High-Level OP Planning and Demand Management EGN 5622 Enterprise Systems Integration (Professional MSEM) Fall, 2012 High-Level OP Planning and Demand Management EGN 5622 Enterprise Systems Integration (Professional MSEM) Fall, 2012

Production Planning & Execution January 2007 (v1.0) © 2007 by SAP AG. All rights reserved. SAP University Alliance. The Rushmore Group, LLC2 Demand Management Forecasting Sales & Operations Planning Sales Inf. System CO/PA MPS MRP/CRP Manufacturing Execution/SFC Order Settlement Procurement Process Tactical Planning Detailed Planning Manufacturing Execution

January 2008 © SAP AG - University Alliances and The Rushmore Group, LLC All rights reserved.3 High-Level OP Planning Planning activities: 1.CO/PA 2.Forecasting 3.Sales and Operations Planning ◦ Planning strategy for a product 4.Demand Management Demand management feeds to MPS and in turn MRP/CRP

Operations Planning & Execution Major Players: ◦Tactical Planning (at corporate level)  COO, CFO, Controller, Sales & Marketing, Product Line mangers, Production Planner ◦Detailed Planning (at plant level)  Production Planner/Scheduler, MRP Controller, Capacity Planners ◦Execution (at shop floor level)  Production Line Workers, Shop Floor Supervisors January 2007 (v1.0) © 2007 by SAP AG. All rights reserved. SAP University Alliance. The Rushmore Group, LLC4 Demand Management Forecasting Sales OP Planning Sales Info CO/PA MPS MRP/CRP Manufacturing Execution/SFC Order Settlement Procurement Process Tactical Planning Detailed Planning Execution

Costing-Based CO-PA

CO-PA is profitability analysis, based on cost-of- sales accounting. CO-PA can be viewed as a cube of 3 dimensions (representing product, country, and customer) CO-PA data are collected from various areas, such as ◦sales orders or billing data from SD, ◦production costs and variance from PP, and ◦values of overhead cost controlling from controlling.

Pricing and Costing for mfg. goods Cost components ◦Materials Costs ◦Labor costs ◦Equipment costs Strategies to reduce costs ◦Lean manufacturing ◦Systems approach

Forecasting, SOP & Demand Management ForecastMethod(s) DemandEstimates SalesForecastManagementTeam Inputs:Market,Economic,Other BusinessStrategy Production Resource Forecasts

Caution for Forecasting Forecasting is the foundation of a reliable SOP Accurate forecasts are essential in the manufacturing sector ◦overstocked & understocked warehouses result in the same thing: a loss in profits. Forecasts are ALWAYS WRONG January 2007 (v1.0) © 2007 by SAP AG. All rights reserved. SAP University Alliance. The Rushmore Group, LLC9

Applications of Forecasting New Facility Planning – It can take up to 5 years to design and build a new factory or design and implement a new production process. Production Planning – Demand for products vary from month to month and it can take several months to change the capacities of production processes. Workforce Scheduling – Demand for services (and the necessary staffing) can vary from hour to hour and employees weekly work schedules must be developed in advance.

Forecast Horizons LongRange MediumRange ShortRange Years Months Days,Weeks Product Lines, Factory Capacities ForecastHorizonTimeSpan Item Being Forecasted Unit of Measure Product Groups, Depart. Capacities Specific Products, Machine Capacities Dollars,Tons Units,Pounds Units,Hours

Forecasting Methods Qualitative Approaches Quantitative Approaches

Qualitative Approaches Usually based on judgments about causal factors that underlie the demand of particular products or services Do not require a demand history for the product or service, therefore are useful for new products/services Approaches vary in sophistication from scientifically conducted surveys to intuitive hunches about future events The approach/method that is appropriate depends on a product’s life cycle stage

Qualitative Methods  Educated guess intuitive/hunches  Executive committee consensus  Delphi method  Survey of sales force  Survey of customers  Historical analogy  Market research scientifically conducted surveys

Quantitative Forecasting Approaches Based on the assumption that the “forces” that generated the past demand will generate the future demand, i.e., history will tend to repeat itself Analysis of the past demand pattern provides a good basis for forecasting future demand Majority of quantitative approaches fall in the category of time series analysis

A time series is a set of numbers where the order or sequence of the numbers is important, e.g., historical demand Analysis of the time series identifies patterns Once the patterns are identified, they can be used to develop a forecast Time Series Analysis

Components of a Time Series Trends are noted by an upward or downward sloping line. Cycle is a data pattern that may cover several years before it repeats itself. Seasonality is a data pattern that repeats itself over the period of one year or less. Random fluctuation (noise) results from random variation or unexplained causes.

