Agenda. Managing Uncertainty in the Supply Chain. The Economic Order Quantity. Classic inventory theory

Save this PDF as:

Size: px
Start display at page:

Download "Agenda. Managing Uncertainty in the Supply Chain. The Economic Order Quantity. Classic inventory theory"

Transcription

1 Agenda Managing Uncertainty in the Supply Chain TIØ485 Produkjons- og nettverksøkonomi Lecture 3 Classic Inventory models Economic Order Quantity (aka Economic Lot Size) The (s,s) Inventory Policy Managing uncertainty Risk Pooling Managing uncertainty with contracts Forecasting Methods Case The Newsboy Problem (The News Vendor Problem) The Economic Order Quantity Classic inventory theory Assumptions: Constant demand rate of D items per day Fixed order quantity Q Fixed cost K every time an order is placed Inventory carrying cost h (holding cost) per unit and day Lead time between placing an order an receipt of goods is zero Initial inventory is zero Planning horizon is infinite 3 4

2 Determining the EOQ The (s,s) Inventory Policy, Introduction Total inventory cost per cycle T Inventory Level htq K + Q Average cost per unit of time KD hq Q + Cost minimizing quantity * KD Q = T h Time This is known as the Economic Order Quantity (EOQ) Basic description of the (s,s) inventory policy: Whenever out inventory level drops below a certain level, say s, we order (or produce) in order to increase the inventory level to S. Such a policy is referred to as an (s,s) or min max policy. s is called the reorder point and S the order-up-to-level 5 6 The (s,s) Inventory Policy, Random Demand The (s,s) Policy with Random Demand Additional Assumptions: Daily demand is random and normally distributed Fixed cost K for placing an order Inventory holding cost h is charged per item per unit time If an order is placed, it arrives after the appropriate lead time All orders that can t be satisfied from stock are lost A service level is defined as the probability that the retailer is not stocking out during lead time Additional information needed to calculate the AVG = Average daily demand STD = Standard deviation of daily demand L x STD = cumulative variance over time L = Replenishment lead time α = Service level (implying that the probability of stocking out is - α) z= Safety factor associated with the service level (can be taken from tables, e. g. Table 3-, p. 6 in SKS) 7 8

3 Reorder Point s and Order-up-to-level S Reorder Point s and Order-up-to-level S Inventory Level Average demand during lead time Inventory position S L AVG Safety stock Q z STD L s z is taken from statistical table to ensure that the probability of a stock out during lead time is -α Reorder point s Lead time L Time L AVG + z STD L P{demand during lead time L AVG+z STD L} = α 9 Recall from EOQ * K AVG Q = h Order-up-to-level S S = Q+ s Expected level of inventory before receipt after receipt on average Q z STD L Q+ z STD L + z STD L Example 3-7 (SKS p. 6) I Example 3-7 (SKS p. 6) II Define the inventory policy for a TV distributor given the following assumptions: Whenever an order is placed the distributor incurs a fixed cost of $45 (independent of order size) The cost of the TV to the distributor is $5 and annual inventory holding cost is about 8% of the product cost Replenishment lead time is weeks Desired service level is 97% Demand data for the last months: Month Sales Sept. Oct. Nov. Dec. Jan. Feb. Mar. Apr. May June July Aug Average monthly demand is 9.7, standard deviation of monthly demand is Lead time is two weeks, transform monthly demand data into weekly demand data: Average monthly demand Average weekly demand = 4.3 Monthly standard deviation Standard deviation of weekly demand = 4.3 Table 3- (SKS, p. 6) determines the safety factor z for a 97% service level as.88

4 Example 3-7 (SKS p. 6) III Parameter Value Calculate Average demand during lead time Safety stock Reorder point Average weekly demand L AVG = = 89.6 L AVG + z STD L = = z STD L = = 85.9 Standard deviation of weekly demand 3.8 Example 3-7 (SKS p. 6) IV Weekly inventory holding cost.8 5 =.87 5 Order quantity Q K AVG Q = = = h.87 Order-up-to-level S = Q+ s= = 854 Average inventory level Q z STD L= = Introducing Variable Lead Times With a normally distributed delivery lead time and AVGL = Average lead time STDL = Standard deviation of lead time Reorder point s s = AVG AVGL + z AVGL STD + AVG STDL Risk pooling From SKS, and additional example Order-up-to-level S S = Q + AVG AVGL + z AVGL STD + AVG STDL 5 6

5 Risk Pooling Risk Pooling Consider these two systems: Warehouse One Supplier Warehouse Two Market One Market Two For the same service level, which system will require more inventory? Why? For the same total inventory level, which system will have better service? Why? What are the factors that affect these answers? Supplier Warehouse Market One Market Two 7 8 Portfolio thinking Example: Demand variance of portfolio of n regions with stochastic demand D n P= Di i= n n Var P= VAR (D i) + COV (D i,d j) i= i= j i If perfect correlation between the n regions (P=nD): VAR( P) = n VAR (D) The variation of total demand is reduced if the regions are negatively correlated. The more positively correlated, the higher the variation. Risk Pooling Example Compare the two systems: two products maintain 97% service level $6 order cost $.7 weekly holding cost $.5 transportation cost per unit in decentralized system, $. in centralized system week lead time The effect of centralized inventories comes from the fact that peaks does not appear simultaneuously in regions which are not perfctly correlated. 9

6 Risk Pooling Example Risk Pooling Example Week Prod A, Market Prod A, Market Prod B, Market Product B, Market Warehouse Product AVG STD CV Market A Market A Market B Market B Risk Pooling Example Warehouse Product AVG STD CV s S Avg. Inven. Market A % Dec. Market A Market B Market B Cent. A % Cent B % Risk Pooling: Important Observations Centralizing inventory control reduces both safety stock and average inventory level for the same service level. This works best for High coefficient of variation, which reduces required safety stock. Negatively correlated demand. Why? What other kinds of risk pooling will we see? 3 4

7 Safety stock Service level Overhead costs Customer lead time Transportation costs Managing uncertainty and the effect of Supply contracts 5 6 Contract design Example Pricing and volume discounts Minimum and maximum purchase quantities Delivery lead times Product or material quality Product return policies Will show: The effect of uncertainty on supply chain co-operation and risk sharing How to use contracts to manage risk When demand is uncertain simple co-ordination schemes based on single prices are not enough to ensure supply chain optimality From SKS Ch

8 SnowTime Sporting Goods Fashion items have short life cycles, high variety of competitors SnowTime Sporting Goods New designs are completed One production opportunity Based on past sales, knowledge of the industry, and economic conditions, the marketing department has a probabilistic forecast The forecast averages about 3,, but there is a chance that demand will be greater or less than this. Supply Chain Time Lines Jan Jan Jan Design Production Retailing Feb Sep Feb Sep Production 9 3 SnowTime Demand Scenarios SnowTime Costs Demand Scenarios Production cost per unit (C): $8 Selling price per unit (S): $5 Salvage value per unit (V): $ Fixed production cost (F): $, Q is production quantity, D demand Probability 3% 5% % 5% % 5% % Profit = Revenue - Variable Cost - Fixed Cost + Salvage Sales 3 3

