Configuration and Usage of Demand Sensing. February, 2016

Transcription

1 Configuration and Usage of Demand Sensing February, 2016

2 Objectives Slide At the end of this lesson, you will be able to: Explain the demand sensing functionality within SAP Integrated Business Planning for demand 6.1 in detail. List the key features of short-term demand forecasting Understand the prerequisites for short-term demand forecasting Customer 2

3 Agenda Create a forecast model for demand sensing Manage snapshot configuration Load historic snapshot data and schedule snapshot jobs Run demand sensing from the SAP IBP add-in for Microsoft Excel Customer 3

4 Create a Forecast Model for Demand Sensing

5 Create a New Forecast Model for Demand Sensing Set up a new forecast model for the demand sensing algorithm via the Manage Forecast Models app. On the next screen, click Create in the lower right corner and select your planning area from the appearing list. Customer 5

6 Create a New Forecast Model for Demand Sensing Time Settings Mandatory: Periodicity needs to be set to Daily for the demand sensing algorithm The periods are entered in days when the periodicity is set to Daily. Historical Periods: As best practice from past customer projects, we recommend using 52 or more weeks as historic data input. This showed very good demand sensing results. Forecast Periods: This depends on the business case. Demand sensing is usually used in a forecasting range of 4-8 weeks. There is a minimum of historical periods that needs to be used in order to expect good results. This depends on your overall settings. The UI will give recommendations upon saving the model when the number you entered is too small. Customer 6

7 Create a New Forecast Model for Demand Sensing Preprocessing Step Promotion Sales Lift Elimination (optional) As of SAP Integrated Business Planning for demand 6.1, you will find a new preprocessing step called Promotion Sales Lift Elimination. This step needs to be used in combination with Demand Sensing (Full) in case you have promotional sales lift information in your system. Preprocessing steps other than Promotion Sales Lift Elimination do not work with Demand Sensing algorithms. The Promotion Sales Lift Elimination preprocessing step is not limited to Demand Sensing in its usage. It can for example also run standalone. Customer 7

8 Create a New Forecast Model for Demand Sensing Preprocessing Step Configuration of Promotion Sales Lift Elimination Outlier Multiplier: A decimal number by which the system is to multiply the range of accepted values when the variance test is used, thus including additional values in the range or excluding a set of values from it. Sales History: Key Figure that contains the Sales History including the sales lift from the promotions (total). For Demand Sensing, this is usually the Confirmed Quantity or the Delivered Quantity. Planned Sales Lifts: Key Figure that contains the planned promotion uplifts or downlifts in the history and future. In case of SAP6, this is PROMOSPLIT ( Promotion Uplift ) Consensus Forecast: Key Figure that contains the Consensus Forecast excluding the sales lift from promotions! In case of SAP6, this is CONSENSUSDEMAND ( Consensus Demand w/o Promotions ) Save Results In: Output Key Figure in which the results should be stored. This would provide the Sales History exluding the promotions Customer 8

9 Excurse: Promotion Sales Lift Elimination Preprocessing Step Identify outliers (sales lifts) associated with promotions, and to remove them from the sales history Step1 Step2 Step3 Step4 Step5 Step6 Identifies outliers within the sales history with Outlier Detection Logic. Range for outlier detection is calculated based on Variance Test. It detects both positive and negative outliers. Result: Outlier Periods Identifies positive sales lifts, caused by promotions in the sales history where promotion quantity is greater than zero. Result: Promotion Periods Searches for correlations to identify the periods when the outliers were actually caused by promotions Eliminates the values from the sales history where a correlation was found between outlier and promotion periods Looks for closest match to adjust values proportionally, in case no exact correlation can be detected Stores the results for sales history, cleansed by the sales lifts in the output key figure Customer 9

10 Create a New Forecast Model for Demand Sensing (Full) Forecasting Steps (1) Main Input for Forecasting Step: Key Figure that contains the data that acts as the main sales history. For Demand Sensing, this is usually the Confirmed Quantity or the Delivered Quantity. Note that the Main Input for Forecasting Steps needs to be the sames as Either one of the inputs for Historical Ordered Quantity or Delivered Quantity for the Demand Sensing (Full) Model. Input for Algorithm on the Preprocessing Step for Promotion Sales Lift Elimination Customer 10

