Getting the Most out of Statistical Forecasting! Author: Ryan Rickard, Senior Consultant Published: July 2017
About SCMO 2 Founded in 2001, SCMO2 Specializes in High-End Supply Chain Consulting Work Focused on The Implementation and Better Use of SAP Applications, Including ERP ECC & S/4, SCM APO & IBP on HANA, Ariba, Among Others Featured in Publications and Regularly Present at SAP Conferences Globally, like SAP Insider, SAPPHIRE NOW and ASUG Annual Conference. Partnered with SAP s Supply Chain Group to Deliver Informative Sessions on Latest Tools and Functionality, like SAP Integrated Business Planning. Partnered with SAP Insider to Deliver Multi-Day Bootcamp Seminars. Company Statistics Delivering Strategic, Implementation, Enhancement, Migration/Upgrade and Outsourced Support Services across SAP s Execution and Supply Chain Planning Suite Including and Not Limited to: ERP ECC & S/4 HANA, SCM APO, IBP on HANA, SCIC/Control Tower, SNC, EIS (SmartOps), S&OP Powered by HANA and Ariba US-based Platinum Level Supply Chain Consultants With Deep Expertise in Both the Technical Tools and Functional Business Processes Delivering Projects and Services Across 20+ Different Countries in North and South America, Europe and Asia Since Our Inception 15+ Years Ago
Forecasting is a Core Competency We already offer programs specific to Demand Planning and S&OP
Session Leader Meet Ryan Rickard Ryan Rickard Senior Consultant 17 years Experience in Supply Chain Planning, Including Working as a Planner, IT Resource, and as a Business Process Re-design Lead Demand Planning and Statistical Forecasting Specialist in APO-DP and IBP-Demand Frequent Speaker at Many Premier Supply Chain Events Contact Info: Ryan Rickard, Sr. Consultant rrickard@scmo2.com (770) 639-7285 Follow SCMO2: www.scmo2.com www.facebook.com/scmo2/ www.twitter.com/breatheinscmo2
Q&A If you have questions throughout today s Webinar feel free to click on the Q&A window and type your question At the end of the Webinar we will attempt to address as many questions as possible, as time allows
Webinar Series Getting the Most out of Statistical Forecasting! A multi-series webinar to explain How to Effectively Analyze & Model your Demand Session 1 Session 2 Session 3 Session 4 Variability Matters Calculating Variability & Segmenting to help drive the process How Much is Enough? How much Historical Data is Enough? How frequent to Run (Stat) & React? Super Model Forecasting The Optimal Level of Aggregation Weeks vs. Months can make a Difference Using the Tool to Find the Best Model FVA: The New Frontier Understanding how Forecast Value Add can enhance your forecasting value
Session 1 Recap Variability Matters All products are not the same. Their DNA and patterns are different. A good (process, approach and models) design is only possible when we understand variability. Calculating Variability can be done using the Coefficient of Variation methodology in Excel or APO/IBP Using different time ranges of data can change the Variability output and Stat modeling approach Zeros matter in the CoV calculation Variability correlates to Forecastability Can I expect 100% FA?
Understanding Variability is Important Understanding the Variability of your business and products will help you design the best forecasting process and organization The lower or more detailed you get with both your analysis and planning, the more variability you will have In our examples from Session 1 we analyzed data at the Product level. Going to Product/Customer or Product/Customer/Location will add variability and complexity in planning accuracy. Understanding the Variability of your data will help you apply the best algorithms and periods of history to optimize the Statistical Forecast output
Session 2 How Much is Enough
How Much Data Should I Use? 0 500 1000 1500 In most cases, the MORE data you have the BETTER The algorithms have the potential to model patterns better using more data points Identifying Seasonality and Trends is more effective and accurate using more data and multiple Seasons For example, do spikes in historical demand happen in the same periods each year clearly reflecting seasonality? Seasonal high spikes occurring in the same months each year Seasonal low valleys occurring in the same months each year
How Much Data Should I Use? But sometimes the pattern 3 years ago isn t representative of the pattern recently Did we lose a big customer? Are we having Operational Issues which are impacting Inventory Availability and Order Fulfillment? Is this item Discontinuing? Only someone with business intelligence is going to know the answer Simply letting the system use all the data you will most likely get the wrong result
Should I use Weekly or Monthly Data? Do you have Weekly Historical Data? Does your business process allow for Weekly forecast changes and releases to Supply Planning? Can you forecast in Weekly buckets? Are you just beginning this analysis? If you have Weekly and Monthly data you should consider analyzing both If you are just beginning this analytical exercise, start with Monthly If you don t current forecast in Weekly buckets, then no need to analyze Weekly data to begin There will be patterns in Weekly data that may not appear when aggregated Monthly
Time Bucket Profiles (example) History in weekly buckets looking back 3 years You can clearly see that this customer orders weekly, but not every week.
