High-Level OP Planning and Demand Management EGN 5622 Enterprise Systems Integration (Professional MSEM) Fall, 2011.

High-Level OP Planning and Demand Management EGN Enterprise Systems Integration (Professional MSEM) Fall, 2011

High-Level OP Planning and Demand management Theories & Concepts EGN Enterprise Systems Integration (Professional MSEM) Fall, 2011

High-Level OP Planning
ECC 6.0 January 2008 Enterprise Operations Planning OP Execution High-Level OP Planning Procurement Process CO/PA Detailed OP Planning Forecasting Sales and Operations Planning Demand Management MPS MRP Execution Sales Information System Manufacturing Execution Warehouse Management Strategy Planning Vision Goals & Objectives Strategy Product Portfolio and Roadmap Sales Process January 2008 © SAP AG - University Alliances and The Rushmore Group, LLC All rights reserved. © SAP AG and The Rushmore Group, LLC 2008

Production Planning & Execution
Version 1.0 Production Planning & Execution January 2007 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 2007 (v1.0) © 2007 by SAP AG. All rights reserved. SAP University Alliance. The Rushmore Group, LLC The Rushmore Group, LLC

High-Level OP Planning
ECC 6.0 January 2008 High-Level OP Planning Planning activities: CO/PA Forecasting Sales and Operations Planning Planning strategy for a product Demand Management Demand management feeds to MPS and in turn MRP/CRP January 2008 © SAP AG - University Alliances and The Rushmore Group, LLC All rights reserved. © SAP AG and The Rushmore Group, LLC 2008

Operations Planning & Execution
Version 1.0 January 2007 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 Sales Info Forecasting CO/PA Sales OP Planning Tactical Planning Demand Management Detailed Planning MPS MRP/CRP Manufacturing Execution/SFC Procurement Process Order Settlement Execution January 2007 (v1.0) © 2007 by SAP AG. All rights reserved. SAP University Alliance. The Rushmore Group, LLC The Rushmore Group, LLC

Costing-Based CO-PA CM: profit margin

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
Inputs: Market, Economic, Other Demand Estimates Forecast Method(s) Sales Forecast Management Team Business Strategy Production Resource Forecasts

Caution for Forecasting
Version 1.0 January 2007 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 Supply chain planning, to a large degree, starts with forecasting. Matching supply and demand is an important goal for most firms and is at the heart of operational planning. It is also of significant importance as the overly optimistic Cisco found in 2001 when it took a $2.2 Billion inventory write-down because of their ability to “forecast demand with near-scientific precision” 1. Since most production systems can’t respond to consumer demand instantaneously, some estimate, or forecast, of future demand is required so that the efficient and effective operational plans can be made. Forecasts are always wrong, but some are “more wrong” than others. Forecasting the demand for innovative products, fashion goods, and the like is generally more difficult than forecasting demand for more “commodity-like” products that are sold on a daily basis. Aggregate forecasts of a group of similar products are generally more accurate than individual forecasts of the individual products that make up the group. Finally, the longer the forecast into the future, the less reliable the forecast will be. January 2007 (v1.0) © 2007 by SAP AG. All rights reserved. SAP University Alliance. The Rushmore Group, LLC The Rushmore Group, LLC

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 Forecast Horizon Time Span Item Being Forecasted
Unit of Measure Long Range Years Product Lines, Factory Capacities Dollars, Tons Medium Range Months Product Groups, Depart. Capacities Units, Pounds Short Range Days, Weeks Specific Products, Machine Capacities 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

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

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.

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

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

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 Students Year Enrolled (1000s) Year Enrolled (1000s)

Simple Linear Regression x y x2 xy Sx=21 Sy= Sx2=91 Sxy=66.5

Simple Linear Regression Y = X

Simple Linear Regression Y7 = (7) = 3.65 or 3,650 students Y8 = (8) = 3.83 or 3,830 students Y9 = (9) = 4.01 or 4,010 students Note: Enrollment is expected to increase by 180 students per year.

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
Multiple regression analysis is used when there are two or more independent variables. An example of a multiple regression equation is: Y = X X2 – 0.03X3 where: Y = firm’s annual sales ($millions) X1 = industry sales ($millions) X2 = regional per capita income ($thousands) X3 = regional per capita debt ($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.

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 a weak positive relationship a strong negative relationship

r is computed by:

Coefficient of Determination (r2)
The coefficient of determination, r2, is the square of the coefficient of correlation. The modification of r to r2 allows us to shift from subjective measures of relationship to a more specific measure. r2 is determined by the ratio of explained variation to total variation:

Example: Railroad Products Co.
Coefficient of Correlation x y x2 xy y2 ,400 1, ,225 1, ,900 1, ,500 1, ,900 2, ,100 3, ,400 3, 1, ,425 15,440 1,287.50

Coefficient of Correlation r = .9829

Coefficient of Determination r2 = (.9829)2 = .966 96.6% 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(syx) Lower limit = Y - t(syx) 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 syx = 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.

