Forecasting Why forecast? Understanding patterns of demand Types of Forecasting Methods Qualitative methods Quantitative methods Roadmapping for future 2

Why forecast? Customer Requirements and patterns of demand needs to be understood & carefully managed Future demand is invariably subject to uncertainty Demand must be managed for efficient utilisation of resources in terms of: Plant Capacity Materials Human resources Organisational system

Challenge for Logistics Executives A major problem is how to balance demand against available capacity manufacturing firms often rely on inventories service organisations cannot rely on inventories of finished goods to act as a buffer between a constrained level of supply and a fluctuating level of demand 1

Managing/Aggregating Typical scenario Often there is a mismatch between demand and and supply (i.e. conditions are dynamic not static) Demand Production and Supply Managing/Aggregating Capacity to Demand The Operation The Customers

Forecasting Forecasting therefore: must be expressed in terms that are useful must be accurate should give indication of relative uncertainty should take account of seasonality should take into account of weekly/daily demand fluctuation

Patterns of demand The amount of capacity required is made difficult by two factors: the demand varies considerably over relatively short periods of time (hours, minutes) e.g. retails outlets, restaurants, banks etc. the time taken to perform the service may itself vary from customer to customer Therefore important to understand the patterns and determinants of demand

Understanding Demand does demand follow a regular predictable cycle? i.e. hourly, daily, weekly etc. and what causes these variations? are changes random in nature? if so, what are the underlying causes? can demand be disaggregated by market segment to reflect such components as: use patterns by a particular type of customer or for a particular purpose variations in the net profitability of each completed transactions?

Strategies to cope with fluctuations in demand Alternative options available: Level capacity plans Ignore fluctuations & keep activity level constant Capacity-leading (lead demand plans) Produce in advance of demand Capacity lagging (chase strategy) plans i.e. capacity changed to follow demand Adjust capacity to reflect the fluctuations in demand Methods of adjusting include: changing the number of service people overtime changing the hours worked part-time/short term contract staff using subcontractors

Managing Demand Influence demand to minimise changes in capacity Methods for influencing demand include: price changes - January sales, airlines Saturday night stay to qualify for a lower fare advertising and promotions developing non-peak demand Alternative products/services

Managing Demand continued developing complementary services (cash dispensers, banking services by Sainsbury, insurance by travel agents using reservation or appointment systems - theatre/hotel bookings, travel agencies making the customer wait or queue Operations Manager may choose to implement mixture of these strategies Yield Management – is the application of information to improve revenue American airlines adjust fares to fill empty seats (but still keep a few in reserve for full paying passengers). This real-time pricing strategy maximises revenue. Marriott hotels have installed a yield management system

Long Term Planning It is therefore important to plan (long, medium and short term planning) 1) Long term typically >2years To decide whether demand is sufficient to enter a market To determine long term capacity needs for facility design

2) Medium to short term planning To adapt capacity & resources in the medium term typically 6 months to 2 years Recruit or shed labour Balance production across multiple sites Ensure supply chain can ramp up or down to ensure consistent supply To enable efficient responsiveness in the short term typically up to 6 months ‘Real’ production & personnel scheduling Material & inventory planning Maintenance planning It is crucial to balance production efficiency & customer response

Demand Management Independent Demand: Finished Goods Dependent Demand: Raw Materials, Component parts, Sub-assemblies, etc. A B(4) C(2) D(2) E(1) D(3) F(2) 3

What a firm can do to manage it? Independent Demand What a firm can do to manage it? Can take an active role to influence demand Can take a passive role and simply respond to demand 4

Types of Forecasts Forecasts should be produced as an integrated part of decision making framework 2 categories of forecasting approaches Qualitative (Judgmental) Quantitative Time Series Analysis Causal Relationships Simulation 5

Average demand for a period of time Trend Seasonal element Components of Demand Average demand for a period of time Trend Seasonal element Cyclical elements Random variation 7

Finding Components of Demand Seasonal variation 1 2 3 4 x Year Linear Trend Sales 6

