Decision Support in Supply Chain Planning

Stephen DeAngelis

June 12, 2019

Many supply chain pundits shake their heads in wonder as companies continue to operate with data locked up in silos. Data silos are a remnant of industrial age organizations and industrial age thinking which focused on optimizing various business functions. Siloed data and silo thinking are also found within the supply chain. Lora Cecere (@lcecere), founder and CEO of Supply Chain Insights, writes, “The broader concepts of connecting from the customer’s customer to the supplier’s supplier are largely forgotten. In essence, the implementation of ERP, along with CRM, APS, and SRM, reduced supply chain to make this silo efficient in a world focused on functional excellence.”[1] She believes the way to free both data and thinking from silos is to leverage decision support systems. She explains, “I believe that the new market is decision support. This is much broader vision than the more limited supply chain planning solutions we have today. How so? Decision support enables the use of analytics to improve decisions by a broader group of employees than planners. In many global supply chain leaders, planners are 5-10% of back office employees. The point? What good is data if it is not usable for the larger organization?” This holistic view of supply chain planning aligns it with the realities of the digital age.

Decision support and planning

Cecere’s decision support vision is important for businesses as a whole, not just supply chain planners. Bain analysts, Michael C. Mankins and Lori Sherer (), assert if you can improve a company’s decision making you can dramatically improve its bottom line. They explain, “The best way to understand any company’s operations is to view them as a series of decisions.”[2] They add, “We know from extensive research that decisions matter — a lot. Companies that make better decisions, make them faster and execute them more effectively than rivals nearly always turn in better financial performance. Not surprisingly, companies that employ advanced analytics to improve decision making and execution have the results to show for it.” I think Cecere agrees with those views. She explains her vision of decision support in supply chain planning this way:

“It is a step-change, not an evolution. The new solution has the characteristics of:

  • Open-source schema on-read architectures like Lokad.
  • In-memory modeling like Kinaxis.
  • Deep optimization like OM Partners.
  • Digital twin like Llamasoft.
  • Client loyalty like SAP.
  • A cloud-based architecture like Oracle.
  • Search like Thoughtspot.
  • Workforce collaboration like Anaplan.
  • Cognitive computing like Enterra.

The new solution is outside-in combining engines and rules to activate larger supply chain visions.”

The objective of Cecere’s vision of decision support in supply chain planning is to leverage data to its fullest. Achieving that goal has been challenging. During an interview about supply chain planning in the digital age, the SupplyChainBrain (SCB) staff asked Madhav Durbha, Vice President of Industry Strategy at Kinaxis, “In the past, the biggest dream of supply chain planners was they wanted more information. Now they have the information, but are they making the best use of it? Are they inundated, are they flooded by it, or can they actually turn it into something actionable to create more accurate plans in the supply chain?”[3] Like Cecere, Durbha sees a real need to break data silos and leverage information in a new way. He responded to the questions asked by the SCB staff this way:

“[Supply chain planners] are absolutely flooded with a lot of information. The data sources and the types of data are exploding. … What’s limiting them, from a supply chain planning perspective, is … the technologies and the paradigms under which most of the companies are working today. These paradigms have been around for 20 years. These are typically batch-oriented, you do a demand plan, and then you push it over to supply plan. Then you do capacity planning, so on and so forth. In such a very linear cadence-oriented processes, where there are multiple handoffs, there’s typically a lot of silos in that and a lot of latency in that. But the need of the hour really is the … the ability to know sooner and act faster, and they’re not able to do that. It’s like engaging in modern warfare, using swords and horses.

Cecere asserts most planners begin planning from the supply side. She believes they should start from the customer side. She explains, “[There needs to be] a focus on customer-centric processes that combine replenishment, transportation and order management optimization logic along with Available-to-Promise and Allocation rule sets.” Her most interesting concepts focus on perspective. She believes too many planners focus on orders as a signal of demand instead of appropriate customer-related data. She explains, “The use of a rules-based ontology to combine customer and product strategies while recognizing that an order is not an order and a customer is not a customer. Using channel data, the solution will enable outside-in processes from the customer back. The new solution will enable the orchestration of substitution logic, and give preference to replenishment based on customer prioritization and market requirements. Today, companies orchestrate replenishment based on orders. The issue? The order is not a good representation of true demand. The new solutions overlay software robots to enable the autonomous supply chain to sense, learn and act while driving bi-directional orchestration.”

Concluding thoughts

Although companies need to lean heavily on data and artificial intelligence, human planners are still required. There’s a reason Cecere stressed “decision support” as the new planning paradigm. Human planners are being supported; but, they need information age tools to help them. Durbha observed, “[Planners, at the very least,] need to run a plan overnight to come back with an answer tomorrow. We live in the world of Uber. When I … ask for a ride, 5 minutes later the car shows up. That’s the world we live in today, and it’s exactly the same paradigm that is needed for planning, as well. They’re not able to do that, the planners are exporting the plans from these batch engines into Excel spreadsheets and trying to do as much as they can within these Excel spreadsheets, and that is causing lot of Excel proliferation within organizations as well. And inconsistencies and scope of increased manual errors, that’s part of the challenge.” The technologies noted by Cecere will help bring planners into the information age and help their companies transform into digital enterprises. Decisions remain a critical part of every business and decision support should be widely embraced at all levels of an organization.

[1] Lora Cecere, “Head Scratcher,” Supply Chain Shaman, 25 April 2019.
[2] Michael C. Mankins and Lori Sherer, “Creating value through advanced analytics,” Bain Brief, 11 February 2015.
[3] Melissa Clow, “Supply chain planning in the digital age,” Kinaxis Blog, 30 June 2017.