2022 T3 Mmm710 W07 Forecasting Ii.pptx

Presentation name or heading here 1 Deakin University CRICOS Provider Code 00113B MMM710 Business Process and Operations Management Week 7 Forecasting Deakin University CRICOS Provider Code 00113B 2 D[.]

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2015

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The EPIC Framework for Country Risk Assessment

• Assess supply chain readiness

• Economic (E), Political (P), Infrastructural (I), and Competence (C)

• Levels of maturity held by a geographic region is assessed with respect to the region’s ability to

support supply chain activities

•Economy – GDP and GDP Growth Rate, Population Size, FDI, Exchange Rate

• Stability & CPI, Balance of Trade

•Politics – Ease of Doing Business, Legal & Regulatory Framework, Risk of Political

• Stability, Intellectual Property Rights

•Infrastructure – Transportation Infrastructure, Utility Infrastructure (Electricity),

• Telecommunications & Connectivity

•Competence – Labour Relations, Education Level, Logistics Competence, Customs & Security

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Usefulness

• Combining forecasts within individual methods

and across different methods

•can reduce forecast errors by as much as 50%

• Forecasts errors from currently used methods

can be reduced

•by increasing their compliance with the principles of conservatism (Golden Rule of Forecasting) and simplicity (Occam’s Razor) Clients and other interested parties can use the checklists to determine whether forecasts were derived using evidence-based procedures and can, therefore, be trusted for making decisions Scientists can use the checklists to devise tests of the predictive validity of their findings

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Week 1 - Introduction

Week 2 - Innovation Perspective on Operations Management

Week 3 - Sustainability Perspective on Operations Management

Week 4 - Strategic Perspective on Operations Management

Week 5 - Mid-term Review

Week 6 - Operations Planning

Week 7 - Forecasting

Week 8 - Resource Management

Week 9 - Quality Management (Assessment Task 1 due on 16 January 2023)

Week 10 - Operations Resilience

Week 11 - Unit Review

MMM710 Weekly Learning Schedule

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Why Forecast?

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Forecasting and Demand Planning

Process of projecting values of one or more variables into the

future

Key component in:

• Supply chain management systems

• Customer relationship management systems

• Revenue management systems

•Demand Planning Modules

• Integrate marketing, inventory, sales, operations planning, and financial data

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Forecasting and Demand Planning (cont’d.)

• Supply chain members find it important to manage demand,

• especially in ‘pull’ environments – The Supply Chain only responds to demand

• Suppliers must find ways to better match supply & demand

• to achieve optimal levels of

• cost, quality, & customer service

• to enable them to compete with other supply chains

• Improved forecasts benefit all trading partners in the supply chain

• & mitigates supply-demand mismatch problems

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The importance of Demand Forecasting

• A forecast is an estimate of future demand & provides the basis for planning

decisions

• The goal is to minimize deviation between actual demand and forecast

• The factors that influence demand must be considered when forecasting thereby

shifting focus onto the end user

• Good forecasting lowers the risk of bullwhip effect & associated overstocking

and stock-out costs, improves production planning & customer service

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The importance of Demand Forecasting

The importance of Demand Forecasting (cont’d.)

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Supply Chain Visualized as a Network

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Forecast Planning Horizon

Planning horizon: Length of time on which a forecast is based

• Spans from short-range forecasts of under 3 months to long-range forecasts of 1

Forecasting Techniques

& intuition (subjective)

historical data to forecast future demand

within the quantitative forecasting techniques

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Forecasting Techniques (cont’d.)

Qualitative Forecasting Methods

• Generally used when data are limited, unavailable, or not currently

relevant

• Forecast depends on skill & experience of forecaster(s) & available

information

Four commonly used qualitative forecasting methods are:

1.Jury of executive opinion 2.Delphi method

3.Sales force composite

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Forecasting Techniques (cont’d.)

Jury of executive opinion

• Group of senior management executives who are knowledgeable

about their markets, competitors, and the business environment

collectively develop the forecast

Delphi method

• Group of internal and external experts are surveyed during several

rounds in terms of future events and long-term forecasts of demand,

to develop a forecast

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Forecasting Techniques (cont’d.)

Sales force composite

• Forecast is based on the sales force’s knowledge of the market and

estimates of customer needs

Consumer survey

• Forecasts are developed from the results surveying customers on

future purchasing needs, new product ideas and opinions about

existing or new products

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Forecasting Techniques (cont’d.)

Based on the assumption that the future will be an extrapolation of the past

Quantitative Methods

extension of the past Historical data is used to predict future demand

factors (independent variables) predict demand

It is generally recommended to use a combination of quantitative &

qualitative techniques (i.e., a “hybrid” or “basket” approach)

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Data Patterns in Time Series - Part 1

Time series: Set of observations measured at successive points in

time or over successive periods of time

•Characteristics

• Trend: Underlying pattern of growth or decline in a time series

• Seasonal patterns: Characterized by repeated runs of ups and downs over short periods

• Cyclical patterns: Regular patterns in a data series that take place over long periods of

Example Linear and Nonlinear Trend

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Simple Moving Average

Moving average (MA) forecast: Average of the most recent “k”

observations in a time series

• As the value of k increases, the forecast reacts slowly to changes in the time series

• As the value of k decreases, the forecast reacts quickly to changes in the time series

• Effective for short planning horizons where demand is relatively stable and consistent

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Single Exponential Smoothing (SES) - Part 1

Uses a weighted average of past time-series values to forecast

the value of the time series in the next period

• Where

- α - Smoothing constant (0 ≤ α ≤ 1) and is

approximately equal to:

So, for α = 0.5, k = (2/0.5) – 1 = 3 i.e SES with α = 0.5 approximately corresponds to a MA with k = 3 i.e a 3-period moving average forecast.