Types of forecasting methods Qualitative Quantitative Economic indicators Scenario writing Causal Relationships Time series analysis Sales force composite Market research Delphi methods Historical analogy Trend projection Smoothing/ Extrapolation Panel Consensus Decomposition

Qualitative Methods Qualitative Methods Scenario analysis Economic Indicators Scenario analysis Qualitative Methods Market Research Panel Consensus Historical Analogy Delphi Method Sales Force Composite

Economic indicators Indicators based on: Prices, employment, production Provide the basis for interpretative judgemental forecasting Types of indicator Leading provide advanced warning Coincident reflect current economic performance Lagging confirm changes previously signalled Composite of leading indicators signal major changes in economic activity Impact of economic indicators on demand for specific product is very sector specific

Market Research Sample members of target market Can provide sophisticated & accurate forecasts on market potential Needs expertise in design and interpretation Usually costly

Historical Analogy May look at same product in a different market e.g. take off of mobile phone telephone phone sales (internet service) in a less developed country New product analogous to another in which similar take up behaviour may occur e.g. Digital TV based on an historical analogy to provide video cassette recorders

Scenario Analysis Origins in strategic military planning Explores a number of different scenarios Identifies the principle factors that may affect the future Acknowledges that different scenarios may be plausible from a different starting point Roadmapping - Visit website (http://www.bridges-eu.org/) This is currently ongoing project.

Forecasting vs Scenarios Forecasting based on past, present experience and determine future projections Scenarios: Analyse past, present and determine various options or scenarios i.e what if ….? (what do we do if there is a fire, flood on the roads, virus in the country (SARS). Based on these scenarios one prepare different plans to counteract

Panel Consensus Forecasting Methods Take the knowledge of more than one expert Pitfalls Consensus methods may be comforting but wrong! – groups take bigger risks! Dominant individuals Individuals with their own agendas Use surveys or the Delphi method

Delphi Method Choose the experts to participate representing a variety of knowledgeable people in different areas Through a questionnaire (or E-mail), obtain forecasts (and any premises or qualifications for the forecasts) from all participants Evaluate responses – produce a numerical summary (modal & extreme values) Redistribute summarised results them to the participants along with appropriate new questions or with explanations to any extreme or unusual values Evaluate and summarize responses again, refining forecasts and conditions, and again develop new questions Repeat Step 4 & 5 until clear forecast emerges Summarise the final results and distribute to all participants (Alan McKinon’s Delphi study) 10

Sales force composite review Supports short & medium term forecasting Particularly popular where there is: A complex product mix Few customers Close contact with customers Sales force have technical expertise Potential sources of errors ….?

Quantitative forecasting Based on mathematical analysis of historical data Causal modelling Time series methods

Causal modelling–projects annual sales of washing m/cs Price MODEL OF RELATIONSHIPS Per Capita Income Annual Retail sales New House Completions INPUTS OUTPUTS MODEL The independent variables The dependent variables Set the values Turn handle Obtain forecast

Causal models Ideally develop a model to predict the dependent variable from one or more independent variables mechanistically The model may be: Univariate with one independent variable Multivariate with more than one independent variable Generally are less interested in the form of the relationship than in its predicative accuracy

Simple Linear Regression Model The simple linear regression model seeks to fit a line through various data over time Y a 0 1 2 3 4 5 x (Time) Yt = a + bx Is the linear regression model Yt is the regressed forecast value or dependent variable in the model, a is the intercept value of the the regression line, and b is similar to the slope of the regression line. However, since it is calculated with the variability of the data in mind, its formulation is not as straight forward as our usual notion of slope. 35

Simple Linear Regression Formulas for Calculating “a” & “b” 36

Simple Linear Regression Problem Data Question: Given the data below, what is the simple linear regression model that can be used to predict sales in future weeks? 37

Answer: First, using the linear regression formulas, we can compute “a” and “b” 38

The resulting regression model is: Yt = 143.5 + 6.3x Now if we plot the regression generated forecasts against the actual sales we obtain the following chart: 180 Period 135 140 145 150 155 160 165 170 175 1 2 3 4 5 Sales Forecast 39

