read: Chap Chap 11.
Supply Chain Inventory Management Multi-period Problems Read: Chap 10.1-10.2. Chap 11. Push vs. Pull Processes PUSH: Order decision initiated in anticipation to customer orders A newspaper vendor orders
More informationMRP Configuration - Adobe Interactive Forms (Japanese) - SCN Wiki
Page 1 of 19 Getting Started Newsletters Store Welcome, Guest Login Register Search the Community Products Services & Support About SCN Downloads Industries Training & Education Partnership Developer Center
More informationHEAT TRANSFER ANALYSIS OF GEOTHERMAL HEAT EXCHANGERS IN GROUND-COUPLED HEAT PUMP SYSTEMS. Abstract. 1. Introduction
HEX-01 HEAT TRANSFER ANALYSIS OF GEOTHERMAL HEAT EXCHANGERS IN GROUND-COUPLED HEAT PUMP SYSTEMS Z Fang, N Diao, M Yu, P Cui The Ground Source Heat Pump Research Center Shandong Institute o Architecture
More informationA Simple and Robust Batch-Ordering Inventory Policy Under Incomplete Demand Knowledge
A Simple and Robust Batch-Ordering Inventory Policy Under Incomplete Demand Knowledge Liwei Bai Christos Alexopoulos Mark E. Ferguson Kwok-Leung Tsui Georgia Institute of Technology December 2005 Abstract
More informationChapter 12 Stormwater Sand Filters
TABLE OF CONTENTS 1.1 Overview o Practice... 1 1. Site Constraints and Siting o the Filter... 5 1..1 Minimum Drainage Area... 5 1.. Maximum Drainage Area... 5 1.. Elevation o Site Inrastructure... 5 1..4
More informationImprovement of Company ABC s Inventory to determine the best safety stock to keep. Kholwa Inneth Ngoma
Improvement of Company ABC s Inventory to determine the best safety stock to keep by Kholwa Inneth Ngoma 04420780 Submitted in partial fulfilment of the requirements for the degree of BACHELORS OF INDUSTRIAL
More informationAn Integrated Single Vendor-Buyer Stochastic Inventory Model with Partial Backordering under Imperfect Production and Carbon Emissions
International Journal on Recent and Innovation Trends in Computing and Communication ISSN: 31-8169 An Integrated Single Vendor-Buyer Stochastic Inventory Model with Partial Backordering under Imperfect
More informationAn EOQ Model for A Deteriorating Item With Time Dependent Exponentially Declining Demand Under Permissible Delay In Payment
IOSR Journal of Mathematics (IOSRJM) ISSN: 2278-5728 Volume 2, Issue 2 (July-Aug 22), PP 3-37 An EOQ Model for A Deteriorating Item With ime Dependent Exponentially Declining Demand Under Permissible Delay
More informationStrength Evaluation of Reinforced Concrete Beam-Column Joints
Strength Evaluation o Reinorced Concrete Beam-Column Joints H. Y. Choi & J. Y. Lee Dept. o Architectural Engineering, Sungkyunkwan University, Korea SUMMARY Joint research by three countries (America,
More informationProceedings of the 2010 Winter Simulation Conference B. Johansson, S. Jain, J. Montoya-Torres, J. Hugan, and E. Yücesan, eds.
