STRATEGIC CAPACITY PLANNING USING STOCK CONTROL MODEL

Save this PDF as:

Size: px
Start display at page:

Transcription

1 Session 6. Applications of Mathematical Methods to Logistics and Business Proceedings of the 9th International Conference Reliability and Statistics in Transportation and Communication (RelStat 09), October 2009, Riga, Latvia, p ISBN Transport and Telecommunication Institute, Lomonosova 1, LV-1019, Riga, Latvia STRATEGIC CAPACITY PLANNING USING STOCK CONTROL MODEL Arnoldina Pabedinskaitė Vilnius Gediminas Technical University Faculty of Business Management Sauletekio av. 11, Vilnius, Lithuania The use of stock management model allows identifying the type of optimal strategy of production capacities. Namely, the optimal strategy of development for the infinite planning period is determined by two values: s and S. The development of capacities takes places only when the level of stocks decreases to the critical level s and the stocks are replenished up to the level S. In the case of a finite planning period, the optimal strategy of replenishing of stocks depends on the initial level of stocks and is determined by the set of pairs (s τ, S τ ), i.e., the planning period of each year has its own critical level of stocks s τ and the level S τ, up to which the stocks are replenished. Keywords: capacity planning, stock control, stochastic demand Introduction The aim of strategy capacity planning is to provide methods of determining a general capacity level of capital-intensive resources, namely, industrial premises, equipment and the aggregate volume of workforce, which would best support the long-term competitive strategy of a company. The selected level of production capacity exerts an enormous influence on the ability of a company to respond to actions of competitors, the structure of its expenses, the policy of stock management and the necessity of organisation of efficient work of the managerial personnel and the entire staff. If the capacity is insufficient, a company could lose customers due to their slow servicing or allow competitors to enter its sales market. If the production (or servicing) capacity is excessive, it is highly likely that, in order to remain in operation and stimulate the demand for its products, a company would be forced either to lower the price of its products (or services), or to make only a partial use of labour resources, or to create excessive stocks of goods and materials, or to manufacture additional and less profitable products [1,2]. The strategic managerial decisions in business are made on the basis of the information which is either insufficient or uncertain or both. In respect of the degree of certainty, the information used when solving managerial tasks is usually subdivided into three main groups [3-8]: Deterministic information or conditions of full certainty, when the information required for decision-making is known in its entirety and is highly specific and certain; Conditions of risk, when possible values of factors and probabilities of these values are known; Conditions of uncertainty, when no certain information is available about external factors. The methods and models of mathematical programming (linear, non-linear, integer and dynamic programming) are applied to facilitate decision-making under deterministic conditions. When making decisions under risk conditions, which is typical of finance management tasks, as there is a possibility of long-term observation of statistical characteristics of fluctuations on a stock exchange being the consequence and also the cause of fluctuations of other financial quantities, possible values of uncontrolled factors and probabilities of these values are known. In such cases, that is, under risk conditions, the methods of decision matrix, decision tree, Bayesian analysis, stock management models, simulation modelling and various stochastic programming models are widely applied for the making of managerial decisions. Heuristic rules, simulation modelling, methods of fuzzy sets, assessment of subjective probability, etc. are used for decision-making under conditions of uncertainty. A substantial uncertainty of information in respect of external factors is typical of strategic decisions. When no information is available about external factors, it is expedient to make certain assumptions about possible values and their probabilities and then to apply the methods which are analogous to the methods applied under risk 303

2 The 9 th International Conference RELIABILITY and STATISTICS in TRANSPORTATION and COMMUNICATION conditions. Incomplete certainty of information as typical of managerial tasks may be taken into consideration when modelling a real situation in a variety of ways: introduction into the formulation of a task of probabilities of complying with individual resource constraints (considering that individual constraints are independent, because resources are not interchangeable), setting the probability of complying with the entire set of constraints (if resources may be interchangeable to some extent), formulation of the objective function in the form of mathematical expectation of a certain function or introduction into the objective function of various penalty coefficients for default on constraints, etc., formulation of different scenarios of stochastic and parametric programming tasks [4-7, 9, 10]. The system of capacity and production planning is of special importance when seeking to improve the function of a company s production planning, which aims at planning and monitoring of production process, which is planning and monitoring of resources, workforce, and equipment. A successful system of a company s activity planning enables managers to efficiently control the flow of raw materials, to effectively utilise human resources and machinery, to co-ordinate activities inside the company with the actions of suppliers, and to maintain contact with consumers with respect to market requirements. External environment forces, such as new technologies, products, processes, and global competition, constantly dictate competition conditions on the market under which a company should operate. The main factors that determine the competitiveness of the enterprise are quality and cost. A successful capacity and production planning reduces stocks and increases labour productivity, reduces production time, and improves equipment and employees utilisation, which, responding to the fluctuations of market requirements, cuts down the cost and improves the quality and elasticity of production. Production planning in a company usually covers several stages. At the initial stage, a highly aggregated production plan as a part of a company s activity plan is prepared. General production targets are provided for by the company s activity plan, which relates and co-ordinates the activities of various offices, such as design, marketing, production, finance. This strategic plan is developed into the strategic plans of functional fields and the budgets and capacities of the offices. A production plan is a constituent part of this plan and provides for production volumes necessary to reach the company s general goals. Production planning is most closely linked to marketing planning. At the next stage, the main calendar production plan assessing the products demand, resources as well as aggregate capacities is prepared. The main plan is further developed into a more detailed plan of materials and capacity requirements, which reflects the amount of capacities and materials necessary for the implementation of the general plan. The last stage is the implementation of the mentioned plans in the company s shops and sales offices. At each stage of production planning, a demand of a different aggregation level is forecasted, with a whole complex of methods and techniques employed for this purpose. The notion of production planning encompasses the planning of the equipment, raw materials, and human resources. By planning we seek to ensure the implementation of production targets. Production process depends on what method of production organisation is going to be selected. The selection of a method, in its turn, should take into consideration the kind of product that is going to be manufactured, the time needed for raw materials sourcing and production of components, and the type and number of operations to be carried out. Finally, the goals set for a particular production area should be considered. Production planning can be of several kinds ranging from long-term production planning, which is necessary for strategic planning, to the preparation of a short-term working timetable or a calendar plan, which includes a week or even a day [11, 12, 13]. Capacity planning helps to prepare more accurate calendar plans of resources procurement and coordinate available capacities with requirements more efficiently. For this purpose it is necessary to determine if there are free capacities as regards the targets of the main production plan and successive actions that seek to relate the capacities with demand for them. Insufficient capacities present an important problem, which has to be quickly solved. However, a company also has to face certain difficulties when its capacities are too large. The initial data for the solution of tasks of long- and medium-term planning of development of corporate production capacities usually include results of demand forecasting, which allows to present the demand for products in the form of probability distribution having regard to the growth of uncertainty in proportion to the increasing remoteness of the planned year. Formulation of the Model Process of strategic capacity planning could be viewed as the stochastic model of stock management. Let us suppose that the demand of product in the year τ (τ = 1, 2 ) is described as a stochastic process Q τ with independent increments. Let us refer to the increment Q τ+1 - Q τ = ω τ as the 304

