Introduction to SAS Forecasting
•Als PPTX, PDF herunterladen•
1 gefällt mir•2,590 views
http://www.sas.com Forecasting is ubiquitous – it’s everywhere! Whenever your company makes a decision regarding a future action – that decision making process is the end result of a process starting with a guess on what is going to happen in the future. Learn how SAS Forecasting helps you make more profitable, faster and more accurate decisions.
Empfohlen
Empfohlen
Weitere ähnliche Inhalte
Was ist angesagt?
Was ist angesagt? (20)
Ähnlich wie Introduction to SAS Forecasting
Ähnlich wie Introduction to SAS Forecasting (20)
Mehr von SAS Canada
Mehr von SAS Canada (7)
Introduction to SAS Forecasting
- 1. Copyr ight © 2012, SAS Institute Inc. All rights reser ved. FORECASTING USING SAS PAT VALENTE, SOLUTION SPECIALIST
- 2. 2Copyright © 2011, SAS Institute Inc. All rights reserved. • FORECASTING – SETTING THE SCENE • PROCESS AND CHALLENGES • SAS TECHNOLOGY SUPPORT Copyright © 2011, SAS Institute Inc. All rights reserved.
- 3. Copyr ight © 2012, SAS Institute Inc. All rights reser ved. FORECASTING IS UBIQUITOUS
- 4. Copyr ight © 2012, SAS Institute Inc. All rights reser ved. PUTTING FORECASTING INTO CONTEXT Managing the future Forecasting • What will the future look like Budgeting •What should the future look like Planning •Actions to achieve a certain target
- 5. 5Copyright © 2011, SAS Institute Inc. All rights reserved. • FORECASTING – SETTING THE SCENE • PROCESS AND CHALLENGES • SAS TECHNOLOGY SUPPORT Copyright © 2011, SAS Institute Inc. All rights reserved.
- 6. Copyr ight © 2012, SAS Institute Inc. All rights reser ved. FORECASTING TWO INTERCONNECTED LOOPS Assessment Post- processing Data preparation Explore and analyze Segmentation Statistical modeling What-if analysis Manual override Consensus Management sign-off Execute Evaluate baseline Forecast production Forecast consumption
- 7. Copyr ight © 2012, SAS Institute Inc. All rights reser ved. WHY GOOD STATISTICAL FORECASTING IS IMPORTANT •Instead of the forecast just being what you have available – supply – the statistical forecast enables you to get a better understanding of what you can actually sell – demand. Forecasts reflect demand. •Reduce the impact of human misjudgment and political biases Solid and unbiased baseline •High quality statistical forecasts enables you to focus on the most difficult to forecast items in the forecast process downstream Forecasting by exception •Continuously improve the forecast process by structuring the process and focusing on the added value the it brings Continuous process improvement •Explicitly dealing with the uncertainty around the forecast Uncertainty
- 8. Copyr ight © 2012, SAS Institute Inc. All rights reser ved. TRADITIONAL FORECASTING CHALLENGES • Supply driven • Silos/Internal politics • Manually intensive • Inefficient processes • Excessive use of spreadsheets • Use of existing planning systems. • Scalability • Lack of automation • Lack of skilled analysts • Gut feeling • Playing the numbers • Internal politics dominate
- 9. 9Copyright © 2011, SAS Institute Inc. All rights reserved. • FORECASTING – SETTING THE SCENE • PROCESS AND CHALLENGES • SAS TECHNOLOGY SUPPORT Copyright © 2011, SAS Institute Inc. All rights reserved.
- 10. Copyr ight © 2012, SAS Institute Inc. All rights reser ved. FORECASTING SUPPORTING SAS TECHNOLOGY Enables you to quickly create a very large number of forecastsScalable Enables you to create statistical forecasts with limited resourcesManageable Enables you to create sound forecasts that follows best practicesReliable SAS® FORECAST SERVER Time Series Exploration SAS® Time Series Studio Batch Interface SAS® Forecast Server Procedures Forecast Modeling SAS® Forecast Studio SAS® FORECAST SERVER
- 11. Copyr ight © 2012, SAS Institute Inc. All rights reser ved. SAS FORECAST SERVER SAS TIME SERIES STUDIO • Before commencing any time series forecasting task it is important to get at better understanding of the data at hand • This will help you answer questions such as • What is the degree of seasonality? • Is there an underlying trend? • Is there a hierarchy in my data I should use? • Would it make more sense to try and segment my data and model each segment separately? • Are there time series which are not suitable for time series modeling? • Are there indications that my forecast is influenced by external factors?