11 Excurse: Demand Sensing Full and Update Runs Demand Sensing can be run in full and update modes. A Full Run Is normally scheduled at the beginning of a week to get the regression weights. In most cases, a full run in demand sensing would follow an updated weekly midterm forecast and, subsequently, a snapshot that has been taken based on that. These outputs are then used in the final sensed demand calculation. An Update Run Is scheduled whenever there are delta changes in the inputs (new open orders/shipments) during the week. Thus, it takes into account any new information for orders/shipments during the week and then calculates the sensed demand based on the regression weights obtained as part of the full run that was done at the beginning of the week. An update run cannot be scheduled until and unless a full run is already done. Customer 11

12 Create a New Forecast Model for Demand Sensing (Full) Forecasting Steps (2) Add the Demand Sensing (Full) algorithm to the model Please refer to the naming conventions for certain key figures that are used as input for demand sensing. This is described in detail in the SAP6 Planning Model slide deck. Customer 12

13 Create a New Forecast Model for Demand Sensing (Full) Forecasting Steps (3) Key Figures Consensus Forecast: Needs to be in sync with the same entry on the Preprocessing Step for Promotion Sales Lift Elimination. In SAP6, this is CONSENSUSDEMAND ( Consensus Demand w/o Promotions ). This key figure should not contain promotion information! Future Ordered Quantity: Key Figure that contains the open orders for the future horizons. In SAP6, this is FUTUREORDEREDQTY ( Future Ordered Quantity ) Historical Ordered Quantity: Key Figure that contains the Sales History. Usually, this is the Confirmed Quantity. In SAP6, this is CONFQTY ( Confirmed Quantity ) Delivered Quantity: Key Figure that contains the delivered quantities from the past weeks. In SAP6, this is ADJDELIVQTY ( Delivered Qty Adjusted ). Customer 13

14 Create a New Forecast Model for Demand Sensing (Full) Forecasting Steps (4) Further Settings Consensus Demand Snapshot Suffix: Needs to match the suffix that is used when creating the forecast snapshot (see chapter 2) Bias Horizon: Bias in SAP s Demand Sensing can be better defined as the consumption or ordering patterns of the customers. It determines or predicts what the future consumption looks like, based on calculated weights from the past buckets. Bias Horizon as in the screenshot is an example and varies depending on your individual data. Maximum is 10 as the highest bias horizon entry. The last 2 entry fields can also be left blank in case that is needed for the specific data set. These four values are used for capping the final sensed demand numbers. See next slides for more information. These thresholds are checking the result of the Demand Sensing Run (before putting uplifts back) against the Consensus Forecast without promotions. Daily Average Calculation Horizon: Rolling window (number of weeks in the past) over which we average daily shipments for every Monday, Tuesday,, Sunday of the weeks. In this case, for the last 4 weeks. Customer 14

15 Threshold Settings The maximum forecast increase / decrease settings are used to cap the demand in case of exceptions in the data and smooth the results. The user can define absolute and percentage values. The threshold settings cap the outcome of DS before the order adjustment. It is a global threshold for all planning objects that are planned with the forecast model, and thus has to be set very carefully. In case you have items with very different volumes, one might think of creating different sensing profiles to avoid setting too aggressive or too limiting thresholds. Example: Max Forecast Increase (absolute value): 50 PC (MAX_INCREASE) Max Forecast Increase (percentage value): 30% (MAX_PCT_INCREASE) The consensus demand is 100 PC (CONSENSUSDEMAND) The sensed demand that would be calculated without taking the forecast increase factors into account is 300 PC UPPER_BOUND = Max (100+50, 100*1.3) => Max (150,130) = 150 PC So, if the final sensed demand numbers calculated during the sensed demand run are higher than 150, then they are capped. Sensed Demand (300 PC) > UPPER_BOUND (150 PC) => Sensed Demand = 150 PC Customer 15

16 Example: Daily Average Calculation Horizon The Daily Average Calculation Horizon is a rolling window (number of weeks in the past) over which the demand sensing averages daily shipments to determine the daily shipment profile. In this case, for 4 weeks into the past, the shipments for each day of the week are averaged. For last four Mondays, Tuesdays etc. This in turn is used in calculating the shipment profile of each day. Lower numbers than 4 weeks will influence the results in a negative way. We recommend using a value equal or higher than 4. The default value is therefore set to 4. Customer 16

