Part Three in a series of four on the Intelligent Enterprise Machine Leaning Framework. Read Part One, Part Two, and Part Four.
In this blog, I am going to introduce the machine learning (ML) project framework and the details required in each of the tasks.
Enterprise performance management (EPM) is an area of business intelligence which monitors and manages an organization’s performance according to KPIs. EPM provides a framework for organising, automating and analysing business methodologies, metrics, processes and systems to drive the overall performance of the organization. It helps organizations translate a unified set of objectives into plans, monitor execution, and deliver critical insight to improve financial and operational performance. It helps organizations get smart!
Many organizations use DMAIC (an acronym for Define, Measure, Analyze, Improve and Control) to support EPM. DMAIC is a well-known data-driven improvement cycle used for improving, optimizing and stabilizing business processes and designs. The DMAIC improvement cycle is the core tool used to drive Six Sigma projects.
Many modern ML initiatives are designed to support EPM type initiatives, as they strive to become best-run businesses. This is why DMAIC is an ideal SAP machine learning project framework for the Intelligent Enterprise.
DMAIC is an iterative process. There are 5 phases:
This project framework is a data-driven improvement cycle that will improve, optimize, and stabilize SAP machine learning models and any associated business processes.
In the Define phase you are encouraged to use some of the hugely powerful, and popular, Design Thinking techniques. These are very effective when you and your customer need to gain a clear understanding of the challenges you will tackle in the project.
Each task below is described in more detail in the images at the end of this blog post:
In the next and final blog, I will present some overview material that summarizes the phases, tasks, and deliverables for the machine learning framework.
For an in-depth look into the intelligent possibilities for your business, review the August 2018 Forrester Consulting study, Powering The Intelligent Enterprise With AI, Machine Learning, And Predictive Analytics, commissioned by SAP.
In this blog, I am going to introduce the machine learning (ML) project framework and the details required in each of the tasks.
Enterprise performance management (EPM) is an area of business intelligence which monitors and manages an organization’s performance according to KPIs. EPM provides a framework for organising, automating and analysing business methodologies, metrics, processes and systems to drive the overall performance of the organization. It helps organizations translate a unified set of objectives into plans, monitor execution, and deliver critical insight to improve financial and operational performance. It helps organizations get smart!
Many organizations use DMAIC (an acronym for Define, Measure, Analyze, Improve and Control) to support EPM. DMAIC is a well-known data-driven improvement cycle used for improving, optimizing and stabilizing business processes and designs. The DMAIC improvement cycle is the core tool used to drive Six Sigma projects.
Many modern ML initiatives are designed to support EPM type initiatives, as they strive to become best-run businesses. This is why DMAIC is an ideal SAP machine learning project framework for the Intelligent Enterprise.
DMAIC Phases
DMAIC is an iterative process. There are 5 phases:- Define the problem, improvement activity, opportunity for improvement, the project goals, and customer (internal and external) requirements.
- Measure process performance.
- Analyse the process to determine root causes of variation, poor performance (defects).
- Improve process performance by addressing and eliminating the root causes.
- Control the improved process and future process performance.
SAP Machine Learning Project Framework
This project framework is a data-driven improvement cycle that will improve, optimize, and stabilize SAP machine learning models and any associated business processes.
In the Define phase you are encouraged to use some of the hugely powerful, and popular, Design Thinking techniques. These are very effective when you and your customer need to gain a clear understanding of the challenges you will tackle in the project.
Each task below is described in more detail in the images at the end of this blog post:
- Objectives: what is expected from that task or why do we implement this task (“why”)
- Roles involved in the task (“who”)
- Descriptions of the sub-tasks that must be accomplished to implement the task (“how”)
- Deliverables for each task
In the next and final blog, I will present some overview material that summarizes the phases, tasks, and deliverables for the machine learning framework.
Learn More
For an in-depth look into the intelligent possibilities for your business, review the August 2018 Forrester Consulting study, Powering The Intelligent Enterprise With AI, Machine Learning, And Predictive Analytics, commissioned by SAP.
Define Phase
Measure Phase
Analyze Phase
Improve Phase
Control Phase
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