A few weeks ago, I blogged about how data from real-time supply chain visibility helps companies improve supply-side collaborative forecasting