Using Operational Analytics to Achieve a Digitized, Visible Supply Chain

To assure a succcessful future, companies must add value to their customers and organizations, and drive competitive advantage. The best way to achieve this is by developing a system based on operational analytics.

Operational analytics come from analyses done on the fly as part of standard business processes. Planning, inventory management, and control are examples of operational analytics.

The main objective of operational analytics is to arrive at a visible, digitized supply flow achieved by replacing an investment in excess inventory with an investment in data. This is accomplished by implementing enterprise automation to help in planning, mobile technology to get real-time data, and analytics to help improve processes.

Operational analytics is connected to my favorite approach: lean and digitize. To improve processes, reduce cycle times, and cut waste requires knowing the process—specifically, measuring it and taking action. This means moving from business intelligence to business process intelligence—from analyzing the data to managing the processes.

The reward of an operational analytics model is a digital value stream that makes it possible to extract visibility and velocity from volume and variety.

What holds average companies back from achieving an operational analytics model is education. Many managers do not know what operational analytics is, and how it can be useful to their companies. On the other hand, many companies—GE Oil & Gas (when I was there as CIO), Praxair, and Zara, to name a few—are using operational analytics to achieve breakthroughs.

By following some best practices, organizations can find many ways to adopt and successfully implement operational analytics. A focus on decision-making, and a plan/vision for analytics, are critical. Some common threads link these best practices, as do common challenges.

The critical success factors in implementing operational analytics are a focus on strategy, process, organization, and technology. After working with several organizations that have successfully adopted operational analytics into multiple facets of their operation, I became aware that they had these best practices in common:

• Cross-channel and batch/online integration.
• Store, cleanse, and integrate transactional details.
• Report, execute, and model using the same data.
• Implement real-time conversation information when making decisions.
• Tie the application to performance management.

   

Many challenges are connected with implementing operational analytics. To remediate these issues, follow this recipe:

• Make sure you have the right mix of resources. IT, HR, and finance can play essential roles in implementing a project.
• Clearly define the responsibilities of all involved.
• Know your organization’s culture and include senior leadership presence in any changes.
• Choose a thought leader in your company who is capable of leading a new initiative to a successful outcome.