Proceedings of the Winter Simulation Conference B. Johansson, S. Jain, J. Montoya-Torres, J. Hugan, and E. Yücesan, eds. DYNAMIC ADJUSTMENT OF REPLENISHMENT PARAMETERS USING OPTIMUM- SEEKING SIMULATION
More informationOffshore Oilfield Development Planning under Uncertainty and Fiscal Considerations
Carnegie Mellon University Research Showcase @ CMU Department o Chemical Engineering Carnegie Institute o Technology 2011 Oshore Oilield Development Planning under Uncertainty and Fiscal Considerations
More informationBasestock Model. Chapter 13
Basestock Model Chapter 13 These slides are based in part on slides that come with Cachon & Terwiesch book Matching Supply with Demand http://cachon-terwiesch.net/3e/. If you want to use these in your
More information! " # $ % % & $'( # ) * 1
! " # $% %&$'( # ) * 1 +,+ #+ ' Percent of annual dollar usage A Items 80 70 60 50 40 30 0 10 0 B Items &( &( + C Items 10 0 30 40 50 60 70 Percent of inventory items Figure 1. ' - %. &( + / / 0 + 1 /
More informationA Genetic Algorithm on Inventory Routing Problem
A Genetic Algorithm on Inventory Routing Problem Artvin Çoruh University e-mail: nevin.aydin@gmail.com Volume 3 No 3 (2014) ISSN 2158-8708 (online) DOI 10.5195/emaj.2014.31 http://emaj.pitt.edu Abstract
More informationInventory Theory. Inventory Theory
Inventory Theory Contents 1 Definitions 2 Economic Order uantity Model 3 EO Example 4 Economic Order uantity Model with hortages 5 EO with hortages Example 6 ingle Period tochastic Inventories 7 Newsboy
More informationAn Inventory Replenishment Model under Purchase Dependency in Retail Sale
An Inventory Replenishment Model under Purchase Dependency in Retail Sale Pradip Kumar Bala Discipline of Management Studies IIT, Roorkee, India ABSTRACT In multi-item inventory with very large number
More informationOptimal Economic Manufacturing Quantity and Process Target for Imperfect Systems
Proceedings of the 2014 International Conference on Industrial Engineering and Operations Management Bali, Indonesia, January 7 9, 2014 Optimal Economic Manufacturing Quantity and Process Target for Imperfect
More informationIbrahim Sameer (MBA - Specialized in Finance, B.Com Specialized in Accounting & Marketing)
Ibrahim Sameer (MBA - Specialized in Finance, B.Com Specialized in Accounting & Marketing) Introduction What is inventory control? Inventory control includes the function of inventory ordering and purchasing,
More informationOptimizing the Safety Stock Inventory Cost Under Target Service Level Constraints
University of Massachusetts Amherst ScholarWorks@UMass Amherst Masters Theses 1911 - February 2014 Dissertations and Theses 2012 Optimizing the Safety Stock Inventory Cost Under Target Service Level Constraints
More informationMaterials Selection for a Torsionally Stressed Cylindrical Shaft
Case Study 1 (CS1) Materials Selection or a Torsionally Stressed Cylindrical Shat Learning Objectives Ater studying this case study, you should be able to do the ollowing: 1. Briely describe how the strength
More informationInventory Management
Inventory Management Inventory Inventory is the stock of any item or resource used in an organization. Inventory include: raw materials, finished products, component parts, supplies, and work-in-process
More informationINVENTORY SYSTEM Introduction
INVENTORY SYSTEM Introduction Inventory is the stock of any item or resources used in an organization. It is idle resource, but usable. The term stock is generally used to refer to resource in its physical
More informationBeyond Pareto: 12 Standard Principles of Inventory and Forecasting. Thomas L. Freese, Principal. Freese & Associates, Inc. Freese & Associates, Inc.
Beyond Pareto: 12 Standard Thomas L. Freese, Principal Twelve Steps Inventory Carrying costs Concepts Make-to-order or stock A B C Accuracy Cycle counting Reordering Valuation Safety stocks Demand Variability
More informationContainer packing problem for stochastic inventory and optimal ordering through integer programming
20th International Congress on Modelling and Simulation, Adelaide, Australia, 1 6 December 2013 www.mssanz.org.au/modsim2013 Container packing problem for stochastic inventory and optimal ordering through
More informationSAP Material Master A Practical Guide. Matthew Johnson
SAP Material Master A Practical Guide Matthew Johnson TABLE OF CONTENTS Table of Contents Preface 7 1 Introduction 11 2 Material Master Basics 17 2.1 Material master structure 17 2.2 Creating and accessing