9 SnowTime Best Solution What to Make? Find order quantity that maximizes weighted average profit. Question: Will this quantity be less than, equal to, or greater than average demand? Question: Will this quantity be less than, equal to, or greater than average demand? Average demand is 3, Look at marginal cost Vs. marginal profit if extra jacket sold, profit is 5-8 = 45 if not sold, cost is 8- = 6 So we will make less than average SnowTime Scenarios SnowTime Expected Profit Scenario One: Suppose you make, jackets and demand ends up being 3, jackets. Expected Profit Profit = 5(,) - 8(,) -, = $44, Scenario Two: Suppose you make, jackets and demand ends up being, jackets. Profit = 5(,) - 8(,) -, + () = $ 335, Profit $4, $3, $, $, $ 8 6 Order Quantity 35 36

10 SnowTime Expected Profit SnowTime Expected Profit $4, $3, Expected Profit $4, $3, Expected Profit Profit $, Profit $, $, $, $ $ Order Quantity Order Quantity SnowTime: Important Observations Tradeoff between ordering enough to meet demand and ordering too much Several quantities have the same average profit Average profit does not tell the whole story SnowTime Demand Scenarios Demand Scenarios Question: 9 and 6 units lead to about the same average profit, so which do we prefer? Probability 3% 5% % 5% % 5% % Sales 39 4

11 Probability of Outcomes Supply Contracts When we add the manufacturer Manufacturer Fixed Production Cost =$, % Variable Production Cost=$35 Probability 8% 6% 4% % Q=9 Q=6 Wholesale Price =$8 Selling Price=$5 Salvage Value=$ % -3-3 Cost 5 Manufacturer Manufacturer DC Retail DC No fixed cost for distributor (otherwise equal to previous example) Stores 4 4 Demand Scenarios Distributor Expected Profit Demand Scenarios 5 Expected Profit Probability 3% 5% % 5% % 5% % Order Quantity Sales 43 44

12 Distributor Expected Profit Supply Contracts (cont.) Expected Profit Distributor optimal order quantity is, units Distributor expected profit is $47, Manufacturer profit is $44, Supply Chain Profit is $9, Order Quantity IS there anything that the distributor and manufacturer can do to increase the profit of both? Supply Contracts Retailer Profit (Buy Back=$55) Fixed Production Cost =$, 6, Variable Production Cost=$35 5, Wholesale Price =$8 Manufacturer Manufacturer DC Retail DC Selling Price=$5 Salvage Value=$ Stores Retailer Profit 4, 3,,, Order Quantity

13 Retailer Profit (Buy Back=$55) Manufacturer Profit (Buy Back=$55) Retailer Profit 6, 5, 4, 3,,, 6 7 $53,8 8 9 Order Quantity Manufacturer Profit 6, 5, 4, 3,,, Production Quantity Manufacturer Profit (Buy Back=$55) Supply Contracts Manufacturer Profit 6, 5, 4, 3,,, 6 7 $47,9 8 9 Production Quantity Fixed Production Cost =$, Variable Production Cost=$35 Wholesale Price =$8 Manufacturer Manufacturer DC Retail DC Selling Price=$5 Salvage Value=$ Stores 5 5

14 Retailer Profit (Wholesale Price $7, RS 5%) 6, 5, 4, 3,,, Retailer Profit Order Quantity 53 Retailer Profit (Wholesale Price $7, RS 5%) 6, 5, 4, 3,,, $54,35 Retailer Profit Manufacturer Profit (Wholesale Price $7, RS 5%) 7, 6, 5, 4, 3,,, 55 Order Quantity 54 Manufacturer Profit Production Quantity Manufacturer Profit (Wholesale Price $7, RS 5%) 7, 6, 5, 4, 3,,, $48,375 Manufacturer Profit Production Quantity 56

15 Supply Contracts Supply Contracts Strategy Retailer Manufacturer Total Sequential Optimization 47,7 44, 9,7 Buyback 53,8 47,9 985,7 Revenue Sharing 54,35 48, ,7 Fixed Production Cost =$, Variable Production Cost=$35 Wholesale Price =$8 Selling Price=$5 Salvage Value=$ Manufacturer Manufacturer DC Retail DC Stores Supply Chain Profit Supply Chain Profit Supply Chain Profit,,,, 8, 6, 4,, Production Quantity Supply Chain Profit,,,, 8, 6, 4,, 6 7 $,4, Production Quantity

16 Supply Contracts Supply Contracts: Key Insights Strategy Retailer Manufacturer Total Sequential Optimization 47,7 44, 9,7 Buyback 53,8 47,9 985,7 Revenue Sharing 54,35 48, ,7 Global Optimization,4,5 Effective supply contracts allow supply chain partners to replace sequential optimization by global optimization Buy Back and Revenue Sharing contracts achieve this objective through risk sharing 6 6 Other Contracts Quantity Flexibility Contracts Supplier provides full refund for returned items as long as the number of returns is no larger than a certain quantity Sales Rebate Contracts Supplier provides direct incentive for the retailer to increase sales by means of a rebate paid by the supplier for any item sold above a certain quantity Forecasting (Hillier & Lieberman Ch, SKS Ch 3) 63 64

17 Forecasting Forecasting Methods Basic Principles of Forecasting: The forecast is always wrong The longer the forecast horizon, the worse the forecast Aggregate forecasts are more accurate Forecasting methods can be split into four general categories: Judgment methods, Market research methods, Causal methods, and Time-series methods Judgment Methods Market Research Methods Panels of experts A group of experts is assembled in order to agree upon a forecast. It is assumed that sharing information and communicating allows for reaching a consensus. Delphi method A group of experts makes independent forecasts. The results of all forecasts is presented to the experts and they are asked to refine their forecasts based on the now available knowledge. The process is repeated a specified number of times or until the expert s forecast are within a certain margin. Market testing Groups of potential customers are assembled and tested for their response to products. Their response is extrapolated to the whole market and used for estimating demand. When new models are launched in the automotive industry, potential customers are assembled to check their response to the planned design long before production starts. Market survey Customer response is estimated by gathering data from various potential customer groups. Data is usually collected through interviews, telephone-based surveys, written surveys, etc

18 Causal Methods Time-Series Methods Causal methods try to generate forecast based on data other than the data being predicted. Using a causal forecasting method, you may try to forecast sales for the next three months based on inflation GNP advertising/marketing activities weather etc. Time-series based methods use a variety of past data to estimate future data. Examples of time-series methods: Last-Value forecasting Averaging forecast Moving average Exponential smoothing Methods for data with trends Methods for seasonal data etc Eample of time series Last-Value forecasting Volume Demand F = t+ xt Variance is large because of small sample size ( data point) Worth considering if Constant-level is not likely World is changing so fast that most of the previous observations are irrelevant Often called the naive method (by statisticians) May be the only relevant value (under rapid changes) Month 7 7