Time Bucket Profiles (example) The same history in monthly buckets looking back 3 years
How Much Data Do You Have? The number of historical data points is important! And it s important for each forecasting combination Products will have different data point counts than Product/Customers will. Start simple! As with Variability, not all Products have the same amount of History Expecting a new sku to behave like a mature sku with many historical data points isn t ideal So, how do we know? A simple way is to analyze in Excel!
Analyzing Periods of History Collect several years of historical data into Excel Fill in null values with zeros after the first data point Count the periods which have a value (numbers and zeros) Consider also counting: Periods with Zeros Recent periods with Zeros (to help identify discontinuations) Before you Collect and Analyze your data, it s important to consider: What Planning Level you want to analyze your data (Product, Product/Customer) What Time Bucket level you want to analyze your data (Months, Weeks) The counts will be different, and the analysis should correlate to the levels that you plan to Statistically Forecast
Analyzing Periods of History In our Demo which have collected 36 months of data at the Product level to analyze (9800+ products) 36 35 34 33 32 31 30 29 28 27 26 25 24 23 22 21 20 Product ID Key Figure Mar-14 Apr-14 May-14 Jun-14 Jul-14 Aug-14 Sep-14 Oct-14 Nov-14 Dec-14 Jan-15 Feb-15 Mar-15 Apr-15 May-15 Jun-15 Jul-15 102043381 Actuals Qty 1 100721958 Actuals Qty 2 4 4 4 2 2 102200983 Actuals Qty 2 2 100753892 Actuals Qty 9 18 2 32 7 12 4 1 11 5 55 9 5 9 11 1 101194758 Actuals Qty 101187020 Actuals Qty 100733611 Actuals Qty 16 101194072 Actuals Qty 15 15 15 15 15 15 15 100754660 Actuals Qty 1 2 101019867 Actuals Qty 11 7 100753623 Actuals Qty 2 2 1 102176535 Actuals Qty 100753199 Actuals Qty 101096516 Actuals Qty 100 100 100 100 100 200 100 200 100 100 101186231 Actuals Qty 101096915 Actuals Qty 175 1 101195443 Actuals Qty 101662624 Actuals Qty 52 50 51 36 52 51 16 112307867 Actuals Qty 101192071 Actuals Qty 2 102026148 Actuals Qty 80 76 40 78 105008243 Actuals Qty 101199607 Actuals Qty 72 72 72 72
Analyzing Periods of History Counting and knowing how many data points each forecasting combination has is important to help to determine what type of forecast algorithms might be applicable For example, if you don t have a seasons worth of data, then running models that check and apply seasonality is not going to produce proper results When you have more than a year of data you can test and apply more possible models (i.e. Seasonal, Seasonal Linear Regression, Seasonal Trend) If you have a lot of zeros in your data, you ll know early that a Sporadic or Constant type model will be best
Fill Blanks with Zeros For each record, after the first real data point, fill blank values with Zeros Using an Excel Macro you can populate with zeros Open the Macro Highlight the data range Run the Macro
Fill Blanks with Zeros You ll see each gap or null value after the first real data point is now filled with a zero
Calculating Periods of History Manually For all 36 months, calculate the number of periods with data using the COUNT formula in Excel =COUNT(range) For all 36 months, calculate the number of periods with Zeros using the COUNTIF formula in Excel =COUNTIF(range,0) This helps us understand how many zeros/gaps are in the total data set. In this example, we have 14 periods of data, but we now know that 10 of the 14 are zeros. Meaning we have a sporadic item which we can model accordingly.
