In this post, I’m going to ask you to consider a scenario from two different standpoints.
You need to imagine for a moment that you’re the manager of a sports team. There’s a big game coming up. You want to make the most of your chances on the day.
So, from the first standpoint, you do some homework. You look at what happened last time you faced those opponents, how their team has changed since then, how yours has, what the conditions will be like on match-day, and how you’d like the game to pan out. It takes shape on paper.
Congratulations: you have the traditional makings of a plan.
Traditional demand planning
What I’ve just described is pretty much how demand planning typically works. It’s a two-stage process, in which historical sales form a baseline forecast, on which known future events such as promotions are overlaid.
There are problems with this approach, and they all relate to the fact that it needs human intervention:
- Its availability and quality is determined by the speed and capability with which people can capture and process information
- It’s subject to manual error at the point of data injection
- It’s prone to optimism and personal bias: for instance, the pressure of sales targets may outweigh the likelihood of their achievement.
For these reasons, forecasts calculated using traditional demand planning methods tend to be accurate only 50% to 60% of the time.
Now let’s consider the same scenario from the second standpoint. This time, we don’t bring personal perspectives into it. We still look at the previous match, and at the current players in their team and ours, and also at the conditions on match-day – but we do it objectively. We keep our hopes and fears out of it, we bring in much more information than before, we update it every time something changes, and we analyze everything computationally.
Once again, we have a plan – but it’s a much better one than before.
This second route constitutes advanced-demand forecasting. It’s more accurate, because it uses artificial intelligence (AI) and machine learning (ML) to recognize historical patterns, select best-fit statistical models, and draw on a variety of inputs and forward-looking variables, such as promotional grids, sell-out data, and environmental factors, to create more accurate demand forecasts. There’s less manual effort – and less wishful thinking. Or pessimism. (Depending on your natural outlook on things.)
The supply-value network
Advanced-demand planning is a key element in the drive towards touchless planning – a self-governing, self-optimizing process that makes use of intelligent automation applications and big data to improve the speed at which plans are created, reviewed, and adapted in response to real-time changes in demand and supply.
It means shifting from the concept of a traditional supply chain to a supply-value network, which enables growth, optimizes operations, and improves service, while reducing costs and working capital.
OK, this new model is more complex, because it involves managing the flow of materials, products, and data between and among a growing number of ecosystem partners – all of which must be coordinated to maintain stability in the network – but complexity isn’t a problem, because the AI and ML functions within the model can handle it all seamlessly and without human intervention.
One of the great things about operating at this level is not just that automated intelligence helps you to plan – it also helps you not to plan. For instance, it can tell when a situation does require a planning response, and when a shift is inconsequential and can safely be ignored. Knowing which is which can save a lot of hassle.
In later posts in this short series, I’ll be looking at how to establish a base on which to build a touchless planning model, and at how to factor in your organization’s culture. I’ll also provide some top-line advice on the best approach to take.
All of which will, I hope, improve the chances of your own team as you get ready for the next big business fixture.
Read other blogs in this series:
Learn more about how Capgemini’s approach to continuous touchless planning provides a framework for organizations to develop and deploy capabilities and processes across the business to deliver new levels of speed, accuracy, and responsiveness.
To learn more about how Capgemini’s Digital Supply Chain Practice can help your organization implement a supply-value network and touchless planning across your supply chain, contact: email@example.com
Jörg Junghanns leverages innovation and a strategic and service mindset to help clients transform their supply chain operations into a growth enabler.