The requirement for fast decision making and the reality of increased data load make a case for Decision Intelligence.
Companies have done an impressive job figuring out how to automate supply chains to prepare and ship customer orders with speed, accuracy, and precision. Amazon, the bellwether for such innovation, employs more than 200,000 mobile robots to move products from warehouse shelves, into boxes, and on to customers. Pitney Bowes, meanwhile, has a massive fulfillment, delivery, and returns center in Greenwood, Indiana where 63 small robots on wheels help process up to 44,000 parcels per hour.
In some ways, this type of automation is easy. It’s what the industry has always done. We offload time-consuming, manual tasks to the latest machinery to get products to customers as fast as possible.
But in nearly every supply chain process, human beings are still required to monitor everything and make decisions when issues arise. And we mostly do that well—except when we don’t.
In a global economy, supply chain management has grown increasingly complex. Planners have to contend with floods of data coming their way on a minute-by-minute basis, and it’s often impossible to keep pace.
As human beings, we make an average of 35,000 conscious and subconscious decisions every day in our personal and professional lives. We don’t have the capacity to handle much more. So, at some point, we either put ourselves on automatic pilot and make decisions least likely to get us fired, become so fatigued that we’re more prone to costly errors.
Each of these scenarios is avoidable. Indeed, a growing number of companies are minimizing such risks by using Decision Intelligence to enhance supply chain decision making.
Built on artificial intelligence (AI) and machine learning (ML) technologies, Decision Intelligence software works by crawling across enterprise systems to find, index, and augment relevant supply chain data. It then tees up recommendations to advise decisions for improving core supply chain disciplines–including forecasting, ordering, inventory, logistics, and procurement.
Experts say the need for greater visibility across systems continues to be one of the highest investment priorities for businesses. In fact, a 2019 survey by JDA Software (now Blue Yonder) and KPMG LLP found 82 percent of supply chain executives polled planned to deploy cognitive analytics by the end of 2020, and 62 percent were investing in AI and ML.
Together, 80 percent of respondents view AI and ML as the most important technology of all “given its wide applicability and promise of addressing complex business problems across the value chain,” the companies reported.
There are numerous ways in which Decision Intelligence technologies can be applied to not only help shorten the clock speed of decisions, but also to help planners make them more effectively.
Here are five of the most common applications in use by organizations today.
1. Labor Scheduling
Supply chain planners typically build work schedules around typical factors such as how many people and trucks they have access to, current demand, and delivery locations. From there, though, it can get tricky as real life happens. People get sick and can’t come to work. Wildfires, floods, hurricanes, or other natural disasters impact transportation routes and timelines. Spikes or drop-offs in demand related to world events, such as the global pandemic or war in Ukraine, necessitate on-the-fly adjustments across the supply chain.
For small and nimble organizations, deciding how to address such changes might not be difficult. But for busier mid-size to large companies, any one of these could be a tipping point for disaster.
Decision Intelligence technologies can help by flagging impending problems and providing data-based labor scheduling ideas to get ahead of them—like locating additional in-house or outside drivers to fulfill orders, and scheduling more or fewer employees to be stocking shelves or loading trucks as demand fluctuates.
2. Capacity Planning
Many third-party logistics (3PL) and fourth-party logistics (4PL) providers struggle to determine exactly how many delivery vehicles they need to support ongoing capacity. In an ideal world, they’d be able to adjust fleet levels every half hour or so to keep pace with the speed of commerce. But nobody can possibly assess all the data and execute smart decisions this rapidly.
Instead, planners default instead to ordering vehicles they think they’ll need on a more manageable weekly or monthly schedule.
But what happens if demand fluctuates and they either over-ordered or under-ordered what they needed? In those situations, either delivery vehicles which the company has paid for are sitting idle, or not enough of them are on hand, resulting in unhappy customers. Either way, revenue is lost. Decision Intelligence can help by predicting variability and recommending real-time improvements to maximize efficiency in capacity planning.
3. Container Space Optimization
Just as airlines do their utmost to avoid having planes departing with too many empty seats, supply chain planners detest when containers go out with any remaining airspace. But it happens all the time.
Part of the problem is that shipments tend to be scheduled for specific days of the week. If containers aren’t loaded in time or there aren’t enough orders justifying the shipments, it doesn’t matter. They still go out, much like those half-empty airplanes we all love when we get a complete row of seats to ourselves.
The bigger issue, however, is that companies simply lack the visibility and transparency to know with any degree of confidence what’s being loaded or not at any given moment. Only 6 percent of them have full visibility into their supply chains, a Geodis survey found. They have a general sense of what’s happening, of course. Just not the up-to-the-minute specificity that would improve overall operational efficiency and minimize the chance that orders fail to ship because they didn’t get into a box on time.
Decision Intelligence can help by collecting and correlating the most accurate data possible about what goods are being loaded where, and when. The solution then correlates, analyzes, and offers recommendations that planners can use to make sure every container is as full as possible when leaving loading docks.
4. Direct Shipment vs. Cross Docking
Since the 1930s, companies have relied on cross docking to ensure that products are distributed to a customer or retailer with little or no handling or storage time. It’s worked for the most part. But with the advent of e-commerce and direct-to-customer sales, the practice is becoming outdated.
Direct shipment is now the way to go for most merchants. It’s faster, it involves fewer hands along the way, and it can save money for businesses and consumers alike. However, to do direct shipment right, companies need very tight control and clear visibility of end-to-end processes.
Decision Intelligence can help by optimizing shipping networks, uncovering opportunities for more efficient transportation, identifying bottlenecks, and automating direct-shipment delay resolution to ensure customers receive their complete shipments on time.
5. Disaster Recovery
Most companies have a disaster recovery plan that touches on supply chain contingencies, ideally preparing them for challenges similar to those we saw during the past two years.
Prior to the pandemic, for many organizations that plan was likely old, outdated and figuratively gathering dust in digital storage.
Today, when global health, economic, and political issues can disrupt business on a moment’s notice, agility is more critical than ever.
You must know, for example, how to reroute delivery vehicles around natural disasters. If you’re delivering perishables in 100-degree temperatures, you’ve got to make sure you’re using refrigerated delivery vehicles or delaying shipments for cooler weather. Also, when you discover a supplier is suddenly out of commission, you need an immediate backup option.
Disaster recovery, by definition, is about having the ability to get back on your feet when the unexpected occurs. But studies show few businesses—especially small businesses—have formal recovery plans.
Decision Intelligence can almost serve as a disaster recovery plan. It provides a means to collect and evaluate data from a variety of sources and make recommendations to compensate for unforeseen events. What’s more, it does so more quickly and efficiently than humans alone could do.