19 Averaging Forecasting t+ t xi = t i= Excellent if the process is stable Normally limited to young processes F Moving Average t+ Very simple forecasting method F t xi = n i= t n+ Calculating the average of previous demand over a given number of time periods Every past demand point is weighted equally Determining the number of time periods n to calculate the average demand is very important Only recent history and multiple observations Exponential Smoothing F = α x + α F ( ) ( ) t+ t t F = αx + α α x t+ t t i α- smoothing constant (between and i ) Forecast is a weighted average of the previous forecast and the last demand More recent value receive more weight than past values Similar to the Moving Average Forecast, but tracks changes in the process faster Lags behind a continuous trend Choice of the smoothing constant α (weight of the last demand) is important i 75 76

20 Methods for Data with Trends Example Regression Analysis, tries to fit a straight line into data points. Thus, it s identifying linear trends. Holt s Method, combines the concepts of exponential smoothing with the ability to capture linear trends Box-Jenkins Box-Jenkins method Box-Jenkins is a methodology for identifying, estimating, and forecasting ARIMA models Requires a large amount of data (historical observations of a variable) Alternative name: ARIMA method Iterative process 79 8

21 ARIMA models ARIMA components Describes a stochastic process or a model of one Explains the variable Y based only on the history of Y No other explanatory variables Not derived from economic theory Stands for "autoregressive integrated moving- average" Made up of sums of autoregressive and moving-average components An ARIMA process is stationary mean, variance and autocorrelation structure do not change over time 8 AR (Autoregressive) Process that can be described by a weighted sum of its previous values and a white noise error AR(): AR(p): t p t p t p= MA (Moving Average) Process that can be described by a constant plus a moving average of the current and past error terms MA(): MA(q): Y Y = αy + θ t t t n = α Y + θ Y = µ + βθ t Y µ βθ t q t q q= n = + t 8 ARIMA components () White noise error ARMA (p, q) May be non-stationary (integrated) Non- constant mean, variance or covariance The d-differences of a time series that are integrated of order d is stationary - differences of Y: Y t Y t- ARIMA (p, d, q) p AR terms, integrated of order d and q MA terms Uncorrelated random error term with zero mean and constant variance Often assumed to be normally distributed 83 84

22 The Box-Jenkins Methodology. Parameter estimation. Identification of the model (Choosing p, d and q). Parameter estimation of the chosen model Estimate the value of the p AR and the q MA parameters Simple problems: use least squares method Otherwise: nonlinear (in parameter) estimation methods Done by several statistical packages (SPSS, MiniTab, S- Plus, EasyReg,..) 3. Diagnostic checking (Are the residuals white noise?) If no go to step If yes go to step 4 4. Forecasting Diagnostic checking 4. Forecasting Test if the estimated model fits the historical data In particular: test if the residuals are white noise If not the model is not appropriate and a new one must be estimated If they are the model may be good and used for forecasting Use the model to forecast new values Can be used as a test of the goodness of the model Out-of-sample tests Cross validation 87 88

23 Conclusions Requires no economic theory Can not make use of any economic theory + Can not misuse economic theory/ make false assumptions Usually provides accurate forecasts Requires a large amount of data Only needs data for one variable Regretion analysys Assume that a company s demand fucntion ca be written on the form Y = A+ Bx + Bx + B3I + B4Pr Y er dependent variable x, x, I, Pr Are examples on independent variables. We wish to estimate Based on historical data AB,, B, B, B Population regression curve Sales Advertising Line shows the value of E(Y X) Y = A+ B x The line is fitted so that squared deviation is minimized 9 9

24 Explained variance and the constant of determination Variance in dependent variable : n ( Y Y) i= and i Y Y = ( Y Yˆ) + ( Yˆ Y) i i i i Explained variance : n i= ( Yˆ Y) i i= n Explained variance and the constant of determination Variation not explained by regression R = Total variation R = n i= ( Y Yˆ ) i i ( Y Y) i i Unexplained variance: n i= ( Y Yˆ ) i i Methods for Seasonal Data Seasonal data has to be accounted for.. Calculate seasonal factor average for the period Seasonal factor = overall average. Calculate seasonally adjusted values actual value Seasonal adjusted value = seasonal factor 3. Select time-series forecasting method 4. Apply this method to the seasonally adjusted data 5. Multiply this forecast with the seasonal factor in order to get the next actual value 95 Example on Forecasting Seasonal Demand (HL p. 9) I CCW's Average Daily Call Volume Year Quarter Call Volume

25 Example on Forecasting Seasonal Demand (HL p. 9) II Example on Forecasting Seasonal Demand (HL p. 9) III Calculating seasonal factors Quarter 3 4 Three-Year Average Total=37 37 Average= = Seasonal Factor = = = = Year Quarter Seasonal Factor Actual Call Volume Seasonally Adjusted Call Volume The Newsboy Problem Short problem description: Stochastic inventory models case: The newsboy problem You are selling newspapers. Each morning you go and buy the amount of newspapers you think you are going to sell in the course of the day. Unfortunately, you don t know exactly how many newspapers you are going to sell, your demand is uncertain. In addition, you have no chance of buying additional newspapers if you realize you face a higher demand than what you anticipated. So all demand you cannot satisfy from stock is lost. In the other case you have bought too many newspapers you will not be able to sell them later, because no one will be interested in yesterday s news. (But you can sell the papers with a loss as recycling material.) How many newspapers do you buy every morning? 99

26 The Newsboy Problem, Formulation by Rudi and Pyke We change notation here: W - cost of buying a newspaper R - selling price of the newspaper (R > W) S - return price (salvage value) of a newspaper (S < W) Q - number of newspapers bought in the morning D - stochastic demand In addition, we will need the following definition: y if y y + = otherwise The Newsboy Problem, Reformulation by Rudi and Pyke Define expected profit as function of Q + Π r ( Q) = E Rmin( D, Q) + S( Q D) WQ With min( D, Q) = D ( D Q ) + and Q= D ( D Q) + ( Q D) + + we reformulate Π r (Q) + + Π r ( Q) = ( R W) ED E ( R W)( D Q) + ( W S)( Q D) and call G r (Q) the cost of uncertainty Gr ( Q) The Newsboy Problem, Reformulation by Rudi and Pyke The first term ( R W)( D Q ) + represents expected number of units short times the opportunity cost per unit short. Thus, define the unit underage cost C u as: C = R W The second term ( W S)( Q D ) + represents expected number of left over units times the opportunity cost per left over unit. Thus, define the unit overage cost C o as: C = W S Rewrite G r (Q) as u o + + Gr( Q) = E Cu( D Q) + Co( Q D) 3 The Newsboy Problem, Reformulation by Rudi and Pyke In order to determine the order quantity that results in the highest expected profit, derive G r (Q) and find Q such that the derivative equals zero. + + Gr( Q) = E Cu( D Q) + Co( Q D) Derivative of the first term of G r (Q): C Pr( D> Q) u Derivative of the second term of G r (Q): C Pr( Q> D) o The derivative of G r (Q): G ( Q) = C Pr( D> Q) + C Pr( Q> D) r u o 4