More informationSimulation of Lean Principles Impact in a Multi-Product Supply Chain
Simulation of Lean Principles Impact in a Multi-Product Supply Chain M. Rossini, A. Portioli Studacher Abstract The market competition is moving from the single firm to the whole supply chain because of
More informationInventory Production Control Model With Back- Order When Shortages Are Allowed
P a g e 64 Vol. 10 Issue 6 (Ver 1.0), October 2010 Inventory Production Control Model With Back- Order When Shortages Are Allowed K.A Adeleke 1,D.A Agunbiade 2 GJSFR- F Clasification FOR 010201,010205,010206,150202
More informationGBS 660 Production and Operations Management
Master of Business Administration (MBA) GBS 660 Production and Operations Management Course Lecturer Prof Levy Siaminwe, Phd Email: lsiaminwe@gmail.com 1 Production and Operations Management Module Contents
More informationNote 8: Materials Requirements Planning
Note 8: Materials Requirements Planning In materials requirements planning (MRP), orders for component parts are timed to coincide with the production schedule of the products in which they are used. We
More informationAcademy session INVENTORY MANAGEMENT
Academy session INVENTORY MANAGEMENT Part 2: Tools for Inventory Management Time: 9:00-12:00, Saturday, 20/08/2011 Location: 1/F, Sherwood Residence, 127 Pasteur, Dist 3, HCMC Speaker: Dr. Eckart Dutz,
More informationAssignment 10 (Solution) Six Sigma in Supply Chain, Taguchi Method and Robust Design
Assignment 10 (Solution) Six Sigma in Supply Chain, Taguchi Method and Robust Design Dr. Jitesh J. Thakkar Department of Industrial and Systems Engineering Indian Institute of Technology Kharagpur Instruction
More informationA Single Item Non Linear Programming (NLP) Economic Order Quantity Model with Space Constraint
A Single Item Non Linear Programming (NLP) Economic Order Quantity Model with Space Constraint M. Pattnaik Dept. of Business Administration Utkal University, Bhubaneswar, India monalisha_1977@yahoo.com
More informationSupply Chain Inventory Policy Analyser (SCIPA) Software Package -User Manual
Supply Chain Inventory Policy Analyser (SCIPA) Software Package -User Manual Department of Mechanical Engineering National Institute of Technology Calicut The document giving details about how to use the
More informationHomework 1 Fall 2000 ENM3 82.1
Homework 1 Fall 2000 ENM3 82.1 Jensen and Bard, Chap. 2. Problems: 11, 14, and 15. Use the Math Programming add-in and the LP/IP Solver for these problems. 11. Solve the chemical processing example in
More informationAn Adaptive Kanban and Production Capacity Control Mechanism
An Adaptive Kanban and Production Capacity Control Mechanism Léo Le Pallec Marand, Yo Sakata, Daisuke Hirotani, Katsumi Morikawa and Katsuhiko Takahashi * Department of System Cybernetics, Graduate School
More informationINVENTORY AND ECONOMIC ORDER QUANTITY MODELS
INVENTORY AND ECONOMIC ORDER QUANTITY MODELS Types of Demand Retailers and distributors must manage independent demand items-that is, items for which demand is influenced by market conditions and isn t
More informationShop Control Guide DBA Software Inc.
Contents 3 Table of Contents 1 Introduction 4 2 Why You Need Shop Control 5 3 Total Control Workflow 8 4 Shop Control Overview 10 5 Setup - Work Center Capacities 13 6 Setup - Job Labor Options 16 7 Setup
More informationDetermining Dryness Fraction
Determining Dryness Fraction SUPERHEATED EVAPORATION 100 C WATER LINE 0 C g Temperature/Entalpy Dryness Fraction Tis andout assumes te student is amiliar wit Steam Tables and te terms ound terein. I required,
More informationLot-Sizing in MRP. Lot Sizing in MRP IE 376 PRODUCTION INFORMATION SYSTEMS. Course Notes Lecture #3 Lot sizing in MRP Systems
PRODUCTION INFORMATION SYSTEMS Course Notes Lecture #3 Lot sizing in MRP Systems Bilkent University Lot-Sizing in MRP Lot-size is the quantity ordered/produced at one time Large lots are preferred because:
More informationINVENTORY STRATEGY INVENTORY PLANNING AND CONTROL
CHAPTER 7 INVENTORY STRATEGY INVENTORY PLANNING AND CONTROL 7.1 PURPOSES OF HOLDING INVENTORY Remember that the goal of a public health supply chain is to improve health outcomes. This goal is achieved
More informationLOADING AND SEQUENCING JOBS WITH A FASTEST MACHINE AMONG OTHERS
Advances in Production Engineering & Management 4 (2009) 3, 127-138 ISSN 1854-6250 Scientific paper LOADING AND SEQUENCING JOBS WITH A FASTEST MACHINE AMONG OTHERS Ahmad, I. * & Al-aney, K.I.M. ** *Department
