No one has to make supply chain professionals aware of the complexities they’re facing right now. The bigger question is, what technology is most effective at helping solving problems when — and even before — they occur?
Clearing supply chain bottlenecks requires situational awareness, and situational awareness starts with data. Every link in a supply chain, whether upstream or downstream, generates data. Raw materials, shipments, inventories, equipment utilization, orders, revenue and many other components produce vast information. Measuring the rate of change in these often millions of data points across time and geography is a primary discipline for supply chain management.
Combining data from external sources adds more valuable context. Today, many trading partners share data so that decisions aren’t made in a vacuum. Vendor, wholesaler and customer data place activities in the framework of the larger business environment, which benefits everyone in the chain.
Yet there is a gap between data and understanding. The ability to see these rates of change, and the impact of dozens of variables on various suppliers, channels and end-users, are critical to strategy development and decision-making. Moreover, executives and planners must be able to generate this understanding when it’s needed; the right insights need to be delivered to the right people, at the right time.
Not everyone is schooled in the art of analytical data discovery, but today, everyone needs understanding. Logistics, inventory controllers and supply chain managers may not know how to slice and dice using analytical tools. Nonetheless, they need understanding the impact of that data, to do their jobs more effectively.
Discovery through analytics
A major goal of analytic solutions should be to raise important changes in data, then provide a mechanism where decision-makers can examine them, discuss, and where necessary, do something about them.
Even the most sophisticated dashboard is of no use if insights are buried amid mountains of data. It’s vital to be able to expose impactful information quickly. Top line data may tell a furniture manufacturer that its inventories of a particular fabric color are sufficient; but if schedulers don’t know that their Boise factory has a 12-week supply while their Charlotte facility is down to six days, the there’s likely to be production issues very soon.
Contextual analytics, the latest business intelligence (BI) innovation, goes even further than traditional analytics by offering automated analytics and data built into an individual’s workflow. As a tool for more effective supply chain management, contextual analytics provides major benefits. First is to identify and isolate KPIs; amid multiple data points, which one or two that actually drive activity? Which pieces of information, if surfaced, are essential to resolving a supply or delivery problem? Second is the ability to act on those insights by embedding analytics within the decision-maker’s workflow. Instead of spotting a low inventory item and then switching over to an online purchasing system to place an order, contextual analytics gives users the ability to simply click on the item within their inventory management system and place the order, saving needless back-and-forth.
A further benefit is the use of algorithms and machine learning. This technology often works in the background of contextual analytics to support the processing of vast quantities of data and producing predictive models. Such models, for example, can predict stock outages by looking at trends in the market or pinpoint machine idle time inefficacy across a fleet of devices. Understanding immediately when a minor trend is likely to cause a major issue so organizations can pivot quickly to mitigate damage.
To make the most of contextual analytics, look first if you have vast amounts of data that will help to answer key questions for your organization or improve processes. If you’re a manufacturer with thousands of product SKUs, for example, you’re often at risk for out-of-stock events. Automating analysis can help to reduce or even end out-of-stocks by replacing outmoded manual processes and delivering insight directly to inventory managers, where solutions can be implemented.
Challenges with COVID-19 continue
Bottlenecks may never disappear completely, but contextual analytics can help understand when and where they are to occur and provide the ability take action within supply chain applications that are used today.
Furthermore, the Coronavirus disease (COVID-19) impacts are still ever changing in different parts of the world, and in turn, ever changing impact on supply chain. In one industry after another, shortages and delays are still surfacing. Supply chain executives need new ways to identify and even anticipate the causes of local, regional and global challenges, and take the steps necessary to keep materials and goods flowing.
The critical goal is helping supply chain personnel to bubble up changes in in real-time, understand their potential impact and then determine and execute appropriate remedial action. Contextual analytics can play a leading role in this challenge. By looking at what you’re trying to achieve, automatically identifying and surfacing insight and providing a vehicle for immediate action.