It goes without saying that the priority of any COO is to improve performance within the value chain, whether that’s reducing costs, improving service, lowering working capital or, more realistically, a combination of all three. 

There are now a myriad of ways that digital technology can be applied across the value chain. Whether its businesses looking to automate repetitive back-office tasks with robotic process automation (RPA) software, the use of cognitive solutions to anticipate future demand, or visibility solutions being used to identify and address continuity issues, the opportunities are endless. Sometimes, however, this broad spectrum of opportunity can lead to confusion. When we talk about digital strategies, are we referring to specific software solutions? Cloud-based technologies? Robotic innovations? Are we looking to optimize existing strategies, or transform an entire business model?

It doesn’t have to be complicated. When it comes to the performance of a supply chain, it’s really just a function of two things; how efficient the processes are, and how effective the decision making is. No matter how well an organization streamlines its processes and optimizes its operating model, if it still makes bad decisions its performance will suffer. Conversely, if an organization is adept at decision making, but doesn’t have the necessary processes to execute them, then it will perform poorly.

That’s why we believe that companies need to define their performance ambition, before they look to the factors that hinder their ability to meet that ambition. Only then should digital process and technology innovation be considered as a means to address these problems. The sole focus of any digital strategy should be to improve performance.

Looked at through the lens of frictionless processes and effortless decisions, the broad list of digital technologies becomes clearer. Those aimed primarily at process improvement, such as physical robots or indeed intelligent automation, tend to be ‘point solutions’ and relatively straightforward to implement. Others lend themselves to improved decision making, such as data analytics, blockchain, artificial intelligence, machine learning, or internet of things (IoT) devices. These technologies, however, tend to be components rather than complete solutions, and need to be harnessed in combination to deliver their full potential. This is where ‘Decision Management’ is emerging as a crucial capability.

Almost every executive has, at some point, mapped their processes and knows the value of simple tools like brown paper and post-it notes to gain a fresh perspective on existing activities. Until now, however, very few leaders have been able to look at supply chain decision making in the same way. Without this competency, they tend to see analytics as one-off projects looking for insight in existing data pools. In doing so they miss the opportunity of exploring advanced analytical solutions and the real value that comes from predictive decision support.