Time Series Analysis Time series forecasting models try to predict the future based on past data You can pick models based on: 1. Time horizon to forecast 2. Data availability 3. Accuracy required 4. Size of forecasting budget 5. Availability of qualified personnel 14

Time series A time series is a set of data taken at successive points in time Used to predict the future behaviour of the series Very important for short-term demand forecasting Most useful in fast moving consumer goods & commodity markets

Time series – long-term trend Is the long-term change in the observations once short-term fluctuations have been removed Trend projection is a very basic form of forecasting

Time series – cyclical Time Time series that fluctuate about their long-term trend Can be caused by: investment cycles, business confidence

Time series – irregular variation Time series – seasonal Recurring changes within a one year period Often exhibited in retail Time series – irregular variation The components of the time series not explained by T, C & S May be considered as random as it is unpredictable Always has some cause

Simple Moving Average Formula The simple moving average model assumes an average is a good estimator of future behavior The formula for the simple moving average is: Ft = Forecast for the coming period N = Number of periods to be averaged A t-1 = Actual occurrence in the past period for up to “n” periods 15

Simple Moving Average Problem (1) Question: What are the 3-week and 6-week moving average forecasts for demand? Assume you only have 3 weeks and 6 weeks of actual demand data for the respective forecasts 15

Calculating the moving averages gives us: 43 F4=(650+678+720)/3 =682.67 F7=(650+678+720 +785+859+920)/6 =768.67 The McGraw-Hill Companies, Inc., 2004 16

Plotting the moving averages and comparing them shows how the lines smooth out to reveal the overall upward trend in this example Note how the 3-Week is smoother than the Demand, and 6-Week is even smoother 17

Simple Moving Average Problem (2) Data Question: What is the 3 week moving average forecast for this data? Assume you only have 3 weeks and 5 weeks of actual demand data for the respective forecasts 18

Simple Moving Average Problem (2) Solution F4=(820+775+680)/3 =758.33 F6=(820+775+680 +655+620)/5 =710.00 19

Weighted Moving Average Formula While the moving average formula implies an equal weight being placed on each value that is being averaged, the weighted moving average permits an unequal weighting on prior time periods The formula for the moving average is: wt = weight given to time period “t” occurrence (weights must add to one) 20

Weighted Moving Average Problem (1) Data Question: Given the weekly demand and weights, what is the forecast for the 4th period or Week 4? Weights: t-1 .5 t-2 .3 t-3 .2 Note that the weights place more emphasis on the most recent data, that is time period “t-1” 20

Weighted Moving Average Problem (1) Solution F4 = 0.5(720)+0.3(678)+0.2(650)=693.4 21

Weighted Moving Average Problem (2) Data Question: Given the weekly demand information and weights, what is the weighted moving average forecast of the 5th period or week? Weights: t-1 .7 t-2 .2 t-3 .1 22

Weighted Moving Average Problem (2) Solution F5 = (0.1)(755)+(0.2)(680)+(0.7)(655)= 672 23

Exponential Smoothing Model Ft = Ft-1 + a(At-1 - Ft-1) Where : F = Forecast value for the coming t time period t F = Forecast value in 1 past time period t - 1 A = Actual occurrence in the past t time period t - 1 a = Alpha smoothing constant Premise: The most recent observations might have the highest predictive value Therefore, we should give more weight to the more recent time periods when forecasting 24

Time series extrapolation Elementary time series methods Moving average Exponential smoothing

Web-Based Forecasting: CPFR Defined Collaborative Planning, Forecasting, and Replenishment (CPFR) a Web-based tool used to coordinate demand forecasting, production and purchase planning, and inventory replenishment between supply chain trading partners. Used to integrate the multi-tier or n-Tier supply chain, including manufacturers, distributors and retailers. CPFR’s objective is to exchange selected internal information to provide for a reliable, longer term future views of demand in the supply chain. CPFR uses a cyclic and iterative approach to derive consensus forecasts. 33

Web-Based Forecasting: Steps in CPFR 1. Creation of a front-end partnership agreement 2. Joint business planning 3. Development of demand forecasts 4. Sharing forecasts 5. Inventory replenishment 33