Seasonalized Times Series Regression Analysis Representative Historical Data Set Year Qtr. ($mil.) Year Qtr. ($mil.)

Seasonalized Times Series Regression Analysis Compute the Seasonal Indexes Quarterly Sales Year Q1 Q2 Q3 Q4 Total Totals Qtr. Avg Seas.Ind

Seasonalized Times Series Regression Analysis Deseasonalize the Data Quarterly Sales Year Q1 Q2 Q3 Q4

Seasonalized Times Series Regression Analysis Perform Regression on Deseasonalized Data Yr. Qtr. x y x2 xy Totals

Seasonalized Times Series Regression Analysis Perform Regression on Deseasonalized Data Y = X

Seasonalized Times Series Regression Analysis Compute the Deseasonalized Forecasts Y9 = (9) = Y10 = (10) = Y11 = (11) = Y12 = (12) = Note: Average sales are expected to increase by .199 million (about $200,000) per quarter.

Seasonalized Times Series Regression Analysis Seasonalize the Forecasts Seas. Deseas. Seas. Yr. Qtr. Index Forecast Forecast

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

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.

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)

Monitoring Accuracy Mean Squared Error (MSE) MSE = (Syx)2
A small value for Syx 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 syx are related: MSE = 1.25(MAD)

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.

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

Moving Average Representative Historical Data Day Calls Day Calls

Moving Average Use the moving average method with an AP = 3 days to develop a forecast of the call volume in Day 13. F13 = ( )/3 = calls

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. F13 = .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).

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? F12 = (198 – ) = F13 = (159 – ) =

Forecast Accuracy - MAD Which forecasting method (the AP = 3 moving average or the a = .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.)

AP = a = .25 Day Calls Forec. |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

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.

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.

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?

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 Have Forecasting modules

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)
Version 1.0 January 2007 Sales and Operations Planning (SOP) Information Origination Sales Marketing Manufacturing Accounting Human Resources Purchasing Intra-firm Collaboration Institutional Common Sense A more recent proposal to fix forecast errors is to use collaboration. The idea is that if different parts of the supply chain collaborate on a common forecast and everyone plans based on that single forecast; then there is little need for one part of the chain to hedge based on the uncertainty of what is done in other parts of the chain. Intra-firm collaboration, you would think, would be common place – seems that a little common sense would dictate that everyone in a firm come together with a common set of forecast figures. But this is rarely the case. Marketing has a set of forecasts, so too does operations. Sales has their forecast and it’s possible that for budgeting purposes Finance uses still another. The advancement of enterprise resource planning (ERP) systems is helping ensure that there is only one forecast, based upon the principles of a single data repository used by all areas of the enterprise. Forecasts affect most functional areas of the firm and are the starting point for resource allocation decisions. For example, manufacturing must plan production on a day to day basis to meet customer orders, while purchasing needs to know how to align supplier deliveries with the production schedules. Finance needs to understand the forecasts so that the proper levels of investment can be made in plant, equipment, and inventory and so that budgets can be constructed to better manage the business. The marketing function needs to know how to allocate resources for various product groups and marketing campaigns. Forecasts also determine the labor requirements required by the firm so that the human resources function can make proper hiring and training decisions when demand is expected to grow. January 2007 (v1.0) © 2007 by SAP AG. All rights reserved. SAP University Alliance. The Rushmore Group, LLC The Rushmore Group, LLC

Master Data - Product Groups
Version 1.0 January 2007 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 Bikes Touring Mountain 24 Speed 18 Speed Red 24T Blue 24T Red 24M Blue 24M Aggregate forecasts of a group of similar products are generally more accurate than individual forecasts of the individual products that make up the group. January 2007 (v1.0) © 2007 by SAP AG. All rights reserved. SAP University Alliance. The Rushmore Group, LLC The Rushmore Group, LLC

Planning Levels Bikes Touring 24 Speed 18 Speed Red 24T Blue 24T
Version 1.0 Planning Levels January 2007 Bikes 55% Touring 45% Mountain 70% 24 Speed 30% 18 Speed 40% 60% 50% Red 24T Blue 24T Red 24M Blue 24M Planning at Product Group Level Planning at Material Level January 2007 (v1.0) © 2007 by SAP AG. All rights reserved. SAP University Alliance. The Rushmore Group, LLC The Rushmore Group, LLC