Calculating Periods of History Manually For the most recent 12 months, calculate the number of periods with Zeros using the COUNTIF formula in Excel =COUNTIF(range,0) This helps us look for items that have been potentially discontinued to determine which we do NOT need to generate a Statistical Forecast for. Instead of using the last 12 months you could also use the last 6 for example. You may have an attribute with Product Status information that can be used to parse discontinued items. If you are using Lifecycle Planning within APO or IBP you can reference items which you ve already Phased-Out. This count of last periods with zeros does though often highlight items that should be Phased-out.
Calculating Periods of History Manually Calculate again using 24 months of historical data Calculate number of periods with data Calculate number of periods with zeros Calculate again using 12 months of historical data
Calculating Periods of History Manually Copy the formulas down for each Product Product ID Key Figure 36M Count 36M Zeros Last 12 Zero? 24M Count 24M Zeros 12M Count 12M Zeros 102176535 Actuals Qty 14 10 10 14 10 12 10 100759029 Actuals Qty 35 14 6 24 8 12 6 100721807 Actuals Qty 25 19 9 24 19 12 9 100721121 Actuals Qty 36 27 7 24 18 12 7 102199292 Actuals Qty 33 26 9 24 20 12 9 100990897 Actuals Qty 36 20 9 24 12 12 9 101020203 Actuals Qty 36 16 8 24 13 12 8 100754130 Actuals Qty 34 24 10 24 21 12 10 100754255 Actuals Qty 30 23 8 24 19 12 8 101019910 Actuals Qty 33 23 7 24 15 12 7 101019447 Actuals Qty 33 25 9 24 21 12 9 101020508 Actuals Qty 35 28 9 24 19 12 9 100721930 Actuals Qty 32 17 9 24 14 12 9 100733933 Actuals Qty 17 12 9 17 12 12 9 101693540 Actuals Qty 36 7 3 24 5 12 3 101190858 Actuals Qty 36 25 9 24 18 12 9 100721554 Actuals Qty 36 23 7 24 18 12 7 101191515 Actuals Qty 34 21 9 24 16 12 9 101020452 Actuals Qty 35 27 9 24 20 12 9 100729459 Actuals Qty 36 10 2 24 6 12 2 102081879 Actuals Qty 31 24 9 24 18 12 9 100722415 Actuals Qty 33 26 10 24 20 12 10 100754546 Actuals Qty 34 5 2 24 5 12 2 101020594 Actuals Qty 36 26 9 24 18 12 9 101975545 Actuals Qty 34 22 7 24 16 12 7 101187208 Actuals Qty 33 23 9 24 17 12 9 102201632 Actuals Qty 36 14 3 24 9 12 3 101020135 Actuals Qty 32 25 7 24 18 12 7
Analyzing Periods of History Mark as Stat or No Stat Which Items do we know that we do not want to generate a Stat for? Which are discontinued? By filtering on our column labeled Last 12 Zero, we can select which items to exclude, or mark as No Stat. An easy place to start is all items with all 12 of the last 12 periods as zeros. This means that in the last year there has been NO sales. Product ID Key Figure Last 12 Zero? 24M Count 24M Zeros 12M Count 12M Zeros Stat or No Stat 101191560 Actuals Qty 12 24 22 12 12 No Stat 104382256 Actuals Qty 12 24 20 12 12 No Stat 102036736 Actuals Qty 12 24 21 12 12 No Stat 101096119 Actuals Qty 12 24 18 12 12 No Stat 101591739 Actuals Qty 12 24 17 12 12 No Stat 101020911 Actuals Qty 12 24 18 12 12 No Stat 1627 Product with No Sales the last 12 months
Analyzing Periods of History Which items have less than a year of history? Which have more than a year? Which can you test Seasonal algorithms on? Which are new and don t have a season worth of data? Although you can run Seasonal models with 12 monthly (or 52 weekly) data points, you really should have 18. (To be able to index at least 6 periods twice) Filtering on column 36M Count, select/show all products with 17 or less periods of data. Product ID Key Figure 36M Count Stat or No Stat 102176535 Actuals Qty 14 < Year 100733933 Actuals Qty 17 < Year 105636569 Actuals Qty 13 < Year 105636722 Actuals Qty 14 < Year 101096857 Actuals Qty 16 < Year 100000107 Actuals Qty 14 < Year 100733637 Actuals Qty 16 < Year 101669133 Actuals Qty 17 No Stat 102039882 Actuals Qty 17 No Stat Mark as < Year if not already No Stat 1254 Products with less than 18 periods
