Applied Data Mining for Forecasting Using SAS. Tim Rey
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In contrast to the previous category, planners actively use the delivered statistically based forecasting models in developing their sales, marketing, or operations plans. Very often they also have the right to override the statistical forecasts with their judgmental estimates. In the case of demand-driven forecasting, these are the users in marketing and sales who “shape the demand” based on analytics and domain knowledge. From all the categories of forecasting users, planners are the most educated and directly involved in the model development and deployment loops. They have the decisive role in introducing expert knowledge by defining events, evaluating model performance, and making the final forecasts adjustments. Planners also have the responsibility to recommend to the decision-makers which developed plans, based on the delivered and adjusted forecasts, get final approval.
Decision-makers
This category of forecasting users includes the middle-layer managers at the departmental level who are responsible for the results of the plans recommended by the planners. They also make the final decision for implementing the plans. Part of the decision-making process is balancing the recommended statistically-driven forecasts from the experts (planners and model developers) and the top management push. Often the decision-making process goes through several iterations until a consensus is reached. This category of users is critical for the success of specific forecasting projects and the overall forecasting activities in the business. Success for decision-makers is not based on model performance measured by forecasting accuracy but is based on the expected value measured by the key performance indicators (KPIs).
Top level managers
This category includes the top executives related to finances, IT, and operations. As users, they might have different roles. One critical role is to establish and support financially, for some period of time, the forecasting capabilities in the organization. Executives might request forecasting projects for developing a business strategy as well. It is expected that at any moment the top executives can access the forecasting reports at any level of the organization and keep track of the KPIs. And finally they can actively influence what decisions are made regarding the implementation of the action plans based on forecasting models.
3.5.3 Work Process Implementation
A key component in developing the organizational infrastructure is selecting and implementing an appropriate work process for data mining in forecasting. An example of such a work process is given in Chapter 2. It is also very important to integrate the selected work process with the existing corporate culture. The best-case scenario is to consolidate the data mining for forecasting work process with the existing standard work processes in the organization. If you can do so, the implementation cost and the time for integration into the corporate culture will be significantly reduced. An example of integration with the most popular work process in industry, Six Sigma, is described in Chapter 2. Another example of a popular work process in the case of demand-driven forecasting – Sales & Operation Planning (S&OP) is given in Chase 2009.
3.5.4 Integration with IT
An organizational issue of critical importance for the final success of applying data mining for forecasting is the smooth integration with corporate IT services. Unfortunately the integration process can be bumpy largely due to the different mode of operation of IT. The IT department is often focused primarily on implementing standard solutions across the business. The focus of data mining for forecasting is on delivering custom and, therefore, nonstandard solutions using specialized software. It is a well-known fact that maintenance and support of data mining for forecasting systems requires specialized expertise rather than the typical skill sets in corporate IT. One potential solution to this problem is allocating the specialized system support within the developers group. Part of the responsibilities of the developers' group system administrator is to coordinate all activities with IT. While establishing the developers group, however, support from top IT management is needed to promote the necessary changes beyond the IT standards.
3 A good comparison between SAS Enterprise Miner, IBM SPSS, and StatSoft STATISTICA Data Miner is given in the Handbook of Statistical Analysis & Data Mining Applications (Nisbet et al. 2009).
4 http://www.sas.com/technologies/dw/etl/access
5 http://www.bloomberg.com/professional
6 http://www.globalinsight.com
Chapter 4: Issues with Data Mining for Forecasting Application
4.2 Technical Issues
4.2.1 Data Quality Issues
4.2.2 Data Mining Methods Limitations
4.2.3 Forecasting Methods Limitations
4.3 Nontechnical Issues
4.3.1 Managing Forecasting Expectations
4.3.2 Handling Politics of Forecasting
4.3.3 Avoiding Bad Practices
4.3.4 Forecasting Aphorisms
4.4 Checklist “Are We Ready?”
4.1 Introduction
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