Quentin Roach, chief procurement officer at pharmaceutical company Merck, doesn’t think any organization should use all emerging technologies. “It’s a matter of picking the ones that have the greatest impact in your particular process today, the culture of your organization and the way your end-to-end supply chain can really take advantage of these new technologies,” he says.
Quentin is right. Of course, companies shouldn’t digitalize just for the sake of digitalizing. However, very often they do. And in the rush to all things digital, companies sometimes assume more digital is better than less.
Changing Conversations About Digital
It seems there is not a single day that I haven’t had a call or meeting with clients to discuss supply chain digitalization strategies. Over the past few years, however, I have seen the focus of these conversations shift from an initial interest about understanding what digitalization is, toward a more practical discussion about how can we put together a digital roadmap. Lately, I have seen another shift — a sharp increase in inquiry calls aimed at discussing what the business outcome of digital supply chain is.
This evolution makes a lot of sense. It’s not digital for digital anymore. As digital supply chain is leaving the hype and entering a phase of pragmatism, CSCOs are asked to provide business justification to get funding.
This isn’t easy at all. You can’t just measure digitalization, per se. There isn’t any value in measuring the increasing amount of bots or Internet of Things (IoT) devices you are deploying. You should measure to what extent digital capabilities power supply chain processes. So you need to make the connection between digitalization progress and what business outcome you can get out of it.
Figure 2 provides an example of how to “translate” digital initiatives into business outcome. For example, an initiative aimed at digitalizing the demand forecasting process might have a digital target of increasing the share of forecast that is generated automatically through machine learning. However, what matters is how that digital target is translated into the expected business outcome.
This approach is well explained in the excellent Gartner note, “Expand Metrics to Ensure Value From the Digital Supply Chain,” which provides guidance on how to measure success in digital transformation initiatives (Figure 3).
Learning From Leaders
The most difficult thing to do in this approach is to determine the right business targets. They must be challenging, yet they must be achievable within the desired realization timeframe. Learning from community members about the business outcome they measured is a great starting point for properly setting your own targets. Some examples:
- United Kingdom grocer Morrisons uses artificial intelligence (AI) to improve short-term forecasting and reinvigorate daily replenishment orders based on store-specific influences, including consumer buying patterns, promotions and weather conditions. In a year implementation, it improved shelf availability 30% among 26,000 SKUs.
- Intel’s autonomous planning capability uses AI to autonomously analyze results from the planning engine and select the right planning scenario based on accumulating supply chain knowledge and expertise, cycle after cycle. In less than a two-year implementation, 50% of products are planned fully autonomously and tens of thousands of planner hours were reinvested in more added value activities.
- Network equipment maker Ericsson uses robotic process automation (RPA) to automate its global order management process. In less than a year implementation, the process is 80% automated. For every 100 inputs given to the bot, 80 are handled automatically and 20 require manual intervention.
- Arrow Electronics uses drones throughout its warehouse operations to support Lean Six Sigma. Drones provides a higher vantage point to observe warehouse operations. With this quick win implementation, the company reduced 6.6 million walking steps yearly and increased productivity by 82%.
- AB InBev uses AI to improve its beer filtration processes. This allows the company to make its beer more consistently tasty, at a reduced cost and in less time. After evaluating six months of data at its Newark, New Jersey, brewery, the AI engine reduced errors in determining the best time to replace filter media by a staggering 400% over manual predictions.
Digital is revolutionizing the way we run supply chains. It’s a not-to-miss opportunity. However, don’t get caught in the hype. Make digitalization a business outcome.