27 The Newsboy Problem, Reformulation by Rudi and Pyke With Pr(D>Q)= Pr(D<Q), we can rewrite G r (Q) as [ ] G ( Q) = C Pr( D< Q) + C Pr( D< Q) r u o Equating this derivative to zero, we get the following Newsboy optimality condition Pr( D Cu < Q ) = C + C u This allows to find the optimal order quantity Q* o Introducing the Concept of Real Options into the Newsboy Problem The Newsboy problem is now extended by the concept of Real Options. It becomes more clear if you do not think of newspapers now, but of this year s favorite Christmas present, this summer s most popular fashion item, etc. Ahead of the selling season, the retailer buys q call options at unit cost c. Each of these options gives the retailer the right to buy one unit of the finished product at exercise price x after observing customer demand. The idea is for the manufacturer to induce the retailer to buy more finished products thus being able to capture more sales by sharing some of the risk of demand uncertainty with the retailer. 5 6 Introducing the Concept of Real Options into the Newsboy Problem Expected profit π = * r ( q ) ( R x)min( D, q) cq Reformulate π r (q) according to the regular Newsboy problem + + π r ( q) = ( R x c) ED E ( R x c)( D q) + c( q D) gr ( q) Express expected opportunity cost g r (q) with overage and underage cost + + gr( q) = E k u( D q) + k o( q D) What can you say about the manufacturers profit Without real options? With real options? Optimality Condition ku Pr( D< q) = k + k u o 7 8

Inventory Management and Risk Pooling. Xiaohong Pang Automation Department Shanghai Jiaotong University

Inventory Management and Risk Pooling. Xiaohong Pang Automation Department Shanghai Jiaotong University Inventory Management and Risk Pooling Xiaohong Pang Automation Department Shanghai Jiaotong University Key Insights from this Model The optimal order quantity is not necessarily equal to average forecast

More information

Contracts. David Simchi-Levi. Professor of Engineering Systems

Contracts. David Simchi-Levi. Professor of Engineering Systems Introduction to Stochastic Inventory Models and Supply Contracts David Simchi-Levi Professor of Engineering Systems Massachusetts Institute of Technology Introduction Outline of the Presentation The Effect

More information

Inventory Management and Risk Pooling

Inventory Management and Risk Pooling CHAPTER 3 Inventory Management and Risk Pooling CASE JAM Electronics: Service Level Crisis JAM Electronics is a Korean manufacturer of products such as industrial relays. The company has five Far Eastern

More information

Agenda. TPPE37 Manufacturing Control. A typical production process. The Planning Hierarchy. Primary material flow

Agenda. TPPE37 Manufacturing Control. A typical production process. The Planning Hierarchy. Primary material flow TPPE37 Manufacturing Control Agenda Lecture 2 Inventory Management 1. Inventory System Defined 2. Inventory Costs 3. Inventory classification 4. Economic order quantity model 5. Newsboy problem 6. Reorder

More information

Demand forecasting & Aggregate planning in a Supply chain. Session Speaker Prof.P.S.Satish

Demand forecasting & Aggregate planning in a Supply chain. Session Speaker Prof.P.S.Satish Demand forecasting & Aggregate planning in a Supply chain Session Speaker Prof.P.S.Satish 1 Introduction PEMP-EMM2506 Forecasting provides an estimate of future demand Factors that influence demand and

More information

MGT 267 PROJECT. Forecasting the United States Retail Sales of the Pharmacies and Drug Stores. Done by: Shunwei Wang & Mohammad Zainal

MGT 267 PROJECT. Forecasting the United States Retail Sales of the Pharmacies and Drug Stores. Done by: Shunwei Wang & Mohammad Zainal MGT 267 PROJECT Forecasting the United States Retail Sales of the Pharmacies and Drug Stores Done by: Shunwei Wang & Mohammad Zainal Dec. 2002 The retail sale (Million) ABSTRACT The present study aims

More information

Supply Chain Management: Inventory Management

Supply Chain Management: Inventory Management Supply Chain Management: Inventory Management Donglei Du Faculty of Business Administration, University of New Brunswick, NB Canada Fredericton E3B 9Y2 (ddu@umbc.edu) Du (UNB) SCM 1 / 83 Table of contents

More information

Industry Environment and Concepts for Forecasting 1

Industry Environment and Concepts for Forecasting 1 Table of Contents Industry Environment and Concepts for Forecasting 1 Forecasting Methods Overview...2 Multilevel Forecasting...3 Demand Forecasting...4 Integrating Information...5 Simplifying the Forecast...6

More information

Supply Chain Management: Risk pooling

Supply Chain Management: Risk pooling Supply Chain Management: Risk pooling Donglei Du (ddu@unb.edu) Faculty of Business Administration, University of New Brunswick, NB Canada Fredericton E3B 9Y2 Donglei Du (UNB) SCM 1 / 24 Table of contents

More information

Package SCperf. February 19, 2015

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

More information

MATERIALS MANAGEMENT. Module 9 July 22, 2014

MATERIALS MANAGEMENT. Module 9 July 22, 2014 MATERIALS MANAGEMENT Module 9 July 22, 2014 Inventories and their Management Inventories =? New Car Inventory Sitting in Parking Lots Types of Inventory 1. Materials A. Raw material B. WIP C. Finished

More information

Forecasting in supply chains

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

More information

Section A. Index. Section A. Planning, Budgeting and Forecasting Section A.2 Forecasting techniques... 1. Page 1 of 11. EduPristine CMA - Part I

Section A. Index. Section A. Planning, Budgeting and Forecasting Section A.2 Forecasting techniques... 1. Page 1 of 11. EduPristine CMA - Part I Index Section A. Planning, Budgeting and Forecasting Section A.2 Forecasting techniques... 1 EduPristine CMA - Part I Page 1 of 11 Section A. Planning, Budgeting and Forecasting Section A.2 Forecasting

More information

Demand Forecasting LEARNING OBJECTIVES IEEM 517. 1. Understand commonly used forecasting techniques. 2. Learn to evaluate forecasts

Demand Forecasting LEARNING OBJECTIVES IEEM 517. 1. Understand commonly used forecasting techniques. 2. Learn to evaluate forecasts IEEM 57 Demand Forecasting LEARNING OBJECTIVES. Understand commonly used forecasting techniques. Learn to evaluate forecasts 3. Learn to choose appropriate forecasting techniques CONTENTS Motivation Forecast

More information

Equations for Inventory Management

Equations for Inventory Management Equations for Inventory Management Chapter 1 Stocks and inventories Empirical observation for the amount of stock held in a number of locations: N 2 AS(N 2 ) = AS(N 1 ) N 1 where: N 2 = number of planned

More information

INVENTORY MANAGEMENT. 1. Raw Materials (including component parts) 2. Work-In-Process 3. Maintenance/Repair/Operating Supply (MRO) 4.

INVENTORY MANAGEMENT. 1. Raw Materials (including component parts) 2. Work-In-Process 3. Maintenance/Repair/Operating Supply (MRO) 4. INVENTORY MANAGEMENT Inventory is a stock of materials and products used to facilitate production or to satisfy customer demand. Types of inventory include: 1. Raw Materials (including component parts)

More information

Production Planning. Chapter 4 Forecasting. Overview. Overview. Chapter 04 Forecasting 1. 7 Steps to a Forecast. What is forecasting?