Analyzing Periods of History Product ID Key Figure 36M Count Stat or No Stat 100759029 Actuals Qty 35 > Year 100721807 Actuals Qty 25 > Year 100721121 Actuals Qty 36 > Year 102199292 Actuals Qty 33 > Year 100990897 Actuals Qty 36 > Year 101020203 Actuals Qty 36 > Year 100754130 Actuals Qty 34 > Year 100754255 Actuals Qty 30 > Year 101019910 Actuals Qty 33 > Year 101019447 Actuals Qty 33 > Year 101020508 Actuals Qty 35 > Year 100721930 Actuals Qty 32 > Year 101693540 Actuals Qty 36 > Year 101190858 Actuals Qty 36 > Year 100721554 Actuals Qty 36 > Year 101191515 Actuals Qty 34 > Year 101020452 Actuals Qty 35 > Year 100729459 Actuals Qty 36 > Year 102081879 Actuals Qty 31 > Year 100722415 Actuals Qty 33 > Year 100754546 Actuals Qty 34 > Year 101020594 Actuals Qty 36 > Year The remaining products should now have 18 or more periods of history Therefore you can test Seasonal models to them We can mark them as > Year
Statistical Algorithms to Apply to our Data Now that we have classified our items into 1 of 3 groups (No Stat, < Year, > Year) we can test different and practical algorithms to them More than 1 Year Constant Seasonal Seasonal Line Regression Seasonal Trent Crostons Less than 1 Year Constant Crostons Alpha Optimization Average / Moving Average Low React
Variability and Time Periods Although you may have 3 years of data, if the Variability is better using less data, then use less. Creating comparison or delta calculation in Excel and reviewing for all > Year items will allow us to put some in a 24M group Product ID Key Figure 36M CoV 24M Cov Stat or No Stat Delta 24M vs. 36M CoV 100753506 Actuals Qty 4.232295641 0.705407684 24M 3.526887957 100754053 Actuals Qty 3.226121413 0.652451675 24M 2.573669738 100001452 Actuals Qty 3.808110608 1.826949629 24M 1.981160979 100759090 Actuals Qty 3.025588176 1.157723482 24M 1.867864694 101191538 Actuals Qty 2.887766748 1.034966681 24M 1.852800067 100001392 Actuals Qty 2.618027773 0.891159139 24M 1.726868635 101191548 Actuals Qty 2.828288158 1.152774431 24M 1.675513727 100759067 Actuals Qty 2.660511625 1.058143191 24M 1.602368433 100722336 Actuals Qty 2.877972316 1.404943372 24M 1.473028945 100722466 Actuals Qty 2.414592264 0.982403177 24M 1.432189087 100721142 Actuals Qty 2.185249524 0.879086356 24M 1.306163168 100001247 Actuals Qty 2.424836785 1.127607249 24M 1.297229536 101709285 Actuals Qty 1.751815709 0.482907761 24M 1.268907949 The 24M CoV is much less than th 36M CoV
Session 2 How frequent to Run (Stat) & React
How often should you Generate a Stat Forecast? Can your supply chain, planning tool and coordinating business processes react to forecast changes? How quickly? How quickly do you need, or want, to update the demand signal for supply planning reaction and optimal inventory management? Ideally you should generate a new Statistical Forecast frequently to incorporate the most recent Sales and Market activity into the demand stream and react as quickly as possible.
A Common Misunderstanding Many SC professionals express a concern that frequent (i.e. weekly) Stat Forecast updates will cause constant and big change to the demand signals each week, making more work and variability downstream. More frequent Statistical forecast runs cause less change period to period and are easier to react to downstream, making the supply chain more agile and more properly balanced overall. Note: Assuming proper models, periods of history and levels of aggregation are used to optimize the Statistical output.
Weekly Example New Item where Sales are trending upward after launch 125 150 Wk 3 175 Wk 4 200 Wk 5 Stat Stat Stat 100 Wk 2 Stat By running Stat weekly we can pickup on the trend quickly and have to potential to increase purchasing and/or manufacturing. Each week downstream planning can attempt to plan more at lead time.