- 12. Copyr ight © 2012, SAS Institute Inc. All rights reser ved. SAS FORECAST SERVER FORECAST STUDIO • Forecast modeling can be a time consuming task. SAS Forecast Studio helps increase the productivity of the forecast analysts by offering: • A unique combination of automatic and manual model building taking into account the effects of external drivers • The ability to use any hierarchical structure in the data to improve forecast accuracy • Automatic outlier detection • Access to an extensive model library • Intelligent management of events influencing the forecasts • Rolling simulations to evaluate the stability of the forecast over time • Easy access to what-if scenario analysis to better understand how the external drivers influence the forecast
- 13. Copyr ight © 2012, SAS Institute Inc. All rights reser ved. SAS FORECAST SERVER WEB BASED PROCESS FLOW The components of SAS Forecast Server can be leveraged individually or through a guided process flow that structures and documents the work being done
- 14. Copyr ight © 2012, SAS Institute Inc. All rights reser ved. PAT VALENTE PRE-SALES SAS CANADA Pat is an analytics professional with a focus on telecommunications but supporting a variety of industries. Two decades of analytical and management positions have given Pat exposure to business areas within Finance, Service and After Sales Logistics, Marketing, Commissioning and Client Care. This has allowed him to develop and demonstrate strengths in areas that include: Relationship management, Strategic business planning, Analytical skills, Database and Systems knowledge, Problem resolution and Budgeting/financial control. Pat holds an M.A. in Economics from York University in Toronto and worked prior to joining SAS in a variety of analytical and management positions at both the wireless and landline divisions of Bell Canada, Canada’s leading telecommunications provider. 416.307.5053 Pat.Valente@sas.com http://ca.linkedin.com/in/patvalente/ Solution Specialist
- 15. Copyr ight © 2012, SAS Institute Inc. All rights reser ved. www.SAS.com
Hinweis der Redaktion
- Agenda for today: First introduce forecasting as a topic and differentiate it from other business domains. Then discuss how we do forecasting and the challenges many companies still struggle with. And finally discuss how SAS can be used as technolgy support to enable companies to improve their forecasting capabilities. Let’s begin with setting the scene.
- Forecasting is ubiquitous – it’s everywhere! Whenever your company makes a decision regarding a future action – that decision making process is the end result of a process starting with a guess on what is going to happen in the future. That holds true for: the manufacturer that wants to know how much inventory to stock The call center that has to decide how many people should come to work every day The car manufacturer that needs to plan production The shipping company that needs to plan its cargo in an optimal way The IT department that wants to optimize the performance of the IT infrastructure The marketing department that plans promotions The telco that wants to optimize its network The retailer that wants to know how much to put on its store shelves The airline that wants to optimize revenue
- The previous slide mentioned some key issues: ”decision making” and ”the future”. Making decisions that allows you to have some control of the future can be a challenge. Even more so if the different activities that deal with the future gets mixed up. Let me therefore share my understanding of what forecasting is and how it relates to the other activities that deals with managing the future. From my perspective forecasting tries to answer the question: “Based on past behavior - what will the future look like?”. The aim is to try to spot things like trend, seasonality, cycles etc. to come up with a educated best guess about how the future will look like – everything else being equal. Additionally, sophisticated forecasting enables you to estimate the effect of future events, promotions and changes in other business drivers. Budgeting on the other hand is all about addressing the question of: “what should the future look like?” or put differently “how do we want the future to look like?”. When phrasing it this way it becomes clear that when doing budgeting we also set targets and goals. Planning defines which actions should be taken to arrive at our goals and targets. This is all very theoretical but consider the following example: When forecasting sales for a particular item we notice a future declining trend. Comparing this to the budget which assume constant sales it becomes clear that we face a negative gap. To eliminate this gap we implement a plan to increase the sales force as well as moving some marketing spend from other areas of the business.
- So – after setting the scene let’s look at how we work with forecasting and some of the challenges companies are facing today
- Working with forecasting can be split into 2 separate areas: The generation of the forecasts – often referred to as ”Forecast Production” The usage of the forecasts generated – which is also known as ”Forecast Consumption” Each area is the responsibility of different user personas with the forecast production being handled by people familiar with the underlying data and source systems as well as statistical forecasting. Forecast consumption is often part of a business process such as for example ”Demand Planning”. The important thing to note is of course the iterative nature of these loops and their dependency on each other. Another crucial aspect is how you weigh the importance of these 2 areas. A lot of energy and effort should obviously go into how you consume and use the forecast. However, many companies today have a tendency to undervalue the importance of the production or generation of the forecasts.