Production Planning. Chapter 4 Forecasting. Overview. Overview. Chapter 04 Forecasting 1. 7 Steps to a Forecast. What is forecasting? Chapter 4 Forecasting Production Planning MRP Purchasing Sales Forecast Aggregate Planning Master Production Schedule Production Scheduling Production What is forecasting? Types of forecasts 7 steps of

More information

Module 6: Introduction to Time Series Forecasting

Module 6: Introduction to Time Series Forecasting Using Statistical Data to Make Decisions Module 6: Introduction to Time Series Forecasting Titus Awokuse and Tom Ilvento, University of Delaware, College of Agriculture and Natural Resources, Food and

More information

Glossary of Inventory Management Terms

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

More information

C H A P T E R Forecasting statistical fore- casting methods judgmental forecasting methods 27-1

C H A P T E R Forecasting statistical fore- casting methods judgmental forecasting methods 27-1 27 C H A P T E R Forecasting H ow much will the economy grow over the next year? Where is the stock market headed? What about interest rates? How will consumer tastes be changing? What will be the hot

More information

Supply Chain Analysis Tools

Supply Chain Analysis Tools Supply Chain Analysis Tools MS&E 262 Supply Chain Management April 14, 2004 Inventory/Service Trade-off Curve Motivation High Inventory Low Poor Service Good Analytical tools help lower the curve! 1 Outline

More information

Demand Forecasting When a product is produced for a market, the demand occurs in the future. The production planning cannot be accomplished unless

Demand Forecasting When a product is produced for a market, the demand occurs in the future. The production planning cannot be accomplished unless Demand Forecasting When a product is produced for a market, the demand occurs in the future. The production planning cannot be accomplished unless the volume of the demand known. The success of the business

More information

Chapter 25 Specifying Forecasting Models

Chapter 25 Specifying Forecasting Models Chapter 25 Specifying Forecasting Models Chapter Table of Contents SERIES DIAGNOSTICS...1281 MODELS TO FIT WINDOW...1283 AUTOMATIC MODEL SELECTION...1285 SMOOTHING MODEL SPECIFICATION WINDOW...1287 ARIMA

More information

Ch.3 Demand Forecasting.

Ch.3 Demand Forecasting. Part 3 : Acquisition & Production Support. Ch.3 Demand Forecasting. Edited by Dr. Seung Hyun Lee (Ph.D., CPL) IEMS Research Center, E-mail : lkangsan@iems.co.kr Demand Forecasting. Definition. An estimate

More information

Use of Statistical Forecasting Methods to Improve Demand Planning

Use of Statistical Forecasting Methods to Improve Demand Planning Use of Statistical Forecasting Methods to Improve Demand Planning Talk given at the Swiss Days of Statistics 2004 Aarau, November 18th, 2004 Marcel Baumgartner marcel.baumgartner@nestle.com Nestec 1800

More information

Sourcing and Contracts Chapter 13

Sourcing and Contracts Chapter 13 Sourcing and Contracts Chapter 13 1 Outline The Role of Sourcing in a Supply Chain Supplier Scoring and Assessment Supplier Selection and Contracts Design Collaboration The Procurement Process Sourcing

More information

Time Series Analysis and Forecasting

Time Series Analysis and Forecasting Time Series Analysis and Forecasting Math 667 Al Nosedal Department of Mathematics Indiana University of Pennsylvania Time Series Analysis and Forecasting p. 1/11 Introduction Many decision-making applications

More information

Exponential Smoothing with Trend. As we move toward medium-range forecasts, trend becomes more important.

Exponential Smoothing with Trend. As we move toward medium-range forecasts, trend becomes more important. Exponential Smoothing with Trend As we move toward medium-range forecasts, trend becomes more important. Incorporating a trend component into exponentially smoothed forecasts is called double exponential

More information

Analysis of Various Forecasting Approaches for Linear Supply Chains based on Different Demand Data Transformations

Analysis of Various Forecasting Approaches for Linear Supply Chains based on Different Demand Data Transformations Institute of Information Systems University of Bern Working Paper No 196 source: https://doi.org/10.7892/boris.58047 downloaded: 16.11.2015 Analysis of Various Forecasting Approaches for Linear Supply

More information

Ud Understanding di inventory issues

Ud Understanding di inventory issues Lecture 10: Inventory Management Ud Understanding di inventory issues Definition of inventory Types of inventory Functions of inventory Costs of holding inventory Introduction to inventory management Economic

More information

RELEVANT TO ACCA QUALIFICATION PAPER P3. Studying Paper P3? Performance objectives 7, 8 and 9 are relevant to this exam

RELEVANT TO ACCA QUALIFICATION PAPER P3. Studying Paper P3? Performance objectives 7, 8 and 9 are relevant to this exam RELEVANT TO ACCA QUALIFICATION PAPER P3 Studying Paper P3? Performance objectives 7, 8 and 9 are relevant to this exam Business forecasting and strategic planning Quantitative data has always been supplied

More information

Replenishment: What is it exactly and why is it important?

Replenishment: What is it exactly and why is it important? Replenishment: What is it exactly and why is it important? Dictionaries define Replenishment as filling again by supplying what has been used up. This definition does not adequately address the business

More information

Forecasting Methods. What is forecasting? Why is forecasting important? How can we evaluate a future demand? How do we make mistakes?

Forecasting Methods. What is forecasting? Why is forecasting important? How can we evaluate a future demand? How do we make mistakes? Forecasting Methods What is forecasting? Why is forecasting important? How can we evaluate a future demand? How do we make mistakes? Prod - Forecasting Methods Contents. FRAMEWORK OF PLANNING DECISIONS....

More information

PITFALLS IN TIME SERIES ANALYSIS. Cliff Hurvich Stern School, NYU

PITFALLS IN TIME SERIES ANALYSIS. Cliff Hurvich Stern School, NYU PITFALLS IN TIME SERIES ANALYSIS Cliff Hurvich Stern School, NYU The t -Test If x 1,..., x n are independent and identically distributed with mean 0, and n is not too small, then t = x 0 s n has a standard

More information

TIME SERIES ANALYSIS

TIME SERIES ANALYSIS TIME SERIES ANALYSIS L.M. BHAR AND V.K.SHARMA Indian Agricultural Statistics Research Institute Library Avenue, New Delhi-0 02 lmb@iasri.res.in. Introduction Time series (TS) data refers to observations

More information

Sales forecasting # 2

Sales forecasting # 2 Sales forecasting # 2 Arthur Charpentier arthur.charpentier@univ-rennes1.fr 1 Agenda Qualitative and quantitative methods, a very general introduction Series decomposition Short versus long term forecasting

More information

Effective Replenishment Parameters By Jon Schreibfeder

Effective Replenishment Parameters By Jon Schreibfeder WINNING STRATEGIES FOR THE DISTRIBUTION INDUSTRY Effective Replenishment Parameters By Jon Schreibfeder >> Compliments of Microsoft Business Solutions Effective Replenishment Parameters By Jon Schreibfeder