3 Session 6. Applications of Mathematical Methods to Logistics and Business demand in the year τ of the planned period. Where the value of production capacities at the beginning of the year τ is equal to М τ, the difference М τ - Q τ will be referred to as the level of stocks in the year τ. As the growth of capacities develops in the year τ is used to meet the need for products of the year τ+1, the value of delay of supply in respect of the present model of stock management is equal to λ = 1 (the batch ordered in the segment τ is supplied in the segment τ + λ). Let us introduce the following symbols reflecting different stages of the process of replenishing of stocks: i τ the level of available stocks at the beginning of the segment τ until supply of the previous year; i τ = М τ-1 - Q τ or, this may be considered as the level of stocks at the end of the first year; q τ the quantity of stocks ordered in the segment τ, or the growth of production capacities in the year τ; h τ the level of stocks at the beginning of the segment τ available to meet the demand in the segment of τ, that is, the available amount of stocks i τ plus the supplied amount of stocks ordered at the beginning of the segment τ, or h τ = i τ + q τ-1 ; hence, h τ is the level of available stocks after supply of the previous year; let us consider that such are stocks at the beginning of the year τ; if ω 0 = Q 1, then the level of stocks at the beginning of the year τ is expressed by means of the demand ω τ-1 in the following manner: h τ = М τ - ω τ-1 ; j τ the level of available stocks at the beginning of the segment τ plus the amount ordered until the beginning of τ and by the taking of the next decision on replenishing of the stocks; that is, j τ = i τ + q τ-1 ; we can see that the numerical values of h τ and j τ are equal, however j τ stands for the value of stocks expected or planned by the taking of the decision, whereas h τ stands for the actual value following the replenishment; the numerical values of the variables j τ and h τ are in this care identical, because the model does not identify as separate (there is no such necessity) the ordering in the year τ (taking of a decision regarding q τ ) and the delivery of the stocks ordered in the year τ, we can regard both as taking place simultaneously, from 31 December of the year (τ - 1) to 1 January of the year τ; y τ the planned level of stocks at the beginning of the year τ plus the amount ordered in the segment τ, however without taking into account the demand of the current year, that is, y τ = h τ + q τ or y τ = М τ - Q τ + q τ. The amount of stocks available to meet the demand in the segment τ + 1 will be equal to h τ+1 = y τ - ω τ. (1) Therefore, in order to meet the demand of the year τ ω τ = Q τ+1 - Q τ and by the taking of a decision regarding the next period τ+1, we obtain the level of available stocks h τ. Following a decision concerning the value of growth of capacities q τ,, the level of stocks will be equal to y τ, and to meet the demand of the next year we will have h τ+1 = y τ - ω τ according to (1). In the model of stock management, the objective function takes the form of the discounted aggregate amount of costs expected in each period. Let с τ (q) be the price of the amount of stock q ordered in the segment τ. In the task of capacity development, с τ (q) stands for the costs of development of new production capacities of the value q. Further, H τ (h, ω) is the costs of maintenance of capacities and losses in the segment τ in the case when the stocks h are available to satisfy the demand ω. Let us suppose that the costs H τ (h, ω) depend only on the difference between the values h and ω, that is H τ (h, ω) = R( h ω), h ω 0, N( ω h), h ω < 0. (2) An important particular instance of (2) is the case when both functions in the right half are linear ones, that is, R(h-ω) = r (h-ω), where r is the coefficient of the costs per unit of spare capacities, and N(h-ω) = k (h-ω), where k is the coefficient of the losses due to the lack of the unit of production capacities. Then the expected costs of stock maintenance and losses in the segment τ+1, with y τ = y, equal to: L τ (y) = y θ = 0 r( y θ ) p( θ ) + θ= 0 θ> y k( θ y) p( θ ), y < 0, k( θ y) p( θ ), y 0, where θ = ϖ τ + ϖ τ+1, а р(θ) is distribution of this amount. (3) 305