17 Create a New Forecast Model for Demand Sensing (Full) Forecasting Steps (5) Workdays Select Workdays: You can control the calculation of the sensed demand with the workday selection. By this, the system will not determine any quantities from the consensus forecast for the unmarked days. However, in case there are quantities booked for these days as part of sales orders (confirmed qty), these will still show up as sensed demand later on. Customer 17

18 Create a New Forecast Model for Demand Sensing (Full) Forecasting Steps (6) Minimum Data Points and WMAPE Threshold Minimum Data Points: It is the minimum number of weeks of historical data (forecast, open orders, deliveries) that the demand sensing algorithm needs to recommend reliable short term forecasting results. 15 weeks are a recommended value based on the experience from past projects. Baseline WMAPE threshold: The system calculates the WMAPE (Weighted Mean Absolute Percentage Error) results for the Consensus Forecast (based on the snapshot input). For certain planning objects (product/location/customer), the consensus forecast might already compute the best forecast. This threshold value is therefore used to determine whether or not the Demand Sensing algorithm could improve the results or not. So if the calculated WMAPE for the Consensus Forecast is less below this threshold, then the demand sensing algo will not run and the Consensus Forecast values are taken. The calculation is done per individual planning object. The WMAPE calculation results are not stored. Customer 18

19 Create a New Forecast Model for Demand Sensing (Update) Forecasting Steps Add the demand sensing (update) algorithm to the model Customer 19

20 Create a New Forecast Model for Demand Sensing Postprocessing Steps Error measures are not calculated during the demand sensing run Customer 20

21 Manage Snapshot Configuration

22 Introduction What is a Snapshot? A snapshot is the current picture of any time key figure for any time range. In case of demand sensing, it is for the mid-term forecast (e.g. consensus demand) A forecast snapshot is needed by the demand planners to review how the forecast changed over a period of time to learn the patterns of demand and correlate forecast with demand for different lags Historical snapshots are needed by the demand sensing algorithm in the calculation of the sensed demand regression metrics. Note that it is mandatory to load historic snapshot data. It is a mandatory input factor to the demand sensing algorithms. Historical forecast snapshots are loaded manually before the first demand sensing run. After the first run, the snapshot creation needs to be scheduled on a regular basis via the SAP IBP add-in for Microsoft Excel (e.g. weekly or monthly, depending how often your mid-term demand plan is generated) Customer 22

23 Generate Snapshot Key Figure for demand sensing A new snapshot key figure is automatically generated for your planning area when a snapshot configuration is saved (see next slides) This snapshot key figure stores the snapshot values when a snapshot is taken on a regular basis. is used to save the historical snapshot values during the initial system set up. Note that historic snapshot values are needed by the demand sensing algorithm to calculate high-quality results. So a certain snapshot history needs to be loaded during the initial system setup (see chapter 3) and while productively using demand sensing, the snapshot creation needs to be scheduled regularly. Customer 23

24 Generate Snapshot Key Figure for demand sensing Productive use of demand sensing Time Load historic snapshot data for a certain historic timeframe at this point and up to this point Generate snapshots regularly (e.g. weekly) via the SAP IBP add-in for Microsoft Excel Customer 24

25 Manage Snapshot Configuration (1) Create a new snapshot key figure via the Manage Snapshot Configuration UI: On the next screen, select your planning area via the dropdown list. Customer 25

26 Manage Snapshot Configuration (2a) Mandatory: Snapshop Type = Change History REV Suffix: Make sure it is the same suffix that is also entered in the Forecast Model Mandatory: Use the same planning level on which the input key figure is defined. In this case: LOCPRODCUSTWEEKLY (Location Product Customer Weekly level) Forecast Model: Mandatory:select the key figure that is defined as the Consensus Forecast in your Forecast Model (see slide 13) e.g. consensus demand without promotions or statistical forecast qty Customer 26