Quantitative Forecasting Approaches Linear Regression Simple Moving Average Weighted Moving Average Exponential Smoothing (exponentially weighted moving average) Exponential Smoothing with Trend (double exponential smoothing)

Long-Range Forecasts Time spans usually greater than one year Necessary to support strategic decisions about planning products, processes, and facilities

Simple Linear Regression Linear regression analysis establishes a relationship between a dependent variable and one or more independent variables. In simple linear regression analysis there is only one independent variable. If the data is a time series, the independent variable is the time period. The dependent variable is whatever we wish to forecast.

Simple Linear Regression Regression Equation This model is of the form: Y = a + bX Y = dependent variable X = independent variable a = y-axis intercept b = slope of regression line

Simple Linear Regression Constants a and b The constants a and b are computed using the following equations:

Simple Linear Regression Once the a and b values are computed, a future value of X can be entered into the regression equation and a corresponding value of Y (the forecast) can be calculated.

Example: College Enrollment Simple Linear Regression At a small regional college enrollments have grown steadily over the past six years, as evidenced below. Use time series regression to forecast the student enrollments for the next three years.Students Year Enrolled (1000s)

Example: College Enrollment Simple Linear Regression xyx 2 xy x=21 y=18.1 x 2 =91 xy=66.5

Example: College Enrollment Simple Linear Regression Y = X

Example: College Enrollment Simple Linear Regression Y 7 = (7) = 3.65 or 3,650 students Y 8 = (8) = 3.83 or 3,830 students Y 9 = (9) = 4.01 or 4,010 students Note: Enrollment is expected to increase by 180 students per year.

Simple Linear Regression Simple linear regression can also be used when the independent variable X represents a variable other than time. In this case, linear regression is representative of a class of forecasting models called causal forecasting models.

Multiple Regression Analysis l Multiple regression analysis is used when there are two or more independent variables. l An example of a multiple regression equation is: Y = X X 2 – 0.03X 3 Y = X X 2 – 0.03X 3 where: Y = firm’s annual sales ($millions) X 1 = industry sales ($millions) X 1 = industry sales ($millions) X 2 = regional per capita income ($thousands) X 2 = regional per capita income ($thousands) X 3 = regional per capita debt ($thousands) X 3 = regional per capita debt ($thousands)

Coefficient of Correlation (r) The coefficient of correlation, r, explains the relative importance of the relationship between x and y. The sign of r shows the direction of the relationship. The absolute value of r shows the strength of the relationship. The sign of r is always the same as the sign of b. r can take on any value between –1 and +1.

Coefficient of Correlation (r) Meanings of several values of r: -1 a perfect negative relationship (as x goes up, y goes down by one unit, and vice versa) +1 a perfect positive relationship (as x goes up, y goes up by one unit, and vice versa) 0 no relationship exists between x and y +0.3 a weak positive relationship -0.8 a strong negative relationship

Coefficient of Correlation (r) r is computed by:

Coefficient of Determination (r 2 ) The coefficient of determination, r 2, is the square of the coefficient of correlation. The modification of r to r 2 allows us to shift from subjective measures of relationship to a more specific measure. r 2 is determined by the ratio of explained variation to total variation:

Example: Railroad Products Co. Coefficient of Correlation xyx 2 xyy ,4001, ,2251, ,9001, ,5001, ,9002, ,1003, ,4003, , ,42515,4401,287.50

Example: Railroad Products Co. Coefficient of Correlation r =.9829

Example: Railroad Products Co. Coefficient of Determination r 2 = (.9829) 2 = % of the variation in RPC sales is explained by national freight car loadings.

Ranging Forecasts Forecasts for future periods are only estimates and are subject to error. One way to deal with uncertainty is to develop best-estimate forecasts and the ranges within which the actual data are likely to fall. The ranges of a forecast are defined by the upper and lower limits of a confidence interval.

Ranging Forecasts The ranges or limits of a forecast are estimated by: Upper limit = Y + t(s yx ) Lower limit = Y - t(s yx ) where: Y = best-estimate forecast t = number of standard deviations from the meanof the distribution to provide a given proba-bility of exceeding the limits through chance s yx = standard error of the forecast

Ranging Forecasts The standard error (deviation) of the forecast is computed as:

Seasonalized Time Series Regression Analysis Select a representative historical data set. Develop a seasonal index for each season. Use the seasonal indexes to deseasonalize the data. Perform lin. regr. analysis on the deseasonalized data. Use the regression equation to compute the forecasts. Use the seas. indexes to reapply the seasonal patterns to the forecasts.