4 The 9 th International Conference RELIABILITY and STATISTICS in TRANSPORTATION and COMMUNICATION The task of minimisation consists of finding the optimal volumes of the growth of capacities q τ (j τ ) for each segment τ and for each possible value of j τ (or, which is the same, determination of the optimal values y τ (j τ ) j τ ). Supposing that the dependence of y τ on j τ in implicit form is included below, we will get the following expression for the expected value of costs for the selected strategy y τ, τ = 1,, Т and the initial level of stocks j 1 E T τ = 1 τ β [ cτ ( y jτ ) + Lτ ( yτ )]. (4) Such an averaging is made with regard to all possible sequences of demand values, and the value β is the coefficient of discounting during the given period. The recursion equation of dynamic programming, which helps to obtain an optimal strategy minimising the expression (4), has the form f τ (j) = min [c τ (y-j) + L τ (y) + β f τ+1 (y-ω)p τ (ω)] (5) with τ = 1, 2,..., Т, f τ+1 (j) = 0. The functions f τ (j) are calculated in an ordinary way for each possible value of j, starting with τ = Т, Т-1,..., 1. In practice, the range of variation of ω is limited by such a finite value at which the function of demand distribution is close to 1. Similarly, the interval of variation of the value of j is imposed a restriction determined by the nature of a real-life situation. The above-mentioned formulation of the task of capacity development in the form of a stock management task allows making use of results of the theory and draws certain general conclusions regarding the optimal strategy of replenishing of stocks in the cases of the finite and infinite planning periods [7]. Case of the Finite Planning Period Let us introduce the following assumptions in respect of the function of costs making up the objective function of the task: C τ (q) = 0, q = 0 k +, > 0, τ cτ q q k τ βk, τ + 1 (6) L τ (y) is convex and (c τ - βc τ+1 )y + L τ (y), if y, (7) where k τ 0, c τ 0, c τ+1 0. Where L τ (y) is determined by the expression (3), it can be easily noticed to be convex and L τ (y) if y. As unit costs of development of new capacities for various years of the planning period differ only by the coefficient of discounting, i.e., c τ =с, then (c τ - βc τ+1 ) = (1-β)с and the condition (7) is fully met. In these assumptions, a model described by the recursion equation (5) has an optimal (s τ, S τ ) - strategy, or a strategy such as for any period y τ (j) = j and q τ (j) = 0 if j s τ, y τ (j) = S τ and q τ (j) = S τ -j if j < s τ. (8) In such a way, if the level of stocks exceeds some critical level S τ, replenishing of stocks will not take place. However, if the level of stocks decreases down to the critical level s τ, a decision is made on the replenishing of stocks, and the stocks will be replenished up to the level S τ. Consequently, the optimal 306

5 Session 6. Applications of Mathematical Methods to Logistics and Business strategy for any segment τ is completely described by two parameters (s τ, S τ ) and does not depend on the level of stocks in the initial segment of the planning period. In the case when the optimal strategy of replenishing stocks has the (s τ, S τ ) -structure, the optimisation process as described by the recursion equation (5) is considerably simplified. The algorithm of determination of an optimal strategy in the segment τ has the following form: Step 1. Calculate g τ (y) c τ y + L τ (y) + β f τ+1 (y ω)p τ (ω) (9) for all values of y which are of interest and suppose that S τ 0 meets the condition g τ (y) = g τ ( S τ ). Step 2. Take as S τ the smallest of the numbers meeting the condition g τ ( S τ ) g τ ( S τ ) + К τ (S τ may have negative values). Step 3. Calculate f τ (j) according to the formula g τ ( S τ ) + К τ - с τ j, j < S τ L τ (y) + β f τ+1 (j ω)p τ (ω), j S τ. (10) In such a way, for each period τ the algorithm consists of a single operation of finding the minimum at Step 1, which replaces performance of an operation of minimisation for each j using the recursion equation (5), which describes a general model of stock management. The sequence of calculations according to the mentioned algorithm begins with τ=т, where f Т+1 0. It should be noted that in this case, there were no limitations imposed on distribution of demand p τ (ω). Case of the Infinite Planning Period In the process of planning of capacity development, each decision adopted is a part of the neverending chain of actions. A choice made earlier influences a current one; the decisions made at the present moment influence the future, etc. Therefore, it is natural to attempt to consider the development strategies in which the duration of the planning period is infinite. For a task with the infinite planning period to yield specific solutions, it is necessary to make some presumptions concerning a stationary state. In the simplest case, it is presumed that the general conditions of a system s functioning remain unchanged with time; in particular, the demand for products is described by a stationary random process. Although in real life the stationary state is rarely maintained during the periods of a long duration, when making decisions at the beginning of the planning period an approach based on the use of a stationary model with the infinite planning period is an efficient and relatively simple way of obtaining optimal planning solutions. Let us make the same presumptions in respect of the function of costs as in the case of the finite planning period, but let us also consider that the economic parameters of the model and distribution of demand are stationary during the infinite planning period. In particular, let us suppose that p τ (ω) = p(ω), 0, q = 0 C(q) = K + cq, q > 0, K L(y) is convex function and 0, c 0, (11) C(1-β)y + L(y) if y. (12) These assumptions make it possible to use the theorem of optimal stationary (s, S)-strategy. Where a task contains a model which is characterised by the recursion equation (5) and in which economic parameters meet the conditions of (11), (12), there exists an optimal (s, S)-strategy for which s τ =S and S τ =S at any value of τ during the infinite planning period. 307

6 The 9 th International Conference RELIABILITY and STATISTICS in TRANSPORTATION and COMMUNICATION A generalisation of the recursion equation (5) in respect of the infinite planning period can be presented in the following way: f(j) = min[c(y-j) + L(y) + β f(y-ω)p(ω)]. (13) A complete algorithm of effective calculation of the optimal (s, S)-strategy for the infinite planning period is sufficiently complicated. However, the case of normal distribution of demand allows applying a similar method [7]. The use of stock management model allows identifying the type of optimal strategy of production capacities. Namely, the optimal strategy of development for the infinite planning period is determined by two values: s and S. The development of capacities takes places only when the level of stocks decreases to the critical level s and the stocks are replenished up to the level S. In the case of a finite planning period, the optimal strategy of replenishing of stocks depends on the initial level of stocks and is determined by the set of pairs (s τ, S τ ), i.e., the planning period of each year has its own critical level of stocks s τ and the level S τ, up to which the stocks are replenished. Conclusions 1. Formulation of a task of development of production capacities in the form of a task of optimal stock management allows making use of results of the theory of optimal stock management. The presented algorithms of solving a task in the cases of the finite and infinite period have been formulated in the terms of the task of capacity planning and could be used when making strategic plans of development of corporate production capacities. 2. The considered models of development of production capacities enable to analyse the prepared development plans with a view to determining the impact of some types of costs on the optimal solution and also comparing the results obtained when making use of various models to solve a task of production capacity development. References 1. Thompson, A. A., Strickland, A. J. Strategic Management. McGraw-Hill, p. 2. Chase, R. B., Aquilano, N. J. and Jacobs, F. R. Production and Operations Management. McGraw Hill, p. 3. Armstrong, M. Handbook of Management Techniques. Kogan Page, p. 4. Moor, J.H., Weatherford, L.R. Decision Modelling with Microsoft Excel. Prentice Hall, p. 5. Taha, H. А. Introduction to Operations Research. D. Pearson Education, p. 6. Curwin, J., Slater, R. Quantitative Methods for Business Decisions. London: Thomson, p. 7. Wagner, H. M. Principles of Operations Research. Prentice Hall, p. 8. Paliulis, N., Chlivickas, E., Pabedinskaitė, A. Management and Information. Vilnius: Technika., p. (In Lithuanian) 9. Pabedinskaitė, Arnoldina. Reliability of Management Decisions under Stochastic Demand, Transport and Telecommunication, Vol. 7, No 3, 2006, pp (In Russian) 10. Vakrinienė, Sigutė; Pabedinskaitė, Arnoldina. Parametric and Stochastic Models for Optimal Investment, Transport and Telecommunication, Vol. 7, No 3, 2006, pp (In Russian) 11. Dillworth, J. B. Operations Management. McGraw Hill, p. 12. Stevenson, W.J. Production/Operations Management. Boston: Homewood Irwin, p. 13. Slack, N., Chambers, S. and Johnston, R. Operations Management. Prentice Hall, p. 308

