Time Series Forecasting with SAS Forecast Server at NPPC Tim Young Senior Demand Planning Statistician, NPPC Aaron Bernstein GAUSS, November 8, 2018 Welcome Everyone My name is Aaron Bernstein and I am a senior Demand Planning Statistician at Nestle Purina. My associate is Tim Young who is also a senior Demand Planning Statistician at Nestle Purina. Tim Young was pivotal in arranging this conference at Nestle Purina so let’s give him a round of applause. We are going to talk about Time Series Forecasting with SAS Forecast Server and specifically how we use SAS to support Demand Planning.

Demand Planning at Nestlé Purina PetCare (NPPC) Agenda Demand Planning at Nestlé Purina PetCare (NPPC) SAS Components in Demand Planning Solution Details of SAS High Performance Forecasting Where are we going? A high level review of Demand Planning at Nestle Purina. The SAS software components that support the demand planning solution at Nestle Purina. A deeper dive into SAS High Performance Forecasting and the Diagnose Procedure.

Introduction to Demand Planning at Nestlé Purina PetCare Let’s take a look at the Nestle Purina Supply Chain in North America. Demand Planning supports all demand forecasting for North America. 21+ manufacturing facilities and distribution centers in the US and CA. 12 sales offices all over the US. And many more customer account teams. All of these teams need to know how much product is expected to be sold, where it’s to be sold, and when it’s to be sold. The Demand Planning Team’s mission is to provide accurate, reliable, and consistent forecasts to help fulfill customer demand. What does that look like in practice? Nestlé Purina Demand Planning drives the supply chain by forecasting customer demand to service the right product, at the right place, at the right time.

Introduction to NPPC Demand Planning (cont.) Over 50,000 forecasts are generated by Demand Planning every week! Demand Planning serves as the hub for demand collaboration between the Customer Sales Teams and Manufacturing Facilities. Customer Sales Teams Demand Planning Manufacturing Facilities Communication Communication Demand Planning generates over 50,000 forecasts every week. Demand Planning serves as the hub for demand collaboration between the Customer Sales Teams and Manufacturing Facilities. Customer Sales Teams Customer specific forecasts Customer promotion estimates (coming Q3 2019). Manufacturing Facilities Final Consensus Demand Plan Forecast Safety Stock Estimates based on forecast error. Customer Specific Forecasts Customer Promotion Estimates Supply Chain Forecasts Supply Chain Safety Stock Estimates

SAS Components of Demand Planning Research & Development SAS System Processing SAS Forecasting How do we generate so many forecasts? We accomplish this using SAS Software. Recently we upgraded to SAS DDPO Software (Demand Driven Planning and Optimization). SAS DDPO contains the Forecast Analyst Workbench and Product Lifecycle Management Tools. FAW generates our forecasts. PLM manages the forecast dataset. Underlying SAS DDPO is our IT system which is run throuy SAS Data Integration Studio and SAS Enterprise Guide. We load SAS System Support underlies all our data transformation and processing. We continuously improve our forecasting system through Research and Development using SAS Forecast Studio and SAS Enterprise Guide.

SAS Components of Demand Planning - R&D Forecast Studio R&D Forecasting Tool. Helps to determine what ideas get implemented in the business. SAS Enterprise Guide R&D Process Improvement Tool BASE SAS and SAS PROCs. At Nestle Purina we are continuously improving our processes to provide a better product and better customer service. For Demand Planning this means a more accurate, less biased forecast. We have to make improvements to our processes as well. We use Forecast Studio for our R&D. Forecast Studio is a more precise analytical tool than FAW. Allows a detailed analysis of forecast and forecast components. Much more nimble than FAW. We’ll talk about that on the next slide. Of course, SAS Enterprise Guide is also crucial to our R&D. Data transformations and processing to create datasets for FS. Analysis on R&D forecasts and processes. Numerous SAS PROCs Time-Series Library Regression Library Promo and Causal Estimates Proc Univariate for data analysis Just to name a few

SAS Components of Demand Planning - SAS System Processing Data Integration Studio Backbone of forecasting system. Drives all data management and transformations. Requires DI specific skillset. SAS Enterprise Guide User window into source data. Allows ad-hoc data manipulations. Requires general SAS programming skillset. How do we process and get data into SAS FAW? Data Integration Studio provides the backbone of all necessary data processing. Interface with company data. Builds all the input datasets to be loaded into SAS FAW. Do much of our specialized processing, data transformation and data massaging before loading into the forecast engine. Note that DI requires a DI specific programming skillset. Most BASE SAS Users cannot simply edit DI Studio programs. If we need to investigate production data or do a last minute data fix SAS EG allows more ad-hoc data interaction. Requires only a general SAS programming skillset allowing more users to interact with the forecast data. Point and Click drop down sort and filter options are often helpful.