Monthly Example Previously stable item where Sales are trending downward all of a sudden. 200 Wk 2 Wk 3 Stat Monthly forecast runs change data in larger amounts, whereas weekly forecasting makes smaller weekly adjustments week over week. Wk 4 Wk 5 Running the Stat once per month reduces the reaction time of real sales and market changes to the supply planners. Perhaps planners procured raw materials the last 3 or 4 weeks that they shouldn t have because the forecast has been reduced. Now they can t get out of it. Perhaps planners didn t order enough the last 3 or 4 weeks because the forecast has increased. Now they don t have time to react. 100
Various Stat Frequency Approaches If you update history weekly and update the forecast weekly to pass to Supply Planning, then running the Stat Forecast each week in weekly buckets will pick up each new week s history. Weekly DP Process Run Stat Weekly Hybrid DP Process Run Stat Weekly Finalize Consensus Monthly Release Monthly Monthly DP Process Run Stat Monthly If you forecast in monthly buckets, then you can run Stat once per month. You only get one new month of history at the beginning of a new month which serves as your first historical data point now. The Hybrid approach allows you to refresh your Demand Plan weekly but release to Supply/Inventory planning only monthly.
Historical Data Analysis & Segmentation Bringing it All Together
Patterns of Demand Patterns of Demand differ. They differ by item, by customer, by industry, by time. The level of detail or aggregation makes a difference. There is more variability at the detailed level. Patterns are less variable as you aggregate.
Categories of Forecasting Methods Characteristics Assumption Techniques Example Quantitative Qualitative Unpredictable Information about the past is available. Can be quantified in the form of numerical data Past Pattern will continue into the future Time Series & Causal Predicting Monthly Sales Rely on Subjective assessments of people, using intuition based on experience Experienced and knowledgeable person is available Delphi, Jury of executive Predicting increased sales into emerging markets Little or no information is available Crystal ball!! Predicting tax code in 2020.
Time Series Forecasting Basics The central theme of Quantitative Time Series Forecasting is that History repeats itself. A Time Series is a series of observations over time of some quantity of interest (i.e. a random variable). A Historical time series consists of a systematic pattern and random noise, which makes the pattern difficult to detect (i.e. outliers). The challenge is to filter out the noise to make the pattern more obvious. Demand Patterns consist of three components: Basic (level), or average volume Trend Seasonality All might be present
Univariate (Time Series) Forecasts Time Series Models Constant Models Trend Models Seasonal Models Seasonal Trend Models Auto Selections Models Crostons Model Other Models Available Forecast Models in SAP APO
Periods of History Required Model Periods of History Required Constant 1 Trend 3 Seasonal 1 season Seasonal trend 3 + 1 season Automatic model selection with seasonal test 2 seasons Automatic model selection with seasonal + trend tests 3 + 2 seasons 2nd-order exponential smoothing 3 Initialization periods are the minimum number of periods required to generate and ex-post forecast Ex-post forecast is a forecast ran in the past using the initial periods
Historical Data Analysis & Segmentation Primary Data Segmentation Considerations Number of Periods of Data Enough for a season? Two seasons? Variability of the Data Secondary Segmentation Considerations Patterns of Demand Forecast Error Use more or less History Is error related to data variability, or natural randomness?
Summary of Key Points Session 2 Typically, the more data you have the better Sometimes the pattern 3 years ago isn t the pattern today If you have Weekly and Monthly data, analyze both. Look for patterns weekly that are masked when aggregated Monthly. The number of real data points is important in assigning products to certain stat algorithms Zeros can make a difference in historical counts, and can highlight discontinuations The frequency of generating Stat should align to your business process. Running Stat more frequently allows your Supply Chain to react the fastest. Considering both Variability & Historical Period counts is important to assigning appropriate models
Questions? Contact Info: Ryan Rickard, Sr. Consultant rrickard@scmo2.com (770) 639-7285 Follow SCMO2: www.scmo2.com www.facebook.com/scmo2/ www.twitter.com/breatheinscmo2
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Getting the Most out of Statistical Forecasting! Author: Ryan Rickard, Senior Consultant Published: July 2017