It doesn’t matter whether you have a supply chain spanning hundreds of suppliers and multiple continents, or a local network, data needs to be considered holistically.
Every time you buy something online, use your phone for directions or go for a run with your fitness tracker, data is being collected. What happens to this data depends on who gathers it. Some companies target you with personalized ads, others tell you about a local restaurant you might like or suggest a new running route.
Other companies do nothing with it. Just store it for years like compulsive collectors. Why? Part of it is the fear factor; “Everyone is collecting data, we should be doing the same.” However, a bigger challenge is refining colossal amounts of information to find value. And when it comes to complex supply chains, you could be looking at millions of data points to filter through.
It doesn’t matter whether you have a supply chain spanning hundreds of suppliers and multiple continents, or a local network, data needs to be considered holistically. Raw figures, such as the acceptance rate of loads per carrier, transport order rejections by lane and average fill rate of trucks, can contain patterns that might unlock key savings and efficiencies. The problem is spotting them.
The human mind is not designed to analyse large data sets. You may well have confirmed this last time you tried to digest a particularly large Excel file with little or no context. We are much better at understanding charts and graphs and using high level snapshots to identify which areas merit more digging. Data visualization tools, such as Alpega Analytics & Reporting, let you do just that.
They make the complex simple and provide a complete view of a process or workstream, allowing you to pinpoint the most interesting areas for a deep dive. They can pull together information from multiple formats and sources and help uncover how seemingly unrelated events impact each other. These insights can then be further developed to find savings, efficiencies and build stronger relationships with key suppliers.
This approach is also very effective when it comes to getting sign-off for initiatives or buy-in from partner organizations. It’s much easier to explain an illustration than it is to talk through a document.
Supply chains are more and more complex, but also leaner than ever before. Margins are tight and the days of boosting profit by negotiating with suppliers are coming to an end. The arrival of technologies based on A.I. and machine learning, alongside an increasingly globalized and connected environment will only serve to boost the amount of data companies collect.
Data-driven decision making has proven its value time and time again. The algorithms devised by companies such as Amazon and Netflix, for example, are a key component of their success. The same is true of supply chain optimization, where luminaries such as Apple’s Tim Cook, have long seen the value of data-driven supply chains.
It’s up to you to make sure this information doesn’t lie dormant in a virtual file cabinet, but is considered, broken down and actioned.