More informationKeyur C. Patel a *, Vikas J. Lakhera b, Dilip Sarda c
Available online at www.sciencedirect.com Procedia Engineering 51 ( 013 ) 650 654 Chemical, Civil and Mechanical Engineering Tracks o 3 rd Nirma University International Conerence (NUiCONE 01) Thermal
More informationSINGLE MACHINE SEQUENCING. ISE480 Sequencing and Scheduling Fall semestre
SINGLE MACHINE SEQUENCING 2011 2012 Fall semestre INTRODUCTION The pure sequencing problem is a specialized scheduling problem in which an ordering of the jobs completely determines a schedule. Moreover,
More informationPERT 03 Manajemen Persediaan (1) Fungsi Inventory Inventory Management Inventory Model dengan Independent Demand. EOQ Model
PERT 03 Manajemen Persediaan (1) Fungsi Inventory Inventory Management Inventory Model dengan Independent Demand. EOQ Model What Is Inventory? Stock of items kept to meet future demand Purpose of inventory
More informationOptimal Policy for an Inventory Model without Shortages considering Fuzziness in Demand, Holding Cost and Ordering Cost
Optimal Policy for an Inventory Model without Shortages considering Fuzziness in Demand Holding ost and Ordering ost 1 2 : : Abstract In this paper an inventory model without shortages has been considered
More informationA MANAGER S ROADMAP GUIDE FOR LATERAL TRANS-SHIPMENT IN SUPPLY CHAIN INVENTORY MANAGEMENT
A MANAGER S ROADMAP GUIDE FOR LATERAL TRANS-SHIPMENT IN SUPPLY CHAIN INVENTORY MANAGEMENT By implementing the proposed five decision rules for lateral trans-shipment decision support, professional inventory
More informationMonte Carlo Simulation for Sparepart Inventory
Monte Carlo Simulation for Sparepart Inventory I Nyoman Pujawan 1, Niniet Indah Arvitrida 2, and Bathamas P. Asihanto Department of Industrial Engineering Sepuluh Nopember Institute of Technology Surabaya,
More informationMore-Advanced Statistical Sampling Concepts for Tests of Controls and Tests of Balances
APPENDIX 10B More-Advanced Statistical Sampling Concepts for Tests of Controls and Tests of Balances Appendix 10B contains more mathematical and statistical details related to the test of controls sampling
More informationFilm Capacitors. Quality. Date: May 2009
Film Capacitors Quality Date: May 2009 EPCOS AG 2009. Reproduction, publication and dissemination of this publication, enclosures hereto and the information contained therein without EPCOS' prior express
More informationOperations Management
Operations Management Chapter 16 JIT and Lean Operations PowerPoint presentation to accompany Heizer/Render Operations Management, 11ed Some additions and deletions have been made by Ömer Yağız to this
More informationSTP351: Purchase Order Collaboration in SNC
SAP Training Source To Pay STP351: Purchase Order Collaboration in SNC External User Training Version: 4.0 Last Updated: 03-Apr-2017 3M Business Transformation & Information Technology Progress set in
More informationInventory Decisions for the Price Setting Retailer: Extensions to the EOQ Setting
Inventory Decisions for the Price Setting Retailer: Extensions to the EOQ Setting by Raynier Ramasra A thesis presented to the University of Waterloo in fulfillment of the thesis requirement for the degree
More informationDesign of Six Sigma Supply Chains
Proceedings of the 3 IEEE International Conference on Robotics & Automation Taipei, Taiwan, September 14-19, 3 Design of Six Sigma Supply Chains Dinesh Garg Indian Institute of Science Bangalore - India
More informationOutline. Push-Pull Systems Global Company Profile: Toyota Motor Corporation Just-in-Time, the Toyota Production System, and Lean Operations
JIT and Lean Operations Outline Push-Pull Systems Global Company Profile: Toyota Motor Corporation Just-in-Time, the Toyota Production System, and Lean Operations Eliminate Waste Remove Variability Improve
More informationThe maximum sustainable yield management of an age-structured salmon population: Fishing vs. conservation
The maximum sustainable yield management o an age-structured salmon population: Fishing vs. conservation Anders Skonhot Department o Economics Norwegian University o Science and Technology (Anders.skonhot@svt.ntnu.no)
More informationRetail Inventory Management for Perishable Products with Two Bins Strategy
Retail Inventory Management for Perishable Products with Two Bins Strategy Madhukar Nagare, Pankaj Dutta, and Amey Kambli Abstract Perishable goods constitute a large portion of retailer inventory and
More information