27 Manage Snapshot Configuration (2b) REV How to calculate the To timeframe: The amount of data that needs to be captured within the snapshots for demand sensing depends on certain factors. The following formula should be used to calculate the snapshot future horizon that is needed for demand sensing: To = [SensingHorizon + Snapshot Frequency -1] * Sensing Horizon in weeks should represent the number that you entered in the Forecast Model at Forecast Periods for your respective demand sensing model: This is the range of periods that the snapshot should be taken for. These numbers represent days. From = 0 means that the snapshot will be taken always from the current day, the snapshot run is executed, onwards. E.g. if the snapshot is scheduled every Monday, the data included in the snapshot starts from that respective Monday onwards. To = 63 means that the snapshot will be taken for the next 63 days (9 weeks) in the future. In this case, 42 days would translate to 6 weeks Snapshot Frequency in weeks depends on whether you want to take the snapshots weekly, monthly, etc. for weekly snapshots use 1, bi-weekly use 2, for monthly snapshots use 4 as input. The frequency depends on how often your midterm forecast (e.g. consensus demand) is being updated. At least one snapshot should be taken every four weeks! Example: Sensing Horizon = 6 weeks Snapshot Frequency = monthly To = [ ] * 7 = 63 days Customer 27

28 Manage Snapshot Configuration (3) Once you save the snapshot configuration, a new key figure will be generated for the respective planning area. Note that the planning area is set to Inactive now, so you need to reactivate it. Customer 28

29 Load Historical Snapshot Data and Schedule Snapshot Jobs

30 Historical Snapshot Upload Prerequisites It is mandatory to have a Snapshot Key Figure as defined in chapter 2 and at least one data point loaded for this snapshot key figure to be able to run demand sensing at all. Please note that high qualitative results in demand sensing can only be achieved with enough historic snapshot information loaded (we recommend 52 snapshot revisions - see chapter 2 for more information) Procedure: Create a new snapshot key figure for the planning area Check and adjust mandatory settings in the planning area Pay attention to prerequisites during data upload of master data and key figures Load Statistical Forecast qty/ Consensus Demand Plan qty for the past periods that you want to use as historical periods in your forecast model. Either load Statistical Forecast qty / Consensus Demand Plan qty for the future (at least one data point is needed) or generate it via a statistical forecast run. Load the historical forecast snapshots before the first demand sensing run Customer 30

31 Mandatory Settings in Planning Area Change History Change History: must be Planning Area Enabled Prerequisite for demand sensing as the latter requires a snapshot key figure Snapshot key figures can only be defined for planning areas with change history enabled Customer 31

32 Prerequisites for Master Data Upload Make sure to load the master data in the correct order. First, the data for the master data of type simple, then compound, then reference (if available), e.g., in SAP6: MDT Simple 1 Time Profile MDT Compound 2 Location, Product, Customer 3 LocationProduct (optional) 4 Currency (optional) 5 Exchange Rate (optional), Currency To 6 UoM To 7 UoM Conversion 8 Sales Order 9 Delivery 10 Promotions 11 Key Figure Data (e.g., Consensus Demand, Delivered Quantity, etc) Important: many key figures are now stored in technical weeks. If you upload data for such key figures you should either upload the data also in technical weeks or you upload it aggregated by the time, e.g. on calendar week or monthly level, by using the new Time Profile Level within uploading (new with IBP 6.1): 12 Historical Snapshot Data for Consensus Demand Quantity Customer 32

33 Upload the Historical Snapshot via the WebUI Create a new import job of data type Snapshots to upload your historical snapshot file. Customer 33

34 Sample Snapshot File Forecasted periods within revision (14 weeks in this example) Forecasted QTY Revision of April 20, 2015 Revision of April 27, 2015 Customer 34

35 How Many Historical Snapshots Should Be Loaded? The amount of weekly snapshot history to be loaded should be equal to the Historical Periods value (in weeks!) that you entered in the time settings of your forecast model. E.g. If the Historical Periods value is 280 in your forecast model, that would be 40 weeks and in this case, weekly snapshots for the last 40 weeks should be loaded. Note that the quality of the results will decrease in case you load less snapshot data (in this case less than 40 weeks). Loading more data will not produce better the results! Customer 35

36 Troubleshooting Error: Cannot import data because period & planning object data doesn t exist for the selected revision, Invalid Revision for Planning Object or Period. Solution: This means the UoM (Unit of Measure) templates are not imported or imported in the wrong order. So, reload all master data types that have Attributes as key figures and then reimport the snapshots (see the Prerequisites during Data Upload slide). In the SAP6 template, there are two Attributes as key figures used for UoM and currency conversion: UOMCONVFACTOR EXCHANGERATE Customer 36

37 Set up Batch Job to Automatically Create New Snapshots for Future Periods (1) Schedule the snapshot operation to run automatically via the SAP IBP add-in for Microsoft Excel Customer 37