SAS Components of Demand Planning - SAS Forecasting Forecast Analyst Workbench Point and Click UI to Forecast Server. Create and adjust forecasts. Utilizes Product Lifecycle Manager function to align forecasted items across business. Let’s talk more in depth about our SAS Forecasting. Tim Young will go into much more depth as well. SAS FAW is the crown jewel of our forecasting solution. Point and Click UI allows forecast access to all members of DP Team. No specialized coding skillset required. Through the UI we have the ability to create and adjust forecasts as necessary. Often we will get inquiries on forecasts. The UI allows us to adjust forecast parameters if we deem necessary. The Product Lifecycle Manager Function helps us collaborate with the business on which items require a forecast and when. The PLM Function is a necessary part of FAW Forecasting which we will cover more in depth later.

Comparison of SAS Forecasting Tools SAS Forecast Studio SAS Forecast Analyst Workbench Flexible data structure Load data and push play Detailed statistical output Inflexible, pre-defined data structure Employs PLM Function Allows individual forecast tuning You may have noticed that we have two forecasting tools with SAS. In reality they are both based on the High Performance Forecast Server. They are each a different way to access that HPF Server. Both have a point and click UI. HOWEVER, SAS FAW is very inflexible when compared with SAS Forecast Studio. Inflexibility due to a detailed initial setup defining data dimensions. Also, there is a requirement to use Product Lifecycle Manager Tool to define forecast data set. Forecasts won’t be reliably produced unless they are correctly defined. SAS Forecast Studio flexible for those knowledgeable making it ideal for R&D Create your dataset and push play. Provides much more detailed analysis statistics and graphs. However, SAS FAW is set up specifically to be used in a mass forecast production environment. PLM Functionality creates forecast data set. Tuning Function to allow users to adjust forecast at will. We’ll have time at the end for any questions. For now, to talk more about SAS High Performance Forecasting my colleague Tim Young will take over. Tim. R & D Production

SAS High Performance Forecasting How does SAS Forecasting work? SAS facilitates forecasting at scale through its High-Performance Forecasting (HPF) solution Focused on time series forecasting Executed through a series of associated (HPF) procs Also known as SAS Forecast Server Accessed via EG code, Forecast Studio, or Forecast Analyst Workbench HPF is a collection of things such as PROC ARIMA, PROC ESM, PROC SCORE, etc. Result is a large number of forecasts without human intervention.

SAS High Performance Forecasting HPF is composed of three main sub-procedures High-Performance Forecasting Process High-Performance Forecasting Think about sequence of forecasting one model: Look for characteristics and determine a model type Estimate parameters and check model fit Determine best model

High-Performance Forecasting SAS High Performance Forecasting High-Performance Forecasting Determine candidate models Fit candidate models Reconcile hierarchy Goal: To determine characteristics of each time series and narrow down potential models What characteristics are checked? Intermittency Seasonality Trend Functional transformations Differencing Outlier detection Significance of input variables Tentative orders for some models Tests used: Augmented Dickey-Fuller Pre-fitting for simple AR/MA orders to minimize SBC Intermittency test Etc. Pause on each characteristic. Output is a set of candidate models.

SAS High Performance Forecasting Determine candidate models Determine candidate models Fit candidate models Fit candidate models Reconcile hierarchy Reconcile hierarchy Goal: To find the “best” model for each time series. Model types available (in order of complexity): ESM – Exponential Smoothing Model Trend Seasonality IDM – Intermittent Demand Model Special Case of ESM ARIMAX – Autoregressive Integrated Moving Average with Exogenous Inputs Trend Seasonality Outliers and Level Shifts Independent Variables UCM – Unobserved Components Model Level Trend Seasonality Cycles Regression effects Estimate parameters. Forecast values.

SAS High Performance Forecasting Determine candidate models Determine candidate models Determine candidate models Fit candidate models Fit candidate models Fit candidate models Reconcile hierarchy Reconcile hierarchy Reconcile hierarchy Champion forecast is selected by a pre-defined model fit statistic Fit statistic can be calculated across entire time series or a shorter holdout sample Explain holdout sample. Over-fitting.

SAS High Performance Forecasting Bottom Middle Top US West Region Store 1 Store 2 East Region Store 3 Store 4 High-Performance Forecasting High-Performance Forecasting High-Performance Forecasting Determine candidate models Determine candidate models Determine candidate models Fit candidate models Fit candidate models Fit candidate models Reconcile hierarchy Reconcile hierarchy Reconcile hierarchy Bottom Up Goal: To ensure all levels of the hierarchy are in sync. Each level is first forecasted independently Then proportions are imposed from one level to another Bottom up Top down Middle out Bottom Middle Top US West Region Store 1 Store 2 East Region Store 3 Store 4 Explain goal of reconciliation first. (If you don't, the top might not equal sum of leaves.) Top Down

SAS High Performance Forecasting HPF Summary High-Performance Forecasting Intermittency Seasonality Trend Etc. ARIMAX ESM IDM UCM Bottom up Middle out Top down

Key Take-Aways Positioned as the hub for demand collaboration, Nestlé Purina’s Demand Planning team generates 50,000 weekly forecasts to support our entire North American supply network A multi-faceted data management and forecasting system requires many moving parts Enterprise Guide, Data Integration Studio, Product Lifecycle Management, Forecast Analyst Workbench, and Forecast Studio Forecasting is accomplished with the SAS High-Performance Forecasting package, which automates forecasting at scale As we wrap up, here are the main points we want you to take way.

Questions ?