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Single Exponential Smoothing (SES) - Part 2

• Large values of α place more emphasis on recent data

• Small values of α is preferred when a time series is volatile and contains substantial

random variability

Disadvantages

• Forecast will lag actual values if a time series exhibits a positive trend

• Forecast will overshoot actual values if the time series exhibits a negative trend

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Forecasting Techniques (cont’d.)

Linear Trend Forecast – trend can be estimated using simple linear

regression to fit a line to a time series

Ŷ = b0 + b1x

Where Ŷ = forecast or dependent variable

• x = time variable

• b0 = intercept of the line

• b1 = slope of the line

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Forecasting Techniques (cont’d.)

Excel’s Add Trendline Function

• Helps find the best-fitting regression model for a time series

• Linear and a variety of nonlinear functional forms are available to fit the

data

• Displays R-squared values for the data entered

• R-squared value is a measure of variation in the dependent variable that can be

attributed to the independent variable (i.e., changes in demand that are

attributable to time when fitting a trendline to a series of observations over

time)

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Forecasting Techniques (cont’d.)

Cross-sectional Models

• One or several external variables are identified that are related to demand i.e., are

seen to have an impact on the future quantity demanded

• Simple regression – Only one explanatory variable is used & is similar to the

previous trend model The only difference from linear trend is that the

explanatory variable is no longer time but some other variable

Ŷ = b0 + b1x

Where Ŷ = forecast or dependent variable

x = explanatory or independent variable

b0 = intercept of the line

b1 = slope of the line

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Forecasting Techniques (cont’d.)

•Multiple regression – helps predict the estimated value of the dependent variable (Y) for known values of multiple independent (or explanatory) variables X1, X2, etc

• Y = b0 + b1X1 + b2X2 + … + bnXn + e

• For example, a multiple regression model that predicts the quantity demanded

using the price of a product and the price of its closest substitute as two

explanatory variables will look like:

•Quantity demanded = b0 + b1(Price of product) + b2(Price of closest

substitute) + e

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Forecast Error - Part 1

Difference between the observed value of the time series

and the forecast (At − Ft)

•Mean square error (MSE)

• Where T is all periods of data in the time series

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Forecast Error - Part 2

•Mean absolute deviation error (MAD)

• Where T is all periods of data in the time series

Mean absolute percentage error (MAPE)

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Forecasting in Practice - Part 1

Managers use a variety of judgmental and quantitative forecasting

techniques

First step in developing a forecast involves understanding its purpose

Choosing a forecasting method depends on:

• Time span for which a forecast is being made

• Needed frequency of forecast updating

Forecasting in Practice - Part 2

Tracking signal - Provides a method for monitoring a forecast

by quantifying bias

•Bias: Tendency of forecasts to consistently be larger or smaller than the

actual values of the time series

• Values between plus and minus 4 indicate an adequate forecasting model

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Collaborative Planning, Forecasting &

Replenishment (CPFR)

What is CPFR?

• It is a business practice that combines the intelligence of multiple trading partners

in the planning & fulfillment of customer demands

• It links sales & marketing best practices, such as category management, to supply

chain planning processes to increase availability while reducing inventory,

transportation & logistics costs.

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Collaborative Planning, Forecasting &

Replenishment (CPFR) – Cont’d

• Real value of CPFR comes from sharing of forecasts among firms rather than

sophisticated algorithms from only one firm

• Does away with the shifting of inventories among trading partners that

sub-optimizes the supply chain

• CPFR provides the supply chain with a plethora of benefits but requires a

fundamental change in the way that buyers & sellers work together

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Collaborative Planning, Forecasting &

Replenishment (CPFR) – Cont’d

CPFR Benefits

• Strengthens partner relationships

• Provides analysis of sales and order forecasts

• Uses point-of-sale data, seasonal activity, promotions, to improve forecast accuracy

• Manages the demand chain and proactively eliminates problems before they

Collaborative Planning, Forecasting &

Replenishment (CPFR) – Cont’d

CPFR Benefits (cont’d)

• Uses joint planning and promotions management

• Integrates planning, forecasting and logistics activities

• Provides efficient category management and understanding of consumer

purchasing patterns

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Collaborative Planning, Forecasting &

Replenishment (CPFR) – Cont’d

CPFR Benefits (cont’d)

• Provides analysis of key performance metrics (e.g., forecast accuracy, forecast

exceptions, product lead times, inventory turnover, percentage stockouts) to

reduce supply chain inefficiencies, improve customer service, and increase

revenues and profitability

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Institute for Business Forecasting & Planning:

• (https://ibf.org/)

International Institute of Forecasters:

• (www.forecasters.org)

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A couple of useful websites for forecasting

tools/advice/research

Key Terms - Part 1

• Moving average (MA) forecast

• Single exponential smoothing (SES)

• Regression analysis

• Multiple linear regression model

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Key Terms - Part 2

In-seminar exercise: continuing with the case

vignette in Week 6

• Read the decision problems for Week 7 and identify the important

quantitative information

• Consider the available information (and also any missing

information) in taking the forecasting decisions outlined in the decision problems.

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Self-learning exercise

• In view of the ‘golden rule’ of forecasting identified by Armstrong and Green

(2018), how would you advise a firm on adopting the best demand forecasting approach?

• How do you think CPFR principles may be aligned with a best demand

forecasting approach chosen by applying the ‘golden rule’?

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