38 Set up Batch Job to Automatically Create New Snapshots for the Future Periods (2) Customer 38

39 Run Demand Sensing from the SAP IBP add-in for Microsoft Excel

40 Demand Sensing Run Demand Sensing can be run in two modes: Batch mode (as a background job) Simulation mode (directly in MS Excel) Customer 40

41 Run Demand Sensing in Batch Mode(1a) Log on to your respective planning area in the SAP IBP add-in for Microsoft Excel and create a daily or a weekly view. Note: Demand sensing is always run on product location customer level. So in order to see the demand sensing forecast model, you need to select the data on this level. Customer 41

42 Run Demand Sensing in Batch Mode(1a) Optional step: Filter based on any attribute (e.g. Product ID, Product Group, etc). Note: The demand sensing algorithm will then only run on the filtered subset of data. Customer 42

43 Run Demand Sensing in Batch Mode(1a) Pick an appropriate Unit of Measure for the conversion of these key figures Customer 43

44 Run Demand Sensing in Batch Mode(2a) Start the full demand sensing job after picking the forecast model that includes your demand sensing algorithm Monitor Job Status Customer 44

45 Run Demand Sensing in Simulation Mode(1b) Log on to your respective planning area in the SAP IBP add-in for Microsoft Excel and create a daily view. Note: The demand sensing algorithm will only run on the filtered subset of data in case filters were applied. Simulation of DS can only happen on a Daily View since the DS algorithms are always set to a periodicity of Daily in the forecast model (Slide 12) Customer 45

46 Run Demand Sensing in Simulation Mode (2b) Run demand sensing from the Simulate drop down after picking the forecast model that includes your demand sensing algorithm. Customer 46

47 Demand Sensing Run (3) In comparison to the mid-to long-term statistical algorithms, there are some differences for demand sensing (full and update) algorithms. Demand Sensing Models are run on Location Product - Customer level only à only visible in the forecast model list if the corresponding IDs are selected attributes are run for the baseline version only à the other versions are disabled and baseline is preselected automatically Time Period is set to daily. Customer 47

48 Demand Sensing (Full) Outputs (1) Demand sensing outputs show up once the job is completed successfully and the data is refreshed from the database (Click Refresh ). Customer 48

49 Run Demand Sensing (Update) 1 Situation: Future ordered quantities (Current Open Order QTY) were modified in the middle of a certain week. In this example, new orders came in during the middle of the week and the current open order quantity was increased from 340 to 600 pieces. Customer 49

50 Run Demand Sensing (Update) 2 Re-run demand sensing and observe the new outputs. In such a case, we would choose the demand sensing (update) algorithm Pick the demand sensing update forecast model that you created earlier Customer 50

51 Lesson Summary You should now be able to Explain the demand sensing functionality within SAP Integrated Business Planning for demand 6.1 in detail. List the key features of short term demand forecasting Understand the prerequisites for short term demand forecasting Customer 51

52 2015 SAP SE or an SAP affiliate company. All rights reserved. No part of this publication may be reproduced or transmitted in any form or for any purpose without the express permission of SAP SE or an SAP affiliate company. SAP and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered trademarks of SAP SE (or an SAP affiliate company) in Germany and other countries. Please see for additional trademark information and notices. Some software products marketed by SAP SE and its distributors contain proprietary software components of other software vendors. National product specifications may vary. These materials are provided by SAP SE or an SAP affiliate company for informational purposes only, without representation or warranty of any kind, and SAP SE or its affiliated companies shall not be liable for errors or omissions with respect to the materials. The only warranties for SAP SE or SAP affiliate company products and services are those that are set forth in the express warranty statements accompanying such products and services, if any. Nothing herein should be construed as constituting an additional warranty. In particular, SAP SE or its affiliated companies have no obligation to pursue any course of business outlined in this document or any related presentation, or to develop or release any functionality mentioned therein. This document, or any related presentation, and SAP SE s or its affiliated companies strategy and possible future developments, products, and/or platform directions and functionality are all subject to change and may be changed by SAP SE or its affiliated companies at any time for any reason without notice. The information in this document is not a commitment, promise, or legal obligation to deliver any material, code, or functionality. All forward-looking statements are subject to various risks and uncertainties that could cause actual results to differ materially from expectations. Readers are cautioned not to place undue reliance on these forward-looking statements, which speak only as of their dates, and they should not be relied upon in making purchasing decisions. Customer 52