Executive Briefings

Success May Turn on Redesign of Decision Support

While over 80 percent of companies have demand and supply planning, success has the same probability as a flip of a coin. How do companies improve the odds? Success may lie in the redesign of supply chain planning from the outside-in using cognitive planning. -Lora Cecere, Founder, Supply Chain Insights

Success May Turn on Redesign of Decision Support

Within the next five years, cognitive computing will transform supply chain planning and other decision support technologies (revenue management and supplier relationship management) to sense and respond through semantic reasoning. This shift will reduce planning headcount by 80 percent and will enable companies to plan at the speed of business. Today’s processes are batch, labor-intensive and difficult to staff. This will change. How do companies get started?

1. The first step is to experiment with cognitive computing to replace the engines in existing systems. To accomplish the goal, start small and then expand. Start with something like demand planning. To build the cognitive engine, partner with a provider of cognitive computing and provide three years of data. Build the engine to use past data to forecast the future. Then enhance the model through unstructured data like weather, pictures and sentiment analysis.

2. The second opportunity is to start to experiment with cognitive computing to drive intelligent rule sets. This includes rules like Available-to-Promise (ATP), substitution logic, allocation, and inventory/order matching. Use the cognitive platforms to drive customer-centric processes based on customer priorities and goals.

3. The third step is to build a planning master database for planning master data and use cognitive computing to read and sense patterns. Planning master data are the parameters required for planning. Examples include lead times, transit times, cycle times, and changeovers. While many companies treat these as fixed values, they are variables with distributions. Most are not a normal distribution.

Cognitive computing offers great opportunity for the supply chain leader to transform decision support and planning. However, it is not an evolution. It requires the redefining of talent, processes and underlying technologies. Evolve slowly through test and learn capabilities. Recognize that the most progress will be made by best-of-breed providers. It is a pivot. The new best -of-breed providers will define the market and the historic supply chain planners will have to redefine their offerings to be relevant. This shift will result in a shake-up of today’s market.

To maximize the value, companies cannot handcuff business innovation teams with traditional project parameters. The ROI, time for implementation and the requirements are unknown. Testing requires innovation at the edge and movement to the core. (Innovate on a business problem that is not core and then translate the insights to the core processes.)

The Outlook

Cognitive computing, within the next five years will be a force in the redefinition of supply chain planning. The new capabilities will enable a faster, more accurate response with less labor. The companies that are the leading edge will gain the most advantage.

Within the next five years, cognitive computing will transform supply chain planning and other decision support technologies (revenue management and supplier relationship management) to sense and respond through semantic reasoning. This shift will reduce planning headcount by 80 percent and will enable companies to plan at the speed of business. Today’s processes are batch, labor-intensive and difficult to staff. This will change. How do companies get started?

1. The first step is to experiment with cognitive computing to replace the engines in existing systems. To accomplish the goal, start small and then expand. Start with something like demand planning. To build the cognitive engine, partner with a provider of cognitive computing and provide three years of data. Build the engine to use past data to forecast the future. Then enhance the model through unstructured data like weather, pictures and sentiment analysis.

2. The second opportunity is to start to experiment with cognitive computing to drive intelligent rule sets. This includes rules like Available-to-Promise (ATP), substitution logic, allocation, and inventory/order matching. Use the cognitive platforms to drive customer-centric processes based on customer priorities and goals.

3. The third step is to build a planning master database for planning master data and use cognitive computing to read and sense patterns. Planning master data are the parameters required for planning. Examples include lead times, transit times, cycle times, and changeovers. While many companies treat these as fixed values, they are variables with distributions. Most are not a normal distribution.

Cognitive computing offers great opportunity for the supply chain leader to transform decision support and planning. However, it is not an evolution. It requires the redefining of talent, processes and underlying technologies. Evolve slowly through test and learn capabilities. Recognize that the most progress will be made by best-of-breed providers. It is a pivot. The new best -of-breed providers will define the market and the historic supply chain planners will have to redefine their offerings to be relevant. This shift will result in a shake-up of today’s market.

To maximize the value, companies cannot handcuff business innovation teams with traditional project parameters. The ROI, time for implementation and the requirements are unknown. Testing requires innovation at the edge and movement to the core. (Innovate on a business problem that is not core and then translate the insights to the core processes.)

The Outlook

Cognitive computing, within the next five years will be a force in the redefinition of supply chain planning. The new capabilities will enable a faster, more accurate response with less labor. The companies that are the leading edge will gain the most advantage.

Success May Turn on Redesign of Decision Support