Example: Computer Products Corp. Seasonalized Times Series Regression Analysis An analyst at CPC wants to develop next year’s quarterly forecasts of sales revenue for CPC’s line of Epsilon Computers. She believes that the most recent 8 quarters of sales (shown on the next slide) are representative of next year’s sales.

Example: Computer Products Corp. Seasonalized Times Series Regression Analysis ◦Representative Historical Data Set YearQtr.($mil.)YearQtr.($mil.)

Example: Computer Products Corp. Seasonalized Times Series Regression Analysis ◦Compute the Seasonal Indexes Quarterly Sales YearQ1Q2Q3Q4Total Totals Qtr. Avg Seas.Ind

Example: Computer Products Corp. Seasonalized Times Series Regression Analysis ◦Deseasonalize the Data Quarterly Sales YearQ1Q2Q3Q

Example: Computer Products Corp. Seasonalized Times Series Regression Analysis ◦Perform Regression on Deseasonalized Data Yr.Qtr.xyx 2 xy Totals

Example: Computer Products Corp. Seasonalized Times Series Regression Analysis ◦Perform Regression on Deseasonalized Data Y = X

Example: Computer Products Corp. Seasonalized Times Series Regression Analysis ◦Compute the Deseasonalized Forecasts Y 9 = (9) = Y 10 = (10) = Y 11 = (11) = Y 12 = (12) = Note: Average sales are expected to increase by.199 million (about $200,000) per quarter.

Example: Computer Products Corp. Seasonalized Times Series Regression Analysis ◦Seasonalize the Forecasts Seas.Deseas.Seas. Yr.Qtr.IndexForecastForecast

Short-Range Forecasts Time spans ranging from a few days to a few weeks Cycles, seasonality, and trend may have little effect Random fluctuation is main data component

Evaluating Forecast-Model Performance Short-range forecasting models are evaluated on the basis on the following three characteristics:  Impulse response  Noise-dampening ability  Accuracy

Evaluating Forecast-Model Performance Impulse Response and Noise-Dampening Ability ◦If forecasts have little period-to-period fluctuation, they are said to be noise dampening. ◦Forecasts that respond quickly to changes in data are said to have a high impulse response. ◦A forecast system that responds quickly to data changes necessarily picks up a great deal of random fluctuation (noise). ◦Hence, there is a trade-off between high impulse response and high noise dampening.

Evaluating Forecast-Model Performance Accuracy ◦Accuracy is the typical criterion for judging the performance of a forecasting approach ◦Accuracy is how well the forecasted values match the actual values

Monitoring Accuracy Accuracy of a forecasting approach needs to be monitored to assess the confidence you can have in its forecasts and changes in the market may require reevaluation of the approach Accuracy can be measured in several ways ◦Standard error of the forecast (covered earlier) ◦Mean absolute deviation (MAD) ◦Mean squared error (MSE)

Monitoring Accuracy Mean Absolute Deviation (MAD)

Mean Squared Error (MSE) MSE = (S yx ) 2 A small value for S yx means data points are tightly grouped around the line and error range is small. When the forecast errors are normally distributed, the values of MAD and s yx are related: MSE = 1.25(MAD) Monitoring Accuracy

Short-Range Forecasting Methods (Simple) Moving Average Weighted Moving Average Exponential Smoothing Exponential Smoothing with Trend

Simple Moving Average An averaging period (AP) is given or selected The forecast for the next period is the arithmetic average of the AP most recent actual demands It is called a “simple” average because each period used to compute the average is equally weighted... more

Simple Moving Average It is called “moving” because as new demand data becomes available, the oldest data is not used By increasing the AP, the forecast is less responsive to fluctuations in demand (low impulse response and high noise dampening) By decreasing the AP, the forecast is more responsive to fluctuations in demand (high impulse response and low noise dampening)

Weighted Moving Average This is a variation on the simple moving average where the weights used to compute the average are not equal. This allows more recent demand data to have a greater effect on the moving average, therefore the forecast.... more

Weighted Moving Average The weights must add to 1.0 and generally decrease in value with the age of the data. The distribution of the weights determine the impulse response of the forecast.

The weights used to compute the forecast (moving average) are exponentially distributed. The forecast is the sum of the old forecast and a portion () of the forecast error (A t-1 -F t-1 ). F t = F t-1 + (A t-1 -F t-1 )... more Exponential Smoothing

The smoothing constant, , must be between 0.0 and 1.0. A large  provides a high impulse response forecast. A small  provides a low impulse response forecast.