- But why this focus on statistical forecasting? Why should it be considered an important part of your forecasting efforts. There are several reasons for that. The most important reason is that a statistical forecast allows you to get a better understanding of what your customers are demanding instead of just focusing on what you are able to supply them with. This also means that you will be able to work with customer demand proactively instead of just hoping that you can sell what is available. Secondly, a high quality statistical forecast provides you with a solid and unbiased baseline forecast that can be used as input to your forecast process. As any other process the quality of the end result hinges on the quality of the input and what you add during the process. Being unbiased means that the forecast is objective – it is not contaminated by any human biases or politics. Having a statistical forecast you can trust also enables you to do forecasting by exception – that is focus on those items where a statistical forecast doesn’t give any value in the downstream forecast process. Having a statistical forecasting that allows you to all of the above will make your forecast process even more efficient and will also enable continuous process improvement by measuring the value added created by the process. When doing forecasting it is important to realize that we are dealing with unknown future outcomes. This means that the forecast we create should reflect this future uncertainty. Failure to take the uncertainty into account will lead to poor planning. The way uncertainty is provided is by reporting both the point forecast and the uncertainty regarding this forecasting – typically in the form of confidence intervals.
- But why is it then that companies still are struggling with getting forecasting right. At an overall level we can divide these challenges into 3 different groups: Processes, People and Technology. Processes: Many companies today base their forecast on what they have ready to sell. In other words the forecasts are ”supply driven” instead of focusing on demand. In addition silos within the company and internal politics often means that different departments base their planning on different perceptions of what the future is going to look like Even if the company has established cross functional processes to enable consensus forecasting this process is often very labor intensive with many resources being spent on gathering data, reporting etc. Also, even though a process exist it is not ensured that it will result in more accurate forecasts. People: In any forecast process there is major human involvement. Unfortunately this does not always produce positive results in the sense of reducing forecast error. The main reason for this is that people often are poor at forecasting – either intentionally by playing the numbers to their own advantage or unintentionally in so far as we humans are very poor at separating structure from randomness. Add to this the fact that a lot of companies lack skilled analysts for analyzing trends, seasonality etc. Technology: Finally, most companies today have inadequate technology support for reducing forecast error. Probably the most used technology for forecasting today is spreadsheets. Spreadsheets have many advantages – supporting a high quality forecast process is not one of them. One issue that spreadsheets share with other technology offerings is scalability – looking at the possible combinations of item x location that many retailers and manufacturers plan for make it clear that spreadsheets can’t handle this task. Similarly, using spreadsheets also makes automation a challenge which in addition to being a waste of resources also carries a lot of risk. The second most used piece of technology are legacy planning systems and while they probably have a more holistic view of the organization than spreadsheets they too do not provide adequate technology support.
- New methods and techniques are being advanced from industry as well as academia. Solutions to these complex problems often span across multiple analytical disciplines and industry domains.
- The flagship product SAS offers for large scale statistical forecasting is SAS Forecast Server which consists of 3 components. Each component can be used individually but obviously the best result will come if they are used in conjunction with each other. TSS: FS: Batch: In other words, what FS will provide is an offering that is: Scalable: Meaning that you can quickly and efficiently create a very large number of forecasts. Manageable: Easy to setup and execute enabling you to create forecasts with limited resources Reliable: The engine will create forecasts that adhere to well documented forecasting principles such as using a hold-out sample for honest assessment etc.
- SAS Time Series Studio enables you to work with raw time series data and prepare the data for time series forecasting. A crucial part of this preparation step is to get a better understanding of the data. By getting a better understanding of the data you will know which part of the data can be reliably forecast using time series methods and which cannot. You will also gain an understanding of any hierarchical structure in the data that can be used.
- If I should put a label on what SAS FS is it is going to be this one: SAS FS is a problem agnostic productivity tool for analysts. It is problem agnostic in the sense that it is not limited to one specific business issue – it caters for all forecasting needs. It is a productivity tool for analysts in the sense that it allows the advanced user to quickly and efficiently create high quality statistical forecasts. So what does it provide: It enables you to work with hierarchies enabling you to create forecasts at different levels of aggregation Automatic outlier detection Extensive model repository – model building on the fly – model families include ESM, ARIMA, UCM and IDM Scenario analysis enabling you to perform what-if analysis of changes in underlying business drivers Intelligent event management to create and estimate the effect of different types of events. Temporal reconciliation enabling you to work with data at different frequencies Combine different forecast models to improve the final forecast accuracy Ability to perform rolling simulations to measure the robustness of your forecast model.
- In order to speed up the modeling process Forecast Server also enables you to use the individual components through a web based proces flow. This will allow you to easily and quickly setup segments and produce forecasts for each segment using different forecasting approaches dependent on the characteristics of each segment.