The creation of any kind of autonomy within the supply chain is totally reliant on the integrity, quality, and consistency of the data it is based on. More specifically within the planning space, a constant supply of perfect supply chain data can have a massive impact on the results delivered by an organization’s planning function. On top of this, the majority of planning requirements now call for shorter, daily cycles that are no longer able to be handled manually and need to be automated – again, the data needs to be perfect.
Planning without automation has now become unthinkable. Organizations must adapt their processes and systems to enable continuous and frequent updates, constant testing of plan validity, and, when necessary, automatic re-planning. We call this capability touchless planning – a self-governing, self-optimizing process that leverages intelligent automation applications – such as artificial intelligence (AI) and machine learning (ML) – and big data to increase the speed at which plans are created, reviewed, and adapted in response to real-time changes in demand and supply. These capabilities enable shorter planning cycles as well as the ability to respond more quickly when necessary to demand and supply dynamics.
To realize the vision of an autonomous planning capability and self-driving supply-value network, organizations must focus on establishing the following foundations within the business:
- Data integrity – the creation and standardization of robust data collection and analysis capabilities that inform the planning system
- Digital operations – as part of this process, some organization have already started to digitize operations, equipping the entirety of the supplier ecosystem with sensors that provide near real-time feedback to the planning systems on various issues, such as production capacity and material availability
- Concurrent planning – the organization will need to enable concurrent planning systems, linking demand and supply systems into a single view, and bringing these functions together within the organization. The rule set will need to be defined and mapped into these systems, thus automating recurring or routine tasks according to program settings and scenario planning.
The combined effects of better forecast accuracy and synchronization of supply with demand drives increased planning effectiveness, improved process and organizational efficiency, and a more stable supply chain, which leads to additional benefits in cost, cash, and service. This all goes back to where we started – supply chain data management excellence.
The pages that follow showcase how Capgemini helped two of our clients overcome in their transformation journey to becoming data-driven champions.