Executive Briefings

Will Advanced Analytics Leveraging Digital Technology Be An Extinction Event?

With the emergence of new digital technologies such as Mobility, Big Data, Internet of Things, Cloud, Business Intelligence/AI, Machine Learning, etc., new and more abundant sources of data are available to improve supply chain planning and execution. Advancing in analytic maturity is the most significant competitive lever in the history of business. Why? Time. Advancing in analytics maturity takes time that many companies aren’t able to make up in a market race. -Rich Sherman, Senior Fellow, Supply Chain Centre of Excellence, Tata Consultancy Services

Will Advanced Analytics Leveraging Digital Technology Be An Extinction Event?

Competition in digital markets isn’t against companies; rather, it’s competing against time. Advanced analytics, particularly machine learning requires new sources of data at every critical supply chain control point. From health monitoring of individuals to their smart phone to the Internet, data are collected in real time. Advancing in analytics maturity takes time to manage, analyze, learn and provide insight to harness the power of smart digital supply networks.

Connecting with channel-wide data is the foundation for smart digital supply network management. Weaving together all of the points in the network with a “digital thread” (as GE calls it) and collecting the data across the channel in real time enables companies to develop and initiate an advance analytics strategy. Companies that aren’t collecting the new data could find themselves at a competitive disadvantage that they simply can’t overcome. Machine learning improves over time, so getting ahead of the game is a competitive imperative.

Getting started at the first level, Descriptive Analytics, requires master data management. MDM today is no longer an IT discussion; it’s a business discussion. MDM enables companies to define the characteristics of product and supply network attributes and harmonize the data to share with channel participants.

The next maturity level is Monitoring Analytics. Companies deploy or share data from sensors, monitors, and other connected (IoT) devices that read and write data about the condition/environment/ownership of physical material as it flows and transforms through the network.

The third maturity level, Diagnostic Analytics, begins to be influenced by time. Logic is applied to the data to assess performance and deviation from plan. Over time, machine learning better understands the behavior indicated by the data and is able to diagnose and identify potential problems/issues with the flow of goods.

Based on historical analysis and learning the likely causes of deviation from plan and/or disruptions, Predictive Analytics are possible.  Business intelligence is applied to determine likely performance outcomes and predict and alert operators to execute corrective actions to bring the demand-supply network back in control.

Finally, as a history of corrective actions is collected, Prescriptive Analytics are developed to guide operators to correct the problem and resolve issues. Or, advanced process control algorithms can be developed for the system to take corrective actions itself (self-correcting applications).

Regardless of the state of your analytics maturity, it takes time to advance to the next level. At each level, significant competitive advantage is gained from the insights provided. According to most industry benchmarks, leaders already have a 50 percent cost advantage over even their median competitors. Add market and consumer intelligence, demand-supply pricing, and other market share gaining initiatives and the laggards may be facing an extinction event.

The Outlook

In 2018, market leaders deploy their digital strategy advancing to descriptive, monitoring and diagnostic analytics. For example, Procter & Gamble uses point-of-sale data to forecast demand daily to identify variability from plan and adjust its production accordingly resulting in significant cost advantage. Amazon uses in-line optimization in its order and fulfillment systems to balance inventory across their network. As more companies implement advanced analytics, catching up by laggards becomes insurmountable.

Competition in digital markets isn’t against companies; rather, it’s competing against time. Advanced analytics, particularly machine learning requires new sources of data at every critical supply chain control point. From health monitoring of individuals to their smart phone to the Internet, data are collected in real time. Advancing in analytics maturity takes time to manage, analyze, learn and provide insight to harness the power of smart digital supply networks.

Connecting with channel-wide data is the foundation for smart digital supply network management. Weaving together all of the points in the network with a “digital thread” (as GE calls it) and collecting the data across the channel in real time enables companies to develop and initiate an advance analytics strategy. Companies that aren’t collecting the new data could find themselves at a competitive disadvantage that they simply can’t overcome. Machine learning improves over time, so getting ahead of the game is a competitive imperative.

Getting started at the first level, Descriptive Analytics, requires master data management. MDM today is no longer an IT discussion; it’s a business discussion. MDM enables companies to define the characteristics of product and supply network attributes and harmonize the data to share with channel participants.

The next maturity level is Monitoring Analytics. Companies deploy or share data from sensors, monitors, and other connected (IoT) devices that read and write data about the condition/environment/ownership of physical material as it flows and transforms through the network.

The third maturity level, Diagnostic Analytics, begins to be influenced by time. Logic is applied to the data to assess performance and deviation from plan. Over time, machine learning better understands the behavior indicated by the data and is able to diagnose and identify potential problems/issues with the flow of goods.

Based on historical analysis and learning the likely causes of deviation from plan and/or disruptions, Predictive Analytics are possible.  Business intelligence is applied to determine likely performance outcomes and predict and alert operators to execute corrective actions to bring the demand-supply network back in control.

Finally, as a history of corrective actions is collected, Prescriptive Analytics are developed to guide operators to correct the problem and resolve issues. Or, advanced process control algorithms can be developed for the system to take corrective actions itself (self-correcting applications).

Regardless of the state of your analytics maturity, it takes time to advance to the next level. At each level, significant competitive advantage is gained from the insights provided. According to most industry benchmarks, leaders already have a 50 percent cost advantage over even their median competitors. Add market and consumer intelligence, demand-supply pricing, and other market share gaining initiatives and the laggards may be facing an extinction event.

The Outlook

In 2018, market leaders deploy their digital strategy advancing to descriptive, monitoring and diagnostic analytics. For example, Procter & Gamble uses point-of-sale data to forecast demand daily to identify variability from plan and adjust its production accordingly resulting in significant cost advantage. Amazon uses in-line optimization in its order and fulfillment systems to balance inventory across their network. As more companies implement advanced analytics, catching up by laggards becomes insurmountable.

Will Advanced Analytics Leveraging Digital Technology Be An Extinction Event?