More information

Chapter 7. 7.1 Introduction. Distribution Strategies. Traditional Warehousing. 7.3. Intermediate Inventory Storage Point Strategies

Chapter 7. 7.1 Introduction. Distribution Strategies. Traditional Warehousing. 7.3. Intermediate Inventory Storage Point Strategies 7.1 Introduction McGraw-Hill/Irwin Chapter 7 Distribution Strategies Copyright 2008 by The McGraw-Hill Companies, Inc. All rights reserved. Focus on the distribution function. Various possible distribution

More information

20 Forecasting. e-text Main Menu Textbook Table of Contents

20 Forecasting. e-text Main Menu Textbook Table of Contents 20 Forecasting How much will the economy grow over the next year? Where is the stock market headed? What about interest rates? How will consumer tastes be changing? What will be the hot new products? Forecasters

More information

Time Series Analysis

Time Series Analysis Time Series Analysis Forecasting with ARIMA models Andrés M. Alonso Carolina García-Martos Universidad Carlos III de Madrid Universidad Politécnica de Madrid June July, 2012 Alonso and García-Martos (UC3M-UPM)

More information

Effective Replenishment Parameters. By Jon Schreibfeder EIM. Effective Inventory Management, Inc.

Effective Replenishment Parameters. By Jon Schreibfeder EIM. Effective Inventory Management, Inc. Effective Replenishment Parameters By Jon Schreibfeder EIM Effective Inventory Management, Inc. This report is the fourth in a series of white papers designed to help forward-thinking distributors increase

More information

A Primer on Forecasting Business Performance

A Primer on Forecasting Business Performance A Primer on Forecasting Business Performance There are two common approaches to forecasting: qualitative and quantitative. Qualitative forecasting methods are important when historical data is not available.

More information

An Analysis of Inventory Management of T-Shirt at Mahanagari Bandung Pisan

An Analysis of Inventory Management of T-Shirt at Mahanagari Bandung Pisan www.sbm.itb.ac.id/ajtm The Asian Journal of Technology Management Vol. 3 No. 2 (2010) 92-109 An Analysis of Inventory Management of T-Shirt at Mahanagari Bandung Pisan Togar M. Simatupang 1 *, Nidia Jernih

More information

Chapter 5 Estimating Demand Functions

Chapter 5 Estimating Demand Functions Chapter 5 Estimating Demand Functions 1 Why do you need statistics and regression analysis? Ability to read market research papers Analyze your own data in a simple way Assist you in pricing and marketing

More information

Probabilistic Forecasting of Medium-Term Electricity Demand: A Comparison of Time Series Models

Probabilistic Forecasting of Medium-Term Electricity Demand: A Comparison of Time Series Models Fakultät IV Department Mathematik Probabilistic of Medium-Term Electricity Demand: A Comparison of Time Series Kevin Berk and Alfred Müller SPA 2015, Oxford July 2015 Load forecasting Probabilistic forecasting

More information

Inventory management in distribution systems case of an Indian FMCG company

Inventory management in distribution systems case of an Indian FMCG company Asia Pacific Management Review (2004) 9(1), 1-22 Inventory management in distribution systems case of an Indian FMCG company Subrata Mitra and A. K. Chatterjee (received April 2003; revision received October

More information

講 師 : 周 世 玉 Shihyu Chou

講 師 : 周 世 玉 Shihyu Chou 講 師 : 周 世 玉 Shihyu Chou Logistics involves the following activities: sourcing and purchasing inputs, managing inventory, maintaining warehouses, and arranging transportation and delivery. There are three

More information

Chapter 7 The ARIMA Procedure. Chapter Table of Contents

Chapter 7 The ARIMA Procedure. Chapter Table of Contents Chapter 7 Chapter Table of Contents OVERVIEW...193 GETTING STARTED...194 TheThreeStagesofARIMAModeling...194 IdentificationStage...194 Estimation and Diagnostic Checking Stage...... 200 Forecasting Stage...205

More information

Tema 4: Supply Chain Management

Tema 4: Supply Chain Management Tema 4: Supply Chain Management Logistics 1 Supplier Manufacturer Warehouse Retailer Customer Tema basado en: Supply Chain Management: Strategy, Planning, and Operations, Sunil Copra and Peter Meindl (Editors).

More information

Rob J Hyndman. Forecasting using. 11. Dynamic regression OTexts.com/fpp/9/1/ Forecasting using R 1

Rob J Hyndman. Forecasting using. 11. Dynamic regression OTexts.com/fpp/9/1/ Forecasting using R 1 Rob J Hyndman Forecasting using 11. Dynamic regression OTexts.com/fpp/9/1/ Forecasting using R 1 Outline 1 Regression with ARIMA errors 2 Example: Japanese cars 3 Using Fourier terms for seasonality 4

More information

5. Multiple regression

5. Multiple regression 5. Multiple regression QBUS6840 Predictive Analytics https://www.otexts.org/fpp/5 QBUS6840 Predictive Analytics 5. Multiple regression 2/39 Outline Introduction to multiple linear regression Some useful

More information

11.3 BREAK-EVEN ANALYSIS. Fixed and Variable Costs

11.3 BREAK-EVEN ANALYSIS. Fixed and Variable Costs 385 356 PART FOUR Capital Budgeting a large number of NPV estimates that we summarize by calculating the average value and some measure of how spread out the different possibilities are. For example, it

More information

Course Supply Chain Management: Inventory Management. Inventories cost money: Reasons for inventory. Types of inventory

Course Supply Chain Management: Inventory Management. Inventories cost money: Reasons for inventory. Types of inventory Inventories cost money: Inventories are to be avoided at all cost? Course Supply Chain Management: Or Inventory Management Inventories can be useful? Chapter 10 Marjan van den Akker What are reasons for

More information

Inventory Management - A Teaching Note

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

More information

Time Series - ARIMA Models. Instructor: G. William Schwert

Time Series - ARIMA Models. Instructor: G. William Schwert APS 425 Fall 25 Time Series : ARIMA Models Instructor: G. William Schwert 585-275-247 schwert@schwert.ssb.rochester.edu Topics Typical time series plot Pattern recognition in auto and partial autocorrelations

More information

LECTURE - 2 YIELD MANAGEMENT

LECTURE - 2 YIELD MANAGEMENT LECTURE - 2 YIELD MANAGEMENT Learning objective To demonstrate the applicability of yield management in services 8.5 Yield Management or Revenue Management Yield management is applied by service organizations

More information

Inventory Models for Special Cases: A & C Items and Challenges

Inventory Models for Special Cases: A & C Items and Challenges CTL.SC1x -Supply Chain & Logistics Fundamentals Inventory Models for Special Cases: A & C Items and Challenges MIT Center for Transportation & Logistics Inventory Management by Segment A Items B Items

More information

Strategic Framework to Analyze Supply Chains

Strategic Framework to Analyze Supply Chains Strategic Framework to Analyze Supply Chains 1 Andy Guo A Strategic Framework for Supply Chain Design, Planning, and Operation Part I: Understand the supply chain Part II: Supply chain performance Part

More information

Time Series Analysis and Forecasting Methods for Temporal Mining of Interlinked Documents

Time Series Analysis and Forecasting Methods for Temporal Mining of Interlinked Documents Time Series Analysis and Forecasting Methods for Temporal Mining of Interlinked Documents Prasanna Desikan and Jaideep Srivastava Department of Computer Science University of Minnesota. @cs.umn.edu

More information

Integrated Resource Plan

Integrated Resource Plan Integrated Resource Plan March 19, 2004 PREPARED FOR KAUA I ISLAND UTILITY COOPERATIVE LCG Consulting 4962 El Camino Real, Suite 112 Los Altos, CA 94022 650-962-9670 1 IRP 1 ELECTRIC LOAD FORECASTING 1.1

More information

Logistics Management Inventory Cycle Inventory. Özgür Kabak, Ph.D.