A Programme Implementation of Several Inventory Control Algorithms

BULGARIAN ACADEMY OF SCIENCES CYBERNETICS AND INFORMATION TECHNOLOGIES Volume, No Sofia 20 A Programme Implementation of Several Inventory Control Algorithms Vladimir Monov, Tasho Tashev Institute of Information

REGRESSION MODEL OF SALES VOLUME FROM WHOLESALE WAREHOUSE

REGRESSION MODEL OF SALES VOLUME FROM WHOLESALE WAREHOUSE Diana Santalova Faculty of Transport and Mechanical Engineering Riga Technical University 1 Kalku Str., LV-1658, Riga, Latvia E-mail: Diana.Santalova@rtu.lv

Optimum decision-making process for an Economic Order Quantity (EOQ) model with budget and floor space constraints

American Journal of Service Science and Management 2014; 1(5): 53-57 Published online January 20, 2015 (http://www.openscienceonline.com/journal/ajssm) Optimum decision-making process for an Economic Order

Stochastic Inventory Control

Chapter 3 Stochastic Inventory Control 1 In this chapter, we consider in much greater details certain dynamic inventory control problems of the type already encountered in section 1.3. In addition to the

Single item inventory control under periodic review and a minimum order quantity

Single item inventory control under periodic review and a minimum order quantity G. P. Kiesmüller, A.G. de Kok, S. Dabia Faculty of Technology Management, Technische Universiteit Eindhoven, P.O. Box 513,

ARTIFICIAL NEURAL NETWORKS FOR ADAPTIVE MANAGEMENT TRAFFIC LIGHT OBJECTS AT THE INTERSECTION

The 10 th International Conference RELIABILITY and STATISTICS in TRANSPORTATION and COMMUNICATION - 2010 Proceedings of the 10th International Conference Reliability and Statistics in Transportation and

Glossary of Inventory Management Terms

Glossary of Inventory Management Terms ABC analysis also called Pareto analysis or the rule of 80/20, is a way of categorizing inventory items into different types depending on value and use Aggregate

SPARE PARTS INVENTORY SYSTEMS UNDER AN INCREASING FAILURE RATE DEMAND INTERVAL DISTRIBUTION

SPARE PARS INVENORY SYSEMS UNDER AN INCREASING FAILURE RAE DEMAND INERVAL DISRIBUION Safa Saidane 1, M. Zied Babai 2, M. Salah Aguir 3, Ouajdi Korbaa 4 1 National School of Computer Sciences (unisia),

INTEGRATED OPTIMIZATION OF SAFETY STOCK

INTEGRATED OPTIMIZATION OF SAFETY STOCK AND TRANSPORTATION CAPACITY Horst Tempelmeier Department of Production Management University of Cologne Albertus-Magnus-Platz D-50932 Koeln, Germany http://www.spw.uni-koeln.de/

An Entropic Order Quantity (EnOQ) Model. with Post Deterioration Cash Discounts

Int. J. Contemp. Math. Sciences, Vol. 6,, no. 9, 93-939 An Entropic Order Quantity (EnOQ Model with Post Deterioration Cash Discounts M. Pattnaik Dept. of Business Administration, Utkal University Bhubaneswar-754,

Forecasting in supply chains

1 Forecasting in supply chains Role of demand forecasting Effective transportation system or supply chain design is predicated on the availability of accurate inputs to the modeling process. One of the

COMBINING THE METHODS OF FORECASTING AND DECISION-MAKING TO OPTIMISE THE FINANCIAL PERFORMANCE OF SMALL ENTERPRISES

COMBINING THE METHODS OF FORECASTING AND DECISION-MAKING TO OPTIMISE THE FINANCIAL PERFORMANCE OF SMALL ENTERPRISES JULIA IGOREVNA LARIONOVA 1 ANNA NIKOLAEVNA TIKHOMIROVA 2 1, 2 The National Nuclear Research

Optimal proportional reinsurance and dividend pay-out for insurance companies with switching reserves

Optimal proportional reinsurance and dividend pay-out for insurance companies with switching reserves Abstract: This paper presents a model for an insurance company that controls its risk and dividend

It is assumed that students are familiar with the topics typically taught in introductory operations management courses.