Version 1.0 Demand Management January 2007 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) Product Groups Bikes 45% Mountain 55% Touring Disaggregation Aggregating forecasts across multiple items reduces forecasting errors. A clothing store, for instance, might be able to estimate within a pretty narrow range what the demand will be for men’s dress shirts. But when that store tries to estimate the demand for individual styles, colors, and sizes of shirts, the accuracy of their forecasts will be considerably worse. Firms handle this kind of forecasting problem usually in one of three ways; they either forecast from the bottom up, from the top down, or they start in the middle and work both up and down. The “top down” forecast essentially estimates total sales demand and then divides those sales dollars level by level until the stock keeping unit (SKU) is reached. The “bottom up” method, as one might expect, starts with forecasts at the SKU level and then aggregates those demand estimates level by level to reach a company–level forecast. Another method, one might call the “in-between” method, starts forecasts at the category level (like men’s dress shirts), and then works up to determine store sales and works down to divide up the forecast into styles, colors and SKUs. 30% 18 Speed 70% 24 Speed 40% 18 Speed 60% 24 Speed 40% Red 24M 60% Blue 24M 50% Red 24T 50% Blue 24T Material January 2007 (v1.0) © 2007 by SAP AG. All rights reserved. SAP University Alliance. The Rushmore Group, LLC The Rushmore Group, LLC

Demand Management Forecast Sales Planned Independent Requirements
Version 1.0 Demand Management January 2007 Forecast Sales Planned Independent Requirements Customer Independent Requirements Demand Program MPS / MRP January 2007 (v1.0) © 2007 by SAP AG. All rights reserved. SAP University Alliance. The Rushmore Group, LLC The Rushmore Group, LLC

Transfer from High Level to Detailed Planning
Version 1.0 January 2007 Transfer from High Level to Detailed Planning Bikes 55% Touring 45% Mountain 70% 24 Speed 30% 18 Speed 40% 60% 50% Red 24T Blue 24T Red 24M 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 2007 (v1.0) © 2007 by SAP AG. All rights reserved. SAP University Alliance. The Rushmore Group, LLC The Rushmore Group, LLC

Creation of The Demand Program Production Plan
ECC 6.0 January 2008 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 January 2008 © SAP AG - University Alliances and The Rushmore Group, LLC All rights reserved. © SAP AG and The Rushmore Group, LLC 2008

Enterprise Strategic Planning SAP Implementation EGN Enterprise Systems Integration (Professional MSEM) Fall, 2011

Version 1.0 Planning Strategies January 2007 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 Planning strategies represent the business procedures for the planning of production quantities and dates. A wide range of production planning strategies are available in the SAP R/3 System, offering a large number of different options ranging from pure make-to-order production to make-to-stock production. Depending on the strategy you choose, you can: Use sales orders and/or sales forecast values to create the demand program Move the stocking level down to the assembly level so that final assembly is triggered by the incoming sales order Carry out Demand Management specifically for the assembly January 2007 (v1.0) © 2007 by SAP AG. All rights reserved. SAP University Alliance. The Rushmore Group, LLC The Rushmore Group, LLC

Version 1.0 Planning Strategies January 2007 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 Planning strategies represent the business procedures for the planning of production quantities and dates. A wide range of production planning strategies are available in the SAP R/3 System, offering a large number of different options ranging from pure make-to-order production to make-to-stock production. Depending on the strategy you choose, you can: Use sales orders and/or sales forecast values to create the demand program Move the stocking level down to the assembly level so that final assembly is triggered by the incoming sales order Carry out Demand Management specifically for the assembly January 2007 (v1.0) © 2007 by SAP AG. All rights reserved. SAP University Alliance. The Rushmore Group, LLC The Rushmore Group, LLC

Planning Strategy for Make-to-Stock
Version 1.0 January 2007 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, LLC The Rushmore Group, LLC

Planning Strategy for Make-to-Order
Version 1.0 January 2007 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, LLC The Rushmore Group, LLC

Forecasting set up in SAP
Version 1.0 Forecasting set up in SAP January 2007 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, LLC The Rushmore Group, LLC

Sales and Operations Planning (SOP)
Version 1.0 January 2007 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, LLC The Rushmore Group, LLC

Statistics Graphics Version 1.0 January 2007 The Rushmore Group, LLC
January 2007 (v1.0) © 2007 by SAP AG. All rights reserved. SAP University Alliance. The Rushmore Group, LLC The Rushmore Group, LLC

Exercises: Review material status for finished products
Review bill of materials for executive pen set Display multi-level bill of materials for ESET Review routing for assembly EPEN Review Routing/BOM in the engineering workbench Review work center and assigned capacity Create consumption values for finished products Create material master for finished products Create bill of material Create finished products routing Create product group Create sales and operations plan Transfer SOP to demand management Review demand management Run MPS with MRP Review stock/requirement list

High-Level OP Planning and Demand Management EGN 5622 Enterprise Systems Integration (Professional MSEM) Fall, 2011.

Similar presentations

Presentation on theme: "High-Level OP Planning and Demand Management EGN 5622 Enterprise Systems Integration (Professional MSEM) Fall, 2011."— Presentation transcript:

Similar presentations

About project

Feedback

Log in

Auth with social network:

High-Level OP Planning and Demand Management EGN 5622 Enterprise Systems Integration (Professional MSEM) Fall, 2011.

Similar presentations

Presentation on theme: "High-Level OP Planning and Demand Management EGN 5622 Enterprise Systems Integration (Professional MSEM) Fall, 2011."— Presentation transcript:

Similar presentations

About project

Feedback