Logistics Management Inventory Cycle Inventory. Özgür Kabak, Ph.D. Logistics Management Inventory Cycle Inventory Özgür Kabak, Ph.D. Role of Inventory in the Supply Chain Improve Matching of Supply and Demand Improved Forecasting Reduce Material Flow Time Reduce Waiting

More information

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 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

More information

Sensex Realized Volatility Index

Sensex Realized Volatility Index Sensex Realized Volatility Index Introduction: Volatility modelling has traditionally relied on complex econometric procedures in order to accommodate the inherent latent character of volatility. Realized

More information

Luciano Rispoli Department of Economics, Mathematics and Statistics Birkbeck College (University of London)

Luciano Rispoli Department of Economics, Mathematics and Statistics Birkbeck College (University of London) Luciano Rispoli Department of Economics, Mathematics and Statistics Birkbeck College (University of London) 1 Forecasting: definition Forecasting is the process of making statements about events whose

More information

Chapter 4: Vector Autoregressive Models

Chapter 4: Vector Autoregressive Models Chapter 4: Vector Autoregressive Models 1 Contents: Lehrstuhl für Department Empirische of Wirtschaftsforschung Empirical Research and und Econometrics Ökonometrie IV.1 Vector Autoregressive Models (VAR)...

More information

Working Capital Management

Working Capital Management Working Capital Management Gitman and Hennessey, Chapter 14 Spring 2004 14.1 Net Working Capital Fundamentals In 2002, current assets accounted for 31.7% of non-financial Canadian corporations total assets.

More information

Working Capital Management

Working Capital Management Working Capital Management Gitman and Hennessey, Chapter 14 Spring 2004 14.1 Net Working Capital Fundamentals In 2002, current assets accounted for 31.7% of non-financial Canadian corporations total assets.

More information

Welcome! First Steps to Achieving Effective Inventory Management

Welcome! First Steps to Achieving Effective Inventory Management Welcome! First Steps to Achieving Effective Inventory Management Tuesday, January 25, 2011 10 a.m. 11 a.m. EST Housekeeping Items This meeting will run for approximately one hour. Submit all questions

More information

I. Introduction. II. Background. KEY WORDS: Time series forecasting, Structural Models, CPS

I. Introduction. II. Background. KEY WORDS: Time series forecasting, Structural Models, CPS Predicting the National Unemployment Rate that the "Old" CPS Would Have Produced Richard Tiller and Michael Welch, Bureau of Labor Statistics Richard Tiller, Bureau of Labor Statistics, Room 4985, 2 Mass.

More information

OBJECTIVE ASSESSMENT OF FORECASTING ASSIGNMENTS USING SOME FUNCTION OF PREDICTION ERRORS

OBJECTIVE ASSESSMENT OF FORECASTING ASSIGNMENTS USING SOME FUNCTION OF PREDICTION ERRORS OBJECTIVE ASSESSMENT OF FORECASTING ASSIGNMENTS USING SOME FUNCTION OF PREDICTION ERRORS CLARKE, Stephen R. Swinburne University of Technology Australia One way of examining forecasting methods via assignments

More information

Univariate and Multivariate Methods PEARSON. Addison Wesley

Univariate and Multivariate Methods PEARSON. Addison Wesley Time Series Analysis Univariate and Multivariate Methods SECOND EDITION William W. S. Wei Department of Statistics The Fox School of Business and Management Temple University PEARSON Addison Wesley Boston

More information

The Newsvendor Model

The Newsvendor Model The Newsvendor Model Exerpted form The Operations Quadrangle: Business Process Fundamentals Dan Adelman Dawn Barnes-Schuster Don Eisenstein The University of Chicago Graduate School of Business Version

More information

SPSS TRAINING SESSION 3 ADVANCED TOPICS (PASW STATISTICS 17.0) Sun Li Centre for Academic Computing lsun@smu.edu.sg

SPSS TRAINING SESSION 3 ADVANCED TOPICS (PASW STATISTICS 17.0) Sun Li Centre for Academic Computing lsun@smu.edu.sg SPSS TRAINING SESSION 3 ADVANCED TOPICS (PASW STATISTICS 17.0) Sun Li Centre for Academic Computing lsun@smu.edu.sg IN SPSS SESSION 2, WE HAVE LEARNT: Elementary Data Analysis Group Comparison & One-way

More information

16 : Demand Forecasting

16 : Demand Forecasting 16 : Demand Forecasting 1 Session Outline Demand Forecasting Subjective methods can be used only when past data is not available. When past data is available, it is advisable that firms should use statistical

More information

Forecasting DISCUSSION QUESTIONS

Forecasting DISCUSSION QUESTIONS 4 C H A P T E R Forecasting DISCUSSION QUESTIONS 1. Qualitative models incorporate subjective factors into the forecasting model. Qualitative models are useful when subjective factors are important. When

More information

Lecture 2: ARMA(p,q) models (part 3)

Lecture 2: ARMA(p,q) models (part 3) Lecture 2: ARMA(p,q) models (part 3) Florian Pelgrin University of Lausanne, École des HEC Department of mathematics (IMEA-Nice) Sept. 2011 - Jan. 2012 Florian Pelgrin (HEC) Univariate time series Sept.

More information

Some useful concepts in univariate time series analysis

Some useful concepts in univariate time series analysis Some useful concepts in univariate time series analysis Autoregressive moving average models Autocorrelation functions Model Estimation Diagnostic measure Model selection Forecasting Assumptions: 1. Non-seasonal

More information

CHAPTER 11 FORECASTING AND DEMAND PLANNING

CHAPTER 11 FORECASTING AND DEMAND PLANNING OM CHAPTER 11 FORECASTING AND DEMAND PLANNING DAVID A. COLLIER AND JAMES R. EVANS 1 Chapter 11 Learning Outcomes l e a r n i n g o u t c o m e s LO1 Describe the importance of forecasting to the value

More information

Smoothing methods. Marzena Narodzonek-Karpowska. Prof. Dr. W. Toporowski Institut für Marketing & Handel Abteilung Handel

Smoothing methods. Marzena Narodzonek-Karpowska. Prof. Dr. W. Toporowski Institut für Marketing & Handel Abteilung Handel Smoothing methods Marzena Narodzonek-Karpowska Prof. Dr. W. Toporowski Institut für Marketing & Handel Abteilung Handel What Is Forecasting? Process of predicting a future event Underlying basis of all

