Touchless supply chain planning – what makes it different?

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Frictionless planning models leverage touchless, continuous, and AI-based processing, making it possible to work in a radically different way.

In the first article in this short series, I outlined the challenges of present-day supply chain planning, and I briefly described how a frictionless approach can become a competitive differentiator, both in terms of streamlined internal operations and improved customer service.

This time, we’re going to dive a little deeper into how touchless supply chain planning differs from current approaches.

Traditional vs. frictionless

Frictionless planning models make it possible to work in a radically different way. Let’s make some comparisons.

Traditionally, processes are siloed: demand here, and supply there; planning here, and execution there. The focus is mostly on the here-and-now, or on the medium term at best – and if things go wrong, the firefighting can be considerable. But with frictionless planning, most of the time-consuming processes are touchless and continuous, allowing the focus to switch to the medium term. Also, because data can be drawn from across the business, insights can deliver long-term value, and assist strategic decision-making.

Traditionally, the approach is reactive and control-based. Metrics focus on efficiency and effectiveness, and two-dimensional segmentations aren’t often revisited. By contrast, frictionless planning models are smarter. Enterprise-wide insights and analytics accelerate decision-making, and intelligent, automated routines enable multi-dimensional segmentations to be spun up to order, and for different purposes. Once again, the focus is on delivering value to the business.

Traditionally, the supply chain workforce is structured in the form of a pyramid, weighted by volume, and with a standard support model and team design. Emphasis is given to industrialized transactional handling in processing hubs – whereas the frictionless approach enables a mix of hub and market planning organization to be established, so as to achieve standardization, to support scalability, to provide market intelligence, and to facilitate collaboration. It’s an AI-augmented workforce, where the planning architecture takes charge of managing the end-to-end workflow, and where people are assigned to tasks not by transaction volumes, but by exception – because most, if not all, the heavy lifting is done touchlessly.

Traditionally, controls are centralized and somewhat rigid. With a frictionless model, controls are automated, risk-based, and dynamic, adapting to circumstances, and any manual interactions and approvals are addressed by AI-augmented team members. The technology supports an intelligent ecosystem that incorporates digital twins, which can be used to develop try-outs and rules from which further automation can be introduced.

To see the effect of all these differences, let’s take demand planning as an example. Traditionally, this is addressed by individual SKUs (stock-keeping units). A frictionless model can consider a larger number of variables from the extended enterprise to influence the forecast, and can also make adjustments to that forecast using real-time point-of-sale signals. What’s more, it does this on a touchless basis, reducing the need for manual intervention to perhaps 30% of cases, so people can focus only on the areas of need.

In short, what emerges is effectively a supply chain control tower, which facilitates agile sales and operations planning (S&OP), provides visibility, identifies alerts, runs scenarios to recommend actions to resolve those alerts, and automates decision-making based on rule-based frameworks.

Real-world success

Like you, I expect, I’ve read plenty of articles that make a case for end-to-end digital transformation – and yes, I realize that in talking about these smart, enterprise-wide models, that’s what I’m doing here too.

But in this instance, at least, it isn’t just business buzz-speak. The benefits of touchless supply chain planning aren’t just possible in theory – they’re already happening.

For example:

  • After transforming its supply chain model, a global CPG manufacturer achieved identified 40–50% of its product portfolio as candidates for touchless demand planning using machine learning (ML) forecasting
  • A European beverage manufacturer achieved 25–30% relative reduction in forecast error, 10–14% inventory reduction, and 20% planners’ time release from demand sensing
  • A global CPG enterprise achieved 72% no touch purchase order (NTPO) compliance from a starting point of 39%, by improving master data, planning system parameter tuning, and loss tree analysis
  • A global industrial leader had too many processes, systems, and long-lead times in responding to customer needs (over 8 months). The business carried too much inventory ($5 billion), which led to much waste through product obsolescence. The company saved 25% in inventory costs within the first year of adopting an integrated solution

In the third and final article in this series, I’ll be looking at deploying touchless planning across the extended enterprise.

To learn more about how Capgemini’s Touchless Supply Chain Planning can transform your organization to drive enhanced customer experience and reduced cost, contact: sandip.sharma@capgemini.com and shaun.cheyne@capgemini.com

Sandip Sharma, CPIMSandip Sharma is a Senior Director and leads Capgemini’s Business Services end-to-end Touchless Supply Chain Planning capability. He also works with clients to create compelling transformation solutions and services to design, run, and evolve their supply chain operations.

 

Shaun R CheyneShaun Cheyne is a Director at Capgemini Invent and leads the UK’s Consumer Products Supply Chain capability. He works with clients to enhance business value through supply chain transformation from strategy to implementation.

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