Example: Central Call Center Moving Average CCC wishes to forecast the number of incoming calls it receives in a day from the customers of one of its clients, BMI. CCC schedules the appropriate number of telephone operators based on projected call volumes. CCC believes that the most recent 12 days of call volumes (shown on the next slide) are representative of the near future call volumes.

Example: Central Call Center Moving Average ◦Representative Historical Data DayCallsDayCalls

Example: Central Call Center Moving Average Use the moving average method with an AP = 3 days to develop a forecast of the call volume in Day 13. F 13 = ( )/3 = calls

Example: Central Call Center Weighted Moving Average Use the weighted moving average method with an AP = 3 days and weights of.1 (for oldest datum),.3, and.6 to develop a forecast of the call volume in Day 13. F 13 =.1(168) +.3(198) +.6(159) = calls Note: The WMA forecast is lower than the MA forecast because Day 13’s relatively low call volume carries almost twice as much weight in the WMA (.60) as it does in the MA (.33).

Example: Central Call Center l Exponential Smoothing If a smoothing constant value of.25 is used and the exponential smoothing forecast for Day 11 was calls, what is the exponential smoothing forecast for Day 13? F 12 = (198 – ) = F 13 = (159 – ) =

Example: Central Call Center Forecast Accuracy - MAD Which forecasting method (the AP = 3 moving average or the  =.25 exponential smoothing) is preferred, based on the MAD over the most recent 9 days? (Assume that the exponential smoothing forecast for Day 3 is the same as the actual call volume.)

Example: Central Call Center AP = 3  =.25 DayCallsForec.|Error|Forec.|Error| MAD

Criteria for Selecting a Forecasting Method Cost Accuracy Data available Time span Nature of products and services Impulse response and noise dampening

Criteria for Selecting a Forecasting Method Data Available ◦Is the necessary data available or can it be economically obtained? ◦If the need is to forecast sales of a new product, then a customer survey may not be practical; instead, historical analogy or market research may have to be used.

Criteria for Selecting a Forecasting Method Time Span ◦What operations resource is being forecast and for what purpose? ◦Short-term staffing needs might best be forecast with moving average or exponential smoothing models. ◦Long-term factory capacity needs might best be predicted with regression or executive- committee consensus methods.

Criteria for Selecting a Forecasting Method Nature of Products and Services ◦Is the product/service high cost or high volume? ◦Where is the product/service in its life cycle? ◦Does the product/service have seasonal demand fluctuations?

Criteria for Selecting a Forecasting Method Impulse Response and Noise Dampening ◦An appropriate balance must be achieved between:  How responsive we want the forecasting model to be to changes in the actual demand data  Our desire to suppress undesirable chance variation or noise in the demand data

Monitoring and Controlling a Forecasting Model Tracking Signal (TS) ◦The TS measures the cumulative forecast error over n periods in terms of MAD ◦If the forecasting model is performing well, the TS should be around zero ◦The TS indicates the direction of the forecasting error; if the TS is positive -- increase the forecasts, if the TS is negative -- decrease the forecasts.

Monitoring and Controlling a Forecasting Model Tracking Signal ◦The value of the TS can be used to automatically trigger new parameter values of a model, thereby correcting model performance. ◦If the limits are set too narrow, the parameter values will be changed too often. ◦If the limits are set too wide, the parameter values will not be changed often enough and accuracy will suffer.

Computer Software for Forecasting Examples of computer software with forecasting capabilities ◦Forecast Pro ◦Autobox ◦SmartForecasts for Windows ◦SAS ◦SPSS ◦SAP ◦POM Software Libary Primarily for forecasting HaveForecastingmodules

World-Class Forecasting Practice Predisposed to have effective methods of forecasting because they have exceptional long-range business planning Formal forecasting effort Develop methods to monitor the performance of their forecasting models Do not overlook the short run.... excellent short range forecasts as well

Sales and Operations Planning (SOP) Information Origination ◦Sales ◦Marketing ◦Manufacturing ◦Accounting ◦Human Resources ◦Purchasing Intra-firm Collaboration ◦Institutional Common Sense January 2007 (v1.0) © 2007 by SAP AG. All rights reserved. SAP University Alliance. The Rushmore Group, LLC79

Master Data - Product Groups Aggregate planning that group together materials or other product groups (Product Families) Multi- or Single- Level Product Groups ◦the lowest level must always consist of materials January 2007 (v1.0) © 2007 by SAP AG. All rights reserved. SAP University Alliance. The Rushmore Group, LLC80 Bikes TouringMountain 24 Speed18 Speed 24 Speed Red 24TBlue 24TRed 24MBlue 24M