G-SCOM 1 Advanced Operations Management Using the quantitative and best practice methodologies, this course will explore the management of the global operations. This module focuses on the design, planning

A 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

Optimal base-stock policy for the inventory system with periodic review, backorders and sequential lead times

44 Int. J. Inventory Research, Vol. 1, No. 1, 2008 Optimal base-stock policy for the inventory system with periodic review, backorders and sequential lead times Søren Glud Johansen Department of Operations

PROBLEMATICAL POINTS IN THE E-TRADE SYSTEM

Proceedings of the 10th International Conference Reliability and Statistics in Transportation and Communication (RelStat 10), 20 23 October 2010, Riga, Latvia, p. 250-254. ISBN 978-9984-818-34-4 Transport

INVENTORY MODELS WITH STOCK- AND PRICE- DEPENDENT DEMAND FOR DETERIORATING ITEMS BASED ON LIMITED SHELF SPACE

Yugoslav Journal of Operations Research Volume 0 (00), Number, 55-69 0.98/YJOR00055D INVENTORY MODELS WITH STOCK- AND PRICE- DEPENDENT DEMAND FOR DETERIORATING ITEMS BASED ON LIMITED SHELF SPACE Chun-Tao

Inventory Management - A Teaching Note

Inventory Management - A Teaching Note Sundaravalli Narayanaswami W.P. No.2014-09-01 September 2014 INDIAN INSTITUTE OF MANAGEMENT AHMEDABAD-380 015 INDIA Inventory Management - A Teaching Note Sundaravalli

A simulation study on supply chain performance with uncertainty using contract. Creative Commons: Attribution 3.0 Hong Kong License

Title A simulation study on supply chain performance with uncertainty using contract Author(s) Chan, FTS; Chan, HK Citation IEEE International Symposium on Intelligent Control Proceedings, the 13th Mediterrean

THE POPULARITY OF STUDY PROGRAMMES IN AVIATION AREA AMONG THE APPLICANTS TO LITHUANIAN HIGHER SCHOOLS

Session 5. Innovations in Education and Research Proceedings of the 9th International Conference Reliability and Statistics in Transportation and Communication (RelStat 09), 2124 October 2009, Riga, Latvia,

Forecasting Demand for Automotive Aftermarket Inventories

Informatica Economică vol. 17, no. 2/2013 119 Forecasting Demand for Automotive Aftermarket Inventories Ovidiu DOBRICAN West University of Timisoara ovidiu.dobrican@feaa.uvt.ro Management decisions regarding

240ST1131 - Operations Management in the Supply Chain

Coordinating unit: Teaching unit: Academic year: Degree: ECTS credits: 2015 240 - ETSEIB - Barcelona School of Industrial Engineering 732 - OE - Department of Management MASTER'S DEGREE IN SUPPLY CHAINS,

Revenue Management for Transportation Problems

Revenue Management for Transportation Problems Francesca Guerriero Giovanna Miglionico Filomena Olivito Department of Electronic Informatics and Systems, University of Calabria Via P. Bucci, 87036 Rende

APPLICATION OF GENETIC ALGORITHMS IN INVENTORY MANAGEMENT

DAAAM INTERNATIONAL SCIENTIFIC BOOK 2010 pp. 245-258 CHAPTER 25 APPLICATION OF GENETIC ALGORITHMS IN INVENTORY MANAGEMENT DANIA, W.A.P. Abstract: Inventory cost is a main component of total logistic costs.

Optimization of the physical distribution of furniture. Sergey Victorovich Noskov

Optimization of the physical distribution of furniture Sergey Victorovich Noskov Samara State University of Economics, Soviet Army Street, 141, Samara, 443090, Russian Federation Abstract. Revealed a significant

An integrated Single Vendor-Single Buyer Production Inventory System Incorporating Warehouse Sizing Decisions 창고 크기의사결정을 포함한 단일 공급자구매자 생산재고 통합관리 시스템

Journal of the Korean Institute of Industrial Engineers Vol. 40, No. 1, pp. 108-117, February 2014. ISSN 1225-0988 EISSN 2234-6457 http://dx.doi.org/10.7232/jkiie.2014.40.1.108 2014 KIIE

Inventory Management. NEELU TIWARI Jaipuria Institute of Management, Vasundhara Gzb.

INTERNATIONAL JOURNAL OF BUSINESS MANAGEMENT, ECONOMICS AND INFORMATION TECHNOLOGY Vol. 3, No. 2, July-December 2011: 303-207 Inventory Management NEELU TIWARI Jaipuria Institute of Management, Vasundhara

A FUZZY LOGIC APPROACH FOR SALES FORECASTING

A FUZZY LOGIC APPROACH FOR SALES FORECASTING ABSTRACT Sales forecasting proved to be very important in marketing where managers need to learn from historical data. Many methods have become available for

LINEAR PROGRAMMING WITH ONLINE LEARNING

LINEAR PROGRAMMING WITH ONLINE LEARNING TATSIANA LEVINA, YURI LEVIN, JEFF MCGILL, AND MIKHAIL NEDIAK SCHOOL OF BUSINESS, QUEEN S UNIVERSITY, 143 UNION ST., KINGSTON, ON, K7L 3N6, CANADA E-MAIL:{TLEVIN,YLEVIN,JMCGILL,MNEDIAK}@BUSINESS.QUEENSU.CA

Spreadsheet Heuristic for Stochastic Demand Environments to Solve the Joint Replenishment Problem

, July 3-5, 2013, London, U.K. Spreadsheet Heuristic for Stochastic Demand Environments to Solve the Joint Replenishment Problem Buket Türkay, S. Emre Alptekin Abstract In this paper, a new adaptation

Detection of changes in variance using binary segmentation and optimal partitioning

Detection of changes in variance using binary segmentation and optimal partitioning Christian Rohrbeck Abstract This work explores the performance of binary segmentation and optimal partitioning in the

Marketing Mix Modelling and Big Data P. M Cain

1) Introduction Marketing Mix Modelling and Big Data P. M Cain Big data is generally defined in terms of the volume and variety of structured and unstructured information. Whereas structured data is stored

An Extension Model of Financially-balanced Bonus-Malus System

An Extension Model of Financially-balanced Bonus-Malus System Other : Ratemaking, Experience Rating XIAO, Yugu Center for Applied Statistics, Renmin University of China, Beijing, 00872, P.R. China Phone:

Optimal Scheduling for Dependent Details Processing Using MS Excel Solver

BULGARIAN ACADEMY OF SCIENCES CYBERNETICS AND INFORMATION TECHNOLOGIES Volume 8, No 2 Sofia 2008 Optimal Scheduling for Dependent Details Processing Using MS Excel Solver Daniela Borissova Institute of

Svetlana Saksonova University of Latvia

LEONARDO DA VINCI Transfer of Innovation Svetlana Saksonova University of Latvia Managerial Accounting for Decision-making Leonardo da Vinci programme project Development and Approbation of Applied Courses

