Applied Data Mining for Forecasting Using SAS. Tim Rey
forecasting is Sales & Operation Planning (S&OP) in which the operations planning and financial departments match supply to a demand forecast and generate supply plans.10
In this book we discuss a more generic integration of the proposed work process into the most widespread work process in industry—Six Sigma. The advantages of this integration are as follows: fast acceptance in more than 50% of Fortune 500 companies (see below), reduced organizational efforts, established project management, well-defined stakeholders' roles, good opportunities for training and leveraging, and so on.
2.4.1 Six Sigma in Industry
What it is
A method or set of techniques, Six Sigma, has become a movement and a management religion for business process improvement.11 It is a quality measurement and improvement program originally developed by Motorola in the 1980s that focuses on the control of a process to the point of ± Six Sigma (standard deviations) from a centerline. The Six Sigma systematic quality program provides businesses with the tools to improve the capability of their business processes. At the basis of Six Sigma methodology is the simple observation that customers feel the variance, not the mean. In other words, reducing the variance of product defects is the key to making customers happy. What is important for Six Sigma is that it provides not only technical solutions but a consistent work process for pursuing continuous improvement in profit and customer satisfaction. This is one of the reasons for the enormous popularity of this methodology in industry.
Industrial acceptance
According to the iSixSigma Magazine,12 about 53% of Fortune 500 companies are currently using Six Sigma, and that figure rises to 82% for the Fortune 100. Over the past 20 years, use of Six Sigma has saved Fortune 500 companies an estimated $427 billion. Companies that properly implement Six Sigma have seen profit margins grow 20% year after year for each sigma shift (up to about 4.8 sigma to 5.0 sigma). Since most companies start at about 3 sigma, virtually each employee trained in Six Sigma will return on average $230,000 per project to the bottom line until the company reaches 4.7 sigma. After that, the cost savings are not as dramatic.
Key Roles
One of the key advantages of Six Sigma is its use of well-defined roles in project development. The typical roles are defined as: Champion, Black Belt, Green Belt, and Master Black Belt. The Champion is responsible for the success of the projects, provides the necessary resources and breaks down organizational barriers. The project leader is called a Black Belt. Project team members are called Green Belts and they do not spend all their time on projects. They receive training similar to that of Black Belts but for less time. There is also a Master Black Belt level. These are experienced Black Belts who have worked on many projects. They are the ones who typically know about more advanced tools, the business, and have had leadership training and often have teaching experience. A primary responsibility of Master Black Belts is mentoring new Black Belts.
2.4.2 The DMAIC Process
The classic Six Sigma methodology includes the following key phases known as Define-Measure-Analyze-Improve-Control (DMAIC) process:
| Define: | Understand the problem. |
| Measure: | Collect data on the problem. |
| Analyze: | Find root cause as to why the problem occurs. |
| Improve: | Make changes to eliminate root causes. |
| Control: | Ensure that the problem is solved. |
A brief description of each phase is given below.
Define
The objective of the define phase is to identify clearly and communicate to all stakeholders the problem to be solved. The team members and the timelines are laid down. Project objectives are based on identifying the needs by collection of the voice of the customer. The opportunities are defined by understanding the flaws of the existing as-is process. The key document in this phase is the project charter, which includes the financial and technical objectives, assessment of the necessary resources, allocation of the stakeholders' roles, and a project plan.
Measure
The goal of the measure phase is to understand the problem in more detail by collecting all available data around it. The following questions need to be answered: what the problem really is, where it occurs, when it occurs, what causes it, and how does it occur. The key deliverables in this phase are identifying the necessary factors (inputs) that can influence the defect (output), and collecting and preparing the data for analysis. Another very important deliverable is the statistical measures from the data, such as the sigma level of the defect and process capability (the ability of a process to satisfy customer expectations), measured by the sigma range of a process's variation.
Analyze
The objective of the analyze phase is to analyze the collected data and to find out the root causes (critical Xs) of the problem. The analyses are mostly statistically based and are designed to identify the critical inputs affecting the defect or the output Y = f (x). The potential cause-effect relationships or models are discussed and prioritized by the experts. As a result, several potential solutions of the problem are identified for deployment.
Improve
The goal of the improve phase is to prioritize the various solutions that were suggested during brainstorming and to explore the best solution with highest impact and the lowest cost effort. At the end of this phase, a pilot solution is implemented. The purpose of the improve phase is to remove the impact of the root causes by implementing changes in the process. Before beginning the implementation steps, however, the selected solution is tested with data to validate the predicted improvements. The initial results from pilot implementation are communicated to all stakeholders.
Control
The objective of this final phase is to complete the implementation of the selected solutions and to validate that the problem has gone away. A measurement system is usually set up to determine if the problem has been solved and the expected performance has been met. One of the key deliverables in the control phase is a process control and monitor plan. Part of the plan is the transition of ownership from the development team to the final user.
2.4.3 Integration with the DMAIC Process
Two options for integration with the DMAIC Six Sigma process are discussed briefly below: (1) the integration of a data mining work process and (2) the integration of the proposed data mining in a forecasting work process.
Data mining within DMAIC
The key blocks of a data mining process within the Six Sigma framework (defined in Kalos and Rey 2005) are shown in Figure 2.5.
The purpose of the first key block, Strategic Intent, is to ensure the relevance of the proposed data mining project with the strategic business goals, enterprise-wide initiatives, and management improvement plans. Another objective of this block is to identify the business success criteria, including various measurements (customer, process, and financial measurements).
The second key block, System & Data Identification, has objectives similar to those of the system structure and data identification substep in the Project Definition block. The third key block, Data Preprocessing, includes activities such as preliminary data analysis, variable selection, data transformation, and documenting the results. The fourth block, Opportunity Discovery, combines data analysis strategy development, exploratory data analysis, and model development and performance assessment. The last, fifth block of the data mining process within Six Sigma, Opportunity Deployment, is characterized by three main activities: (1) immediately using the developed models for business decisions, (2) integrating the developed models in other projects, and (3) triggering other projects based on the discovered opportunities and generating of preliminary Six Sigma project charters.