Subscribe to the Supply Chain Planning Blog

Keep up with the latest trends, research, and insights about supply chain planning, demand forecasting and inventory optimization.


How Gartner Sees the Future of Supply Chain Planning

By Jeff Bodenstab7 Jun 2016

Source: Algorithmic Supply Chain Planning: The Future of SCP, Amber Salley, May 2016

Gartner has taken a look at the future and sees lots of algorithms. The analyst firm says algorithms “are now feasting on the wealth of data becoming available, leveraging the huge computing resources in the cloud and becoming a pivotal source of competitive differentiation.” They have now grown to the point where Gartner considers them a “significant disruptive trend.” *

At the recent Gartner Supply Chain Executive Conference, two analyst presentations described the impact of algorithms on supply chain planning, and included three ToolsGroup clients as examples. Michael Burkett presented Supply Chain 2025: Planning the Future Supply Chain; followed by Amber Salley presenting Algorithmic Supply Chain Planning: the Future of SCP. Before describing what they see as they look into the future, here first are a few examples of well-known business algorithms:

  • Google’s PageRank, the algorithm that virtually defines the company
  • Amazon’s recommendation engine
  • Goldman Sachs’ trading algorithms
  • Variable-pricing algorithms in the airline industry and elsewhere
  • Customer specific offers as part of retail loyalty programs

When applying algorithms to supply chain planning, Burkett stressed the importance of beginning with a focus on the customer experience. Customers and data are the keys, he says. This is the basis for an understanding of where demand is coming from, and what products and services businesses and consumers want.

Connected data helps, he says. Gartner estimates there will be 50 billion connected end points in the marketplace by 2025—a 10-fold increase from today.

Software with learning capabilities can help you understand this data to predict demand and inventory needs. Burkett cited Lennox as an example of a company using machine learning as part of demand planning to find patterns in data and better predict demand, even when faced with a broad product mix and seasonal variation.

Burkett sees humans and smart machines interacting in an “algorithmic business,” taking advantage of algorithms “to improve business decisions or to improve the automation of a process.” Burkett calls this convergence of people and machines the “augmented workforce”, central to a future framework that exploits big data.

He says this connectivity is an extension of Metcalfe’s Law, which states that the value of a network is proportional to the square of the number of connected users of the system. This value increases exponentially with the Internet of Things (IoT)—and the higher-value outcome in supply chain planning is stronger predictive and prescriptive analytics.

Amber Salley dove deeper into algorithmic Supply Chain Planning. She says that algorithmic SCP software enables firms to decode all the data streams coming into the business, understand the patterns, and align the supply chain to respond in kind. She sees a more continuous, self-adaptive and automated environment.

One company doing this today, Salley says, is Costa Express, who captures data at its fresh-brewed-coffee kiosks across Europe, uses demand planning software with algorithmic technology to identify stands with excess capacity, and then shapes demand via promotions to coax customers to machines with extra product. Algorithmic planning also helps Costa forecast incidentals like as sugar and cream, lowering inventory costs and revealing consumption patterns over time.

These algorithms constantly learn—with the help of the human workforce. “Planners themselves are moving away from actually doing the work to managing the algorithm itself,” Salley says. As overseers of the increasingly automated planning system, planners will help direct the algorithms by tweaking their logic.

She cited Internet retailer Wayfair for using algorithmic planning to support replenishment. Demand planners taken a step back and switched from creating forecasts to being what Salley calls “market advisors.” The company dramatically improved forecast accuracy and lowered unhealthy inventory by 50 percent.

Key steps toward algorithmic planning? Salley says understand how the IoT will open visibility into your supply chain and how smart machines will help you gain insight into events; how different data types will raise your supply chain knowledge; and how evolving SCP will help you segment your interactions with omni-channel customers.

Explore Algorithmic Business to Drive Differentiation, March 9, 2016, Stephen Prentice

Subscribe to the Supply Chain Planning Blog

Keep up with the latest trends, research, and insights about supply chain planning, demand forecasting and inventory optimization.

Supply Chain Brief