SUPPLIER SELECTION IN A CLOSED-LOOP SUPPLY CHAIN NETWORK

SUPPLIER SELECTION IN A CLOSED-LOOP SUPPLY CHAIN NETWORK Satish Nukala, Northeastern University, Boston, MA 025, (67)-373-7635, snukala@coe.neu.edu Surendra M. Gupta*, Northeastern University, Boston,

Factoring Patterns in the Gaussian Plane

Factoring Patterns in the Gaussian Plane Steve Phelps Introduction This paper describes discoveries made at the Park City Mathematics Institute, 00, as well as some proofs. Before the summer I understood

Nonparametric adaptive age replacement with a one-cycle criterion

Nonparametric adaptive age replacement with a one-cycle criterion P. Coolen-Schrijner, F.P.A. Coolen Department of Mathematical Sciences University of Durham, Durham, DH1 3LE, UK e-mail: Pauline.Schrijner@durham.ac.uk

SINGLE-STAGE MULTI-PRODUCT PRODUCTION AND INVENTORY SYSTEMS: AN ITERATIVE ALGORITHM BASED ON DYNAMIC SCHEDULING AND FIXED PITCH PRODUCTION

SIGLE-STAGE MULTI-PRODUCT PRODUCTIO AD IVETORY SYSTEMS: A ITERATIVE ALGORITHM BASED O DYAMIC SCHEDULIG AD FIXED PITCH PRODUCTIO Euclydes da Cunha eto ational Institute of Technology Rio de Janeiro, RJ

Optimal replenishment for a periodic review inventory system with two supply modes

European Journal of Operational Research 149 (2003) 229 244 Production, Manufacturing and Logistics Optimal replenishment for a periodic review inventory system with two supply modes Chi Chiang * www.elsevier.com/locate/dsw

High-Mix Low-Volume Flow Shop Manufacturing System Scheduling

Proceedings of the 14th IAC Symposium on Information Control Problems in Manufacturing, May 23-25, 2012 High-Mix Low-Volume low Shop Manufacturing System Scheduling Juraj Svancara, Zdenka Kralova Institute

1. (First passage/hitting times/gambler s ruin problem:) Suppose that X has a discrete state space and let i be a fixed state. Let

Copyright c 2009 by Karl Sigman 1 Stopping Times 1.1 Stopping Times: Definition Given a stochastic process X = {X n : n 0}, a random time τ is a discrete random variable on the same probability space as

POPULARITY OF STUDY PROGRAMMES IN TRANSPORT MANAGEMENT AMONG THE APPLICANTS TO LITHUANIAN HIGHER SCHOOLS, PARTICIPATING IN JOINT ADMISSION PROGRAMME

POPULARITY OF STUDY PROGRAMMES IN TRANSPORT MANAGEMENT AMONG THE APPLICANTS TO LITHUANIAN HIGHER SCHOOLS, PARTICIPATING IN JOINT ADMISSION PROGRAMME Olegas Prentkovskis 1, Romualdas Kliukas 2, Alfonsas

СLARA EXPERT VERBAL METHOD OF DETERMINING INVESTMENT RISK IN CONSTRUCTION

СLARA EXPERT VERBAL METHOD OF DETERMINING INVESTMENT RISK IN CONSTRUCTION Leonas Ustinovičius 1, Dmitry Kochin 2, Galina Ševčenko 1, 1 Department of Construction Technology and Management, Vilnius Gediminas

Operations and Supply Chain Management Prof. G. Srinivasan Department of Management Studies Indian Institute of Technology, Madras

Operations and Supply Chain Management Prof. G. Srinivasan Department of Management Studies Indian Institute of Technology, Madras Lecture - 36 Location Problems In this lecture, we continue the discussion

Simulation-based Optimization Approach to Clinical Trial Supply Chain Management

20 th European Symposium on Computer Aided Process Engineering ESCAPE20 S. Pierucci and G. Buzzi Ferraris (Editors) 2010 Elsevier B.V. All rights reserved. Simulation-based Optimization Approach to Clinical

Math Review. for the Quantitative Reasoning Measure of the GRE revised General Test

Math Review for the Quantitative Reasoning Measure of the GRE revised General Test www.ets.org Overview This Math Review will familiarize you with the mathematical skills and concepts that are important

Machine Learning in Statistical Arbitrage

Machine Learning in Statistical Arbitrage Xing Fu, Avinash Patra December 11, 2009 Abstract We apply machine learning methods to obtain an index arbitrage strategy. In particular, we employ linear regression

240IOI21 - Operations Management

Coordinating unit: 240 - ETSEIB - Barcelona School of Industrial Engineering Teaching unit: 732 - OE - Department of Management Academic year: Degree: 2015 MASTER'S DEGREE IN INDUSTRIAL ENGINEERING (Syllabus

INVESTMENT RISK MANAGEMENT AND ECONOMIC ASPECTS OF TRANSPORT INFRASTRUCTURE DEVELOPMENT

Transport and Telecommunication, 2012, Volume 13, No 2, 101 107 Transport and Telecommunication Institute, Lomonosova 1, Riga, LV-1019, Latvia DOI 10.2478/v10244-012-0008-6 INVESTMENT RISK MANAGEMENT AND

Stochastic Models for Inventory Management at Service Facilities

Stochastic Models for Inventory Management at Service Facilities O. Berman, E. Kim Presented by F. Zoghalchi University of Toronto Rotman School of Management Dec, 2012 Agenda 1 Problem description Deterministic

D Lab: Supply Chains

D Lab: Supply Chains Inventory Management Class outline: Roles of inventory Inventory related costs Types of inventory models Focus on EOQ model today (Newsvender model next class) Stephen C. Graves 2013

Discrete Optimization

Discrete Optimization [Chen, Batson, Dang: Applied integer Programming] Chapter 3 and 4.1-4.3 by Johan Högdahl and Victoria Svedberg Seminar 2, 2015-03-31 Todays presentation Chapter 3 Transforms using

TEACHING SIMULATION WITH SPREADSHEETS Jelena Pecherska and Yuri Merkuryev Deptartment of Modelling and Simulation Riga Technical University 1, Kalku Street, LV-1658 Riga, Latvia E-mail: merkur@itl.rtu.lv,

