As supply chains become more digital, it makes sense to leverage the massive amounts of data being generated by digital transactions in the planning process. At the same time, planners need to remember that technology doesn’t mean planning fundamentals no longer apply. Lora Cecere (@lcecere), founder and CEO of Supply Chain Insights, believes too many supply chain professionals have forgotten the fundamentals of planning in the digital age. She writes, “We have lost the ability to have a discussion on the fundamentals. It drives a supply chain planning gal like me crazy.”[1] At its most fundamental level, Cecere states, supply chain planning is about managing inputs into a data model to drive outputs. The goal, she writes, “should be about the ‘success of the model output’ to drive value.” For years, Cecere has advocated outside in planning. By that she means planning must take into account all of the demand data being generated outside of corporate boundaries. Leveraging both internal and external data fosters increased planning alignment throughout the supply chain.

Fostering supply chain alignment through cognitive planning

Jeff Bodenstab, Vice President of Marketing at ToolsGroup, reports Gartner clients are increasingly asking, “What will the future of supply chain planning look like?”[2] Gartner tried to answer that question in a report entitled “Technology Reference Model for Stage 5 Maturity Supply Chain Planning.” According to Bodenstab, Gartner analysts see three “mega trends” driving their vision of supply chain planning. Those trends are: horizontal alignment, vertical alignment, and automation. According to Bodenstab, Gartner analyst Tim Payne views supply chain planning as a decision making process. Payne believes when the three mega trends converge in the planning process, they can help companies achieve Stage 5 Maturity in supply chain planning. Bodenstab briefly describes each of those trends.

• Horizontal alignment – “[Horizontal alignment] means ‘joining up’ planning decisions across the E2E supply chain. For example, [Payne] says, ‘today most companies do not want to make demand-planning decisions in isolation of their ability to support these forecasts from the supply side of their supply chains.’ Over time, he projects, this horizontal alignment will extend beyond the four walls of the enterprise and out into customers and suppliers to enable multi-enterprise capabilities.” Horizontal alignment is similar to what Cecere calls outside in demand planning.
• Vertical alignment – “In this case,” Bodenstab writes, “the ‘joining up’ planning decisions is from the top of the company to the bottom and vice versa. ‘This is how strategic objectives are turned into execution and how execution could eventually influence strategic direction,’ [Payne] says. For example, S&OP decisions are not taken in isolation of operational planning decisions.” Vertical alignment requires breaking down data silos so everyone in the enterprise is working from a single version of the truth.
• Automation – Bodenstab explains, “This is automating predictive and prescriptive planning decisions. Payne says that as companies proceed on their digital planning initiatives, they often cite improving planner productivity as one of the key drivers supported by programs. Examples include ‘zero-touch planning’ or ‘lights-out planning’.” Clearly, Payne believes some sort of cognitive system is essential for enterprises to achieve Stage 5 maturity.

Cecere suggests, “A business decision for supply chain planning should focus on the selection of the technology that can drive better business outcomes.”[3] She recommends nine concrete steps companies can take when deciding what supply chain planning tools they should buy. Any company wanting to improve their planning processes should carefully read her article. She concludes:

“I am very excited by the level of market innovation happening in the market today. We are at the juncture of software planning redesign. This includes:

• Schema on Read.
• Cognitive Computing.
• What-if Analysis and Redefinition of Planning.

New solutions — Aera, Anaplan, Bluecrux, Enterra, Lokad, O9, Rulex, and — offer new possibilities. I also find the evolution of deeper optimization/machine learning within the John Galt, Gains Systems, Kinaxis, and ToolsGroup solutions promising. We are moving towards the autonomous supply chain and the redefinition of planning. It is a step change.”

Obviously, I’m pleased Cecere included my company, Enterra Solutions®, in her list of innovative companies providing new solutions.

The benefits of cognitive technologies in planning

As noted above, Cecere believes cognitive computing is one of the exciting new technologies with great potential to improve supply chain planning. My definition of cognitive computing combines Semantic Intelligence (i.e., natural language processing, machine learning, and ontologies) with computational intelligence (i.e., advanced mathematics). Shaun Phillips, Product Manager for QAD, writes, “The case for adopting machine learning techniques [in supply chain planning] is logical. It is faster, cheaper, more accurate and is less human input for higher quality output. The case is very obvious.”[4] He continues:

“At its essence, supply chain planning is about balancing supply and demand to achieve alignment with a corporate strategy. To achieve this balance, supply chain planners are bombarded with large volumes of both historical and real time data. With that data, one can identify trends, outliers and exceptions. That data, however, also raises a large number of questions to be answered including:

• How much or less do I need to sell?
• How much capacity will I have?
• How long will it take to deliver?
• How will this new product launch go?

Having enough of the right kinds of data allows supply chain planners to make informed decisions. The more data, the more informed the decision making process. This is where machine learning comes to play.”

Steve Banker (@steve_scm), Vice President of Supply Chain Services at ARC Advisory Group, notes, “While artificial intelligence (AI), particularly machine learning, has been used in supply chain applications for some time, there is an ongoing arms race to more effectively leverage both machine learning and artificial intelligence in demand planning solutions in new ways.”[5] The concept of using machine learning for planning, Banker insists, is simple. “Machine learning works by taking the output of an application (for example, a forecast), examining that output against some measure of the ‘truth,’ and then adjusting the parameters or math involved in generating the output (forecast), and seeing if the adjustments lead to more accurate outputs. It can truly be said that the machine is learning from experience.” Obviously, getting the kinds of results we’re discussing here to achieve what Gartner calls Stage 5 maturity is not easy; but, cognitive technologies are making it easier.

Concluding thoughts

Alexa Cheater (@Alexa_Cheater), Product Marketing Manager at Kinaxis, observes, “While examples of AI and machine learning in supply chain planning are few and far between, that doesn’t mean folks aren’t making progress in this area. … The wave of new AI-enabled technology is only growing, and coming with it is a surge of opportunity related to supply chain planning. Companies that can see past the hype and put a solid supply chain planning foundation in place now will be better equipped to utilize AI in the future to drive real value that matters.”[6] Payne calls this wave of new AI-enabled technology “algorithmic supply chain planning.” Bodenstab explains, “This is a far reaching concept that includes a host of capabilities such as insight (to support logical thought, conscious exploring of options and automated responses), configurability (adaptive to new environments), algorithms that support accuracy and resiliency, connections that support environmental signals (e.g., IoT data) and the planning layers. [Payne] also cites other emerging technologies such as advanced analytics, deep learning, and data lakes.” One common thread among subject matter experts is optimism. They agree that cognitive technologies are going to improve supply chain planning and make it easier as well.

Footnotes
[1] Lora Cecere, “What Is Planning?” Supply Chain Shaman, 6 March 2018.
[2] Jeff Bodenstab, “Three Mega Trends Driving Gartner Stage 5 Supply Chain Planning,” ToolsGroup, 30 October 2018.
[3] Lora Cecere, “Yowza! A Nine-Step Decision Process to Help Guide Supply Chain Planning Selection,” Supply Chain Shaman, 24 September 2018.
[4] Shaun Phillips, “The Case for Machine Learning in Supply Chain Planning,” Supply & Demand Chain Executive, 10 December 2018.
[5] Steve Banker, “Artificial Intelligence in Demand Planning,” Logistics Viewpoints, 5 December 2019.
[6] Alexa Cheater, “Cutting through the hype: AI in supply chain planning,” Kinaxis Blog, 29 September 2017.