When I first started working in supply chain, back in the 90s, data availability and technology were nowhere near what we have today. I was fortunate enough to work with Alan Braithwaite, who created the initial cost-to-serve methodology. There were significant challenges then, but after months of hard work, we generated a cost-to serve model that often led to some surprising results and strategic choices. This was similar for all supply chain professionals. Projects were long, exhausting and expensive, involving large teams of consultants. Collecting data was a hassle to say the least. Fast forward to 2018 and we are at the onset of a digital supply chain transformation. How exactly is technology simplifying the supply chain for the better?
Simplifying data collection
Gartner predicts that by 2020, over 20 Billion devices will be connected. With the Internet of Things and IT integration projects, data collection has gotten somewhat easier. But this has also created other challenges: data overload and quality issues. When you buy supply chain optimization software, you still need to hunt around for data to make the technology work. It often feels like you work for the technology vendor, not the other way around. That’s why 60% of the time spent in analytical supply chain projects is spent collecting, cleansing and formatting data.
What if instead of trying to fit your data to the technology, the technology’s architecture would be designed around satisfying your broad supply chain needs? Imagine building a comprehensive and robust supply chain data set with the help of a wizard that tells you exactly which data you need to answer the questions you have? This is now within reach. At AIMMS, we’re simplifying the data collection process with Data Navigator, an integrated cloud database that brings structure to complexity and enables you to share data across multiple functional areas.
I expect to see data architecture moving in this direction, as it makes it easier to deploy additional solutions, test new things, and embed a new strategy in your business very quickly.
Simplifying data analysis
In the past, it could take hours or (dare I say) days to solve a supply chain model. Today, thanks to advanced computing power, we can solve a model in seconds. Still, not all organizations are making use of this speed. Clunky systems that need to be managed by experts still abound. The promise of real-time, self-service analytics is elusive.
Take S&OP support software, for instance. We held an S&OP workshop at a recent conference in Amsterdam where we simulated a Capacity Review meeting. During the workshop, we learned that virtually all attendees had a pain point in common: they always had to go back to their desk to test different scenarios after an S&OP meeting. None was able to perform scenario comparisons in real-time to discuss insights with stakeholders during the meeting. Some also lacked advanced analytics and optimization capabilities.
New self-service technologies enriched with prescriptive analytics, such as AIMMS S&OP Navigator, allow you to perform easy and real-time scenario management without data science expertise. With this capability, you can run and compare multiple scenarios side-by-side in the executive meeting at the end of S&OP and make conversations richer, more entertaining and fluid. Moreover, your meetings will be more strategic, rather than focused on granular, operational things.
Besides bringing data analysis closer to end (business) users, technology is also simplifying collaboration in supply chain. Most organizations start their supply chain analytics journey by using spreadsheets, using data from transactional systems. Spreadsheets are great for personal productivity and they tend to work well for the person that builds them, but what happens when you work in a team of 100 spread across different regions? As more and more people get involved, more versions of these spreadsheets get shared. Sharing knowledge is difficult and the original logic behind each spreadsheet is often hard for others to comprehend.
Cloud based analytical systems that allow for version control are simplifying collaboration across the supply chain. Replacing spreadsheets with a central tool allows people to gain a lot more insight into their business. Centralizing this data and providing wide access has been one of the biggest benefits AIMMS clients like BP have achieved.
Basing complex supply chain decisions on common sense has its pitfalls, but organizations still tend to rely on gut feel to run their supply chain. With prescriptive analytics, they no longer need to. As long as the system is fed the right data, they should be able to support their decisions on fact and predictable business outcomes. Thanks to mobile technologies, decisions can also be made on the go, regardless of your location or time of day. Thanks to advances in machine learning, it is no longer difficult to imagine a semi-autonomous supply chain, were certain decisions are taken automatically and others are left to human insight.
In short, technology has certainly simplified supply chain throughout the years. But as much as that is true, technology alone is not going to revolutionize supply chain management. The key factor for success is people, and the commitment of supply chain leadership and teams to leverage new, intuitive technologies to improve their supply chain strategy and execution.
Chris Gordon has worked in supply chain consulting and operations across Europe, the US and India for over 25 years. He’s driven dozens of supply chain initiatives in retail, consumer goods and manufacturing with a heavy focus on leveraging analytics to promote major business change. He is passionate about making supply chain analytics accessible to everyone to drive data-driven, factbased decisions to drive major strategic and operational impact.