Optimal decision in fuzzy NLP EOQ model for a single item and dynamic setup cost with space constraint

ISSN 1 746-7233, England, UK World Journal of Modelling and Simulation Vol. 9 (2013) No. 1, pp. 74-80 Optimal decision in fuzzy NLP EOQ model for a single item and dynamic setup cost with space constraint

Operations Research in Production Planning, Scheduling, and Inventory Control

TECHNISCHE HOCHSCHULE DARMSTADT Fachbereich 1 Ges am t b i bj_[o_t he_k Bet ri eb? wi rtscha\ \ si eh re Inventar-Nr. : Abstell-Nr. : Sachgebiete:.^~ Operations Research in Production Planning, Scheduling,

Forecasting methods applied to engineering management

Forecasting methods applied to engineering management Áron Szász-Gábor Abstract. This paper presents arguments for the usefulness of a simple forecasting application package for sustaining operational

Effective control policies for stochastic inventory systems with a minimum order quantity and linear costs \$

Int. J. Production Economics 106 (2007) 523 531 www.elsevier.com/locate/ijpe Effective control policies for stochastic inventory systems with a minimum order quantity and linear costs \$ Bin Zhou, Yao Zhao,

Resource grouping selection to minimize the maximum over capacity planning

2012 International Conference on Industrial and Intelligent Information (ICIII 2012) IPCSIT vol.31 (2012) (2012) IACSIT Press, Singapore Resource grouping selection to minimize the maximum over capacity

INDIRECT INFERENCE (prepared for: The New Palgrave Dictionary of Economics, Second Edition)

INDIRECT INFERENCE (prepared for: The New Palgrave Dictionary of Economics, Second Edition) Abstract Indirect inference is a simulation-based method for estimating the parameters of economic models. Its

An Autonomous Agent for Supply Chain Management

In Gedas Adomavicius and Alok Gupta, editors, Handbooks in Information Systems Series: Business Computing, Emerald Group, 2009. An Autonomous Agent for Supply Chain Management David Pardoe, Peter Stone

Exact Fill Rates for the (R, S) Inventory Control with Discrete Distributed Demands for the Backordering Case

Informatica Economică vol. 6, no. 3/22 9 Exact Fill ates for the (, S) Inventory Control with Discrete Distributed Demands for the Backordering Case Eugenia BABILONI, Ester GUIJAO, Manuel CADÓS, Sofía

Supplement to Call Centers with Delay Information: Models and Insights

Supplement to Call Centers with Delay Information: Models and Insights Oualid Jouini 1 Zeynep Akşin 2 Yves Dallery 1 1 Laboratoire Genie Industriel, Ecole Centrale Paris, Grande Voie des Vignes, 92290

What is Linear Programming?

Chapter 1 What is Linear Programming? An optimization problem usually has three essential ingredients: a variable vector x consisting of a set of unknowns to be determined, an objective function of x to

A Comparative Study of the Pickup Method and its Variations Using a Simulated Hotel Reservation Data

A Comparative Study of the Pickup Method and its Variations Using a Simulated Hotel Reservation Data Athanasius Zakhary, Neamat El Gayar Faculty of Computers and Information Cairo University, Giza, Egypt

A Profit-Maximizing Production Lot sizing Decision Model with Stochastic Demand

A Profit-Maximizing Production Lot sizing Decision Model with Stochastic Demand Kizito Paul Mubiru Department of Mechanical and Production Engineering Kyambogo University, Uganda Abstract - Demand uncertainty

Impact of Proper Inventory Control System of Electric Equipment in RUET: A Case Study

Impact of Proper Inventory ontrol System of Electric Equipment in RUET: A ase Study Khairun Nahar*, Khan Md. Ariful Haque**, Mahbub A Jannat*, Sonia Akhter*, Umme Khatune Jannat* and Md. Mosharraf Hossain

EEV, MCEV, Solvency, IFRS a chance for actuarial mathematics to get to main-stream of insurance value chain

EEV, MCEV, Solvency, IFRS a chance for actuarial mathematics to get to main-stream of insurance value chain dr Krzysztof Stroiński, dr Renata Onisk, dr Konrad Szuster, mgr Marcin Szczuka 9 June 2008 Presentation

Package SCperf. February 19, 2015

Package SCperf February 19, 2015 Type Package Title Supply Chain Perform Version 1.0 Date 2012-01-22 Author Marlene Silva Marchena Maintainer The package implements different inventory models, the bullwhip

GESTION DE LA PRODUCTION ET DES OPERATIONS PICASSO EXERCICE INTEGRE

ECAP 21 / PROD2100 GESTION DE LA PRODUCTION ET DES OPERATIONS PICASSO EXERCICE INTEGRE 2004-2005 Prof : Pierre Semal : semal@poms.ucl.ac.be Assistants : Eléonore de le Court : delecourt@poms.ucl.ac.be

Numerical methods for American options

Lecture 9 Numerical methods for American options Lecture Notes by Andrzej Palczewski Computational Finance p. 1 American options The holder of an American option has the right to exercise it at any moment

1 The EOQ and Extensions

IEOR4000: Production Management Lecture 2 Professor Guillermo Gallego September 9, 2004 Lecture Plan 1. The EOQ and Extensions 2. Multi-Item EOQ Model 1 The EOQ and Extensions This section is devoted to

2.1 Model Development: Economic Order Quantity (EOQ) Model

_ EOQ Model The first model we will present is called the economic order quantity (EOQ) model. This model is studied first owing to its simplicity. Simplicity and restrictive modeling assumptions usually

Analysis of Bayesian Dynamic Linear Models

Analysis of Bayesian Dynamic Linear Models Emily M. Casleton December 17, 2010 1 Introduction The main purpose of this project is to explore the Bayesian analysis of Dynamic Linear Models (DLMs). The main

A LOT-SIZING PROBLEM WITH TIME VARIATION IMPACT IN CLOSED-LOOP SUPPLY CHAINS

A LOT-SIZING PROBLEM WITH TIME VARIATION IMPACT IN CLOSED-LOOP SUPPLY CHAINS Aya Ishigaki*, ishigaki@rs.noda.tus.ac.jp; Tokyo University of Science, Japan Tetsuo Yamada, tyamada@uec.ac.jp; The University

24. The Branch and Bound Method

24. The Branch and Bound Method It has serious practical consequences if it is known that a combinatorial problem is NP-complete. Then one can conclude according to the present state of science that no

ECON20310 LECTURE SYNOPSIS REAL BUSINESS CYCLE

ECON20310 LECTURE SYNOPSIS REAL BUSINESS CYCLE YUAN TIAN This synopsis is designed merely for keep a record of the materials covered in lectures. Please refer to your own lecture notes for all proofs.