Planning Levels January 2007 (v1.0) © 2007 by SAP AG. All rights reserved. SAP University Alliance. The Rushmore Group, LLC81 Bikes 55% Touring 45% Mountain 70% 24 Speed 30% 18 Speed 40% 18 Speed 60% 24 Speed 50% Red 24T 50% Blue 24T 40% Red 24M 60% Blue 24M Planning at Product Group Level Planning at Material Level

Demand Management Link between Strategic Planning (SOP) & Detailed Planning (MPS/MRP) The results of Demand Mgmt is called the Demand Program, it is generated from our independent requirements – PIR (Planned IR) and CIR (Customer IR) January 2007 (v1.0) © 2007 by SAP AG. All rights reserved. SAP University Alliance. The Rushmore Group, LLC82 Bikes 55% Touring 45% Mountain 70% 24 Speed 30% 18 Speed 40% 18 Speed 60% 24 Speed 50% Red 24T 50% Blue 24T 40% Red 24M 60% Blue 24M Disaggregation Product Groups Material

Demand Management January 2007 (v1.0) © 2007 by SAP AG. All rights reserved. SAP University Alliance. The Rushmore Group, LLC83 Planned Independent Requirements Customer Independent Requirements SalesForecast Demand Program MPS / MRP

Transfer from High Level to Detailed Planning January 2007 (v1.0) © 2007 by SAP AG. All rights reserved. SAP University Alliance. The Rushmore Group, LLC84 Bikes 55% Touring 45% Mountain 70% 24 Speed 30% 18 Speed 40% 18 Speed 60% 24 Speed 50% Red 24T 50% Blue 24T 40% Red 24M 60% Blue 24M Demand Planning Data Planning at Material Level Disaggregation Planned Independent Requirements At Material and Plant Level Transfer Operative Planning Data Planning at Group Level

January 2008 © SAP AG - University Alliances and The Rushmore Group, LLC All rights reserved.85 Creation of The Demand Program Production Plan The result of Demand Management is the demand program Data Elements of the Production Plan ◦Time Buckets ◦Sales ◦Production ◦Stock levels ◦Days supply

Enterprise Strategic Planning SAP Implementation EGN 5622 Enterprise Systems Integration (Professional MSEM) Fall, 2012 Enterprise Strategic Planning SAP Implementation EGN 5622 Enterprise Systems Integration (Professional MSEM) Fall, 2012

Planning Strategies Planning strategies represent the business procedures for ◦The planning of production quantities ◦Dates SAP offers a range of PP strategies ◦From those for MTS ◦To those for MTO January 2007 (v1.0) © 2007 by SAP AG. All rights reserved. SAP University Alliance. The Rushmore Group, LLC87

Planning Strategies Multiple types of planning strategies based upon environment –Make-To-Stock (MTS) –Make-To-order (MTO) Driven by sales orders –Configurable materials Mass customization of one –Assembly orders January 2007 (v1.0) © 2007 by SAP AG. All rights reserved. SAP University Alliance. The Rushmore Group, LLC88

Planning Strategy for Make-to-Stock Planning Strategy for Make-to-Stock Planning takes place using Independent Requirements ◦Sales are covered by make-to-stock inventory Strategies ◦10 – Net Requirements Planning ◦11 – Gross Requirements Planning ◦30 – Production by Lot Size ◦40 – Planning with Final Assembly January 2007 (v1.0) © 2007 by SAP AG. All rights reserved. SAP University Alliance. The Rushmore Group, LLC89

Planning Strategy for Make-to-Order Planning Strategy for Make-to-Order Planning takes place using Customer Orders ◦Sales are covered by make-to-order production Strategies ◦20 – Make to Order Production ◦50 – Planning without Final Assembly ◦60 – Planning with Planning Material January 2007 (v1.0) © 2007 by SAP AG. All rights reserved. SAP University Alliance. The Rushmore Group, LLC90

Forecasting set up in SAP Forecasting Models ◦Trend ◦Seasonal ◦Trend and Seasonal ◦Constant Selecting a Model ◦Automatically ◦Manually January 2007 (v1.0) © 2007 by SAP AG. All rights reserved. SAP University Alliance. The Rushmore Group, LLC91

Sales and Operations Planning (SOP) Flexible forecasting and planning tool Usually consists of three steps: ◦Sales Plan ◦Production Plan ◦Rough Cut Capacity Plan Planned at an aggregate level in time buckets January 2007 (v1.0) © 2007 by SAP AG. All rights reserved. SAP University Alliance. The Rushmore Group, LLC92

Statistics Graphics January 2007 (v1.0) © 2007 by SAP AG. All rights reserved. SAP University Alliance. The Rushmore Group, LLC93