More information

D Lab: Supply Chains

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

More information

Time Series Laboratory

Time Series Laboratory Time Series Laboratory Computing in Weber Classrooms 205-206: To log in, make sure that the DOMAIN NAME is set to MATHSTAT. Use the workshop username: primesw The password will be distributed during the

More information

Managing Service Inventory

Managing Service Inventory Managing Service Inventory Shin Ming Guo NKFUST Reasons to Hold Inventory Inventory Models ABC Classification News Vendor Problem Hospitals and Inventory Management Control Barcodes and computers keep

More information

Risk and return (1) Class 9 Financial Management, 15.414

Risk and return (1) Class 9 Financial Management, 15.414 Risk and return (1) Class 9 Financial Management, 15.414 Today Risk and return Statistics review Introduction to stock price behavior Reading Brealey and Myers, Chapter 7, p. 153 165 Road map Part 1. Valuation

More information

Analysis of algorithms of time series analysis for forecasting sales

Analysis of algorithms of time series analysis for forecasting sales SAINT-PETERSBURG STATE UNIVERSITY Mathematics & Mechanics Faculty Chair of Analytical Information Systems Garipov Emil Analysis of algorithms of time series analysis for forecasting sales Course Work Scientific

More information

Statistics in Retail Finance. Chapter 6: Behavioural models

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

More information

Time Series Analysis of Aviation Data

Time Series Analysis of Aviation Data Time Series Analysis of Aviation Data Dr. Richard Xie February, 2012 What is a Time Series A time series is a sequence of observations in chorological order, such as Daily closing price of stock MSFT in

More information

2) The three categories of forecasting models are time series, quantitative, and qualitative. 2)

2) The three categories of forecasting models are time series, quantitative, and qualitative. 2) Exam Name TRUE/FALSE. Write 'T' if the statement is true and 'F' if the statement is false. 1) Regression is always a superior forecasting method to exponential smoothing, so regression should be used

More information

Time Series Analysis

Time Series Analysis Time Series Analysis Autoregressive, MA and ARMA processes Andrés M. Alonso Carolina García-Martos Universidad Carlos III de Madrid Universidad Politécnica de Madrid June July, 212 Alonso and García-Martos

More information

Advanced Forecasting Techniques and Models: ARIMA

Advanced Forecasting Techniques and Models: ARIMA Advanced Forecasting Techniques and Models: ARIMA Short Examples Series using Risk Simulator For more information please visit: www.realoptionsvaluation.com or contact us at: admin@realoptionsvaluation.com

More information

Not Your Dad s Magic Eight Ball

Not Your Dad s Magic Eight Ball Not Your Dad s Magic Eight Ball Prepared for the NCSL Fiscal Analysts Seminar, October 21, 2014 Jim Landers, Office of Fiscal and Management Analysis, Indiana Legislative Services Agency Actual Forecast

More information

Outline: Demand Forecasting

Outline: Demand Forecasting Outline: Demand Forecasting Given the limited background from the surveys and that Chapter 7 in the book is complex, we will cover less material. The role of forecasting in the chain Characteristics of

More information

Theory at a Glance (For IES, GATE, PSU)

Theory at a Glance (For IES, GATE, PSU) 1. Forecasting Theory at a Glance (For IES, GATE, PSU) Forecasting means estimation of type, quantity and quality of future works e.g. sales etc. It is a calculated economic analysis. 1. Basic elements

More information

Chapter 15: Pricing and the Revenue Management

Chapter 15: Pricing and the Revenue Management Chapter 15: Pricing and the Revenue Management 1 Outline The Role of RM (Revenue Management) in the SCs RM for Multiple Customer Segments RM for Perishable Assets RM for Seasonable Demand RM for Bulk and

More information

Time series Forecasting using Holt-Winters Exponential Smoothing

Time series Forecasting using Holt-Winters Exponential Smoothing Time series Forecasting using Holt-Winters Exponential Smoothing Prajakta S. Kalekar(04329008) Kanwal Rekhi School of Information Technology Under the guidance of Prof. Bernard December 6, 2004 Abstract

More information

A Log-Robust Optimization Approach to Portfolio Management

A Log-Robust Optimization Approach to Portfolio Management A Log-Robust Optimization Approach to Portfolio Management Dr. Aurélie Thiele Lehigh University Joint work with Ban Kawas Research partially supported by the National Science Foundation Grant CMMI-0757983

More information

" Y. Notation and Equations for Regression Lecture 11/4. Notation:

 Y. Notation and Equations for Regression Lecture 11/4. Notation: Notation: Notation and Equations for Regression Lecture 11/4 m: The number of predictor variables in a regression Xi: One of multiple predictor variables. The subscript i represents any number from 1 through

More information

INCREASING FORECASTING ACCURACY OF TREND DEMAND BY NON-LINEAR OPTIMIZATION OF THE SMOOTHING CONSTANT

INCREASING FORECASTING ACCURACY OF TREND DEMAND BY NON-LINEAR OPTIMIZATION OF THE SMOOTHING CONSTANT 58 INCREASING FORECASTING ACCURACY OF TREND DEMAND BY NON-LINEAR OPTIMIZATION OF THE SMOOTHING CONSTANT Sudipa Sarker 1 * and Mahbub Hossain 2 1 Department of Industrial and Production Engineering Bangladesh

More information

Forecasting the first step in planning. Estimating the future demand for products and services and the necessary resources to produce these outputs

Forecasting the first step in planning. Estimating the future demand for products and services and the necessary resources to produce these outputs PRODUCTION PLANNING AND CONTROL CHAPTER 2: FORECASTING Forecasting the first step in planning. Estimating the future demand for products and services and the necessary resources to produce these outputs

More information

COMPARISON OF FIXED & VARIABLE RATES (25 YEARS) CHARTERED BANK ADMINISTERED INTEREST RATES - PRIME BUSINESS*

COMPARISON OF FIXED & VARIABLE RATES (25 YEARS) CHARTERED BANK ADMINISTERED INTEREST RATES - PRIME BUSINESS* COMPARISON OF FIXED & VARIABLE RATES (25 YEARS) 2 Fixed Rates Variable Rates FIXED RATES OF THE PAST 25 YEARS AVERAGE RESIDENTIAL MORTGAGE LENDING RATE - 5 YEAR* (Per cent) Year Jan Feb Mar Apr May Jun

More information

COMPARISON OF FIXED & VARIABLE RATES (25 YEARS) CHARTERED BANK ADMINISTERED INTEREST RATES - PRIME BUSINESS*

COMPARISON OF FIXED & VARIABLE RATES (25 YEARS) CHARTERED BANK ADMINISTERED INTEREST RATES - PRIME BUSINESS* COMPARISON OF FIXED & VARIABLE RATES (25 YEARS) 2 Fixed Rates Variable Rates FIXED RATES OF THE PAST 25 YEARS AVERAGE RESIDENTIAL MORTGAGE LENDING RATE - 5 YEAR* (Per cent) Year Jan Feb Mar Apr May Jun

More information