Monte Carlo Methods in Finance

Author: Yiyang Yang Advisor: Pr. Xiaolin Li, Pr. Zari Rachev Department of Applied Mathematics and Statistics State University of New York at Stony Brook October 2, 2012 Outline Introduction 1 Introduction

Case study of a batch-production/inventory system E.M.M. Winands 1, A.G. de Kok 2 and C. Timpe 3

Case study of a batch-production/inventory system E.M.M. Winands 1, A.G. de Kok 2 and C. Timpe 3 The plant of BASF under consideration consists of multiple parallel production lines, which produce multiple

Modeling Multi-Echelon Multi-Supplier Repairable Inventory Systems with Backorders

J. Service Science & Management, 2010, 3, 440-448 doi:10.4236/jssm.2010.34050 Published Online December 2010 (http://www.scirp.org/journal/jssm) Modeling Multi-Echelon Multi-Supplier Repairable Inventory

The Myth of the Discounted Cash Flow

The Myth of the Discounted Cash Flow (How Uncertainty Affects Liabilities) by Tom Mullin ttmullin@bigpond.com 03-9372-5349 The Myth of the Discounted Cash Flow Page 2 Table of Contents 1. Précis...3 2.

Oscillatory Reduction in Option Pricing Formula Using Shifted Poisson and Linear Approximation

EPJ Web of Conferences 68, 0 00 06 (2014) DOI: 10.1051/ epjconf/ 20146800006 C Owned by the authors, published by EDP Sciences, 2014 Oscillatory Reduction in Option Pricing Formula Using Shifted Poisson

Supply Chain Management in the Computer Industry Katta G. Murty Department of Industrial and Operations Engineering University of Michigan, Ann Arbor, Michigan 48109{2117, USA Phone: 734-763-3513 fax:

PERFORMANCE ANALYSIS OF A CONTRACT MANUFACTURING SYSTEM

PERFORMANCE ANALYSIS OF A CONTRACT MANUFACTURING SYSTEM Viswanadham.N 1, Vaidyanathan.G 2 The Logistics Institute- Asia Pacific National University of Singapore Singapore 11926 mpenv@nus.edu.sg 1 engp9778@nus.edu.sg

CRASHING-RISK-MODELING SOFTWARE (CRMS)

International Journal of Science, Environment and Technology, Vol. 4, No 2, 2015, 501 508 ISSN 2278-3687 (O) 2277-663X (P) CRASHING-RISK-MODELING SOFTWARE (CRMS) Nabil Semaan 1, Najib Georges 2 and Joe

ID Class MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question.

Exam: Operations Management Name Total Scors: ID Class MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. 1) Which of the following incorporates the

International Journal of Advances in Science and Technology (IJAST)

Determination of Economic Production Quantity with Regard to Machine Failure Mohammadali Pirayesh 1, Mahsa Yavari 2 1,2 Department of Industrial Engineering, Faculty of Engineering, Ferdowsi University

Optimization of Fuzzy Inventory Models under Fuzzy Demand and Fuzzy Lead Time

Tamsui Oxford Journal of Management Sciences, Vol. 0, No. (-6) Optimization of Fuzzy Inventory Models under Fuzzy Demand and Fuzzy Lead Time Chih-Hsun Hsieh (Received September 9, 00; Revised October,

CHAPTER 5. Number Theory. 1. Integers and Division. Discussion

CHAPTER 5 Number Theory 1. Integers and Division 1.1. Divisibility. Definition 1.1.1. Given two integers a and b we say a divides b if there is an integer c such that b = ac. If a divides b, we write a

Min/Max Inventory Planning for Military Logistics

21st International Congress on Modelling and Simulation, Gold Coast, Australia, 29 Nov to 4 Dec 2015 www.mssanz.org.au/modsim2015 Min/Max Inventory Planning for Military Logistics S. Shekh Defence Science

A joint control framework for supply chain planning

17 th European Symposium on Computer Aided Process Engineering ESCAPE17 V. Plesu and P.S. Agachi (Editors) 2007 Elsevier B.V. All rights reserved. 1 A joint control framework for supply chain planning

Profit Forecast Model Using Monte Carlo Simulation in Excel

Profit Forecast Model Using Monte Carlo Simulation in Excel Petru BALOGH Pompiliu GOLEA Valentin INCEU Dimitrie Cantemir Christian University Abstract Profit forecast is very important for any company.

TEACHING AGGREGATE PLANNING IN AN OPERATIONS MANAGEMENT COURSE

TEACHING AGGREGATE PLANNING IN AN OPERATIONS MANAGEMENT COURSE Johnny C. Ho, Turner College of Business, Columbus State University, Columbus, GA 31907 David Ang, School of Business, Auburn University Montgomery,

In this section, we will consider techniques for solving problems of this type.

Constrained optimisation roblems in economics typically involve maximising some quantity, such as utility or profit, subject to a constraint for example income. We shall therefore need techniques for solving

Fexible QoS Management of Web Services Orchestrations

Fexible QoS Management of Web Services Orchestrations Ajay Kattepur 1 1 Equipe ARLES, INRIA Paris-Rocquencourt, France. Collaborators: Albert Benveniste (INRIA), Claude Jard (INRIA / Uni. Nantes), Sidney

Statistics in Retail Finance. Chapter 6: Behavioural models

Statistics in Retail Finance 1 Overview > So far we have focussed mainly on application scorecards. In this chapter we shall look at behavioural models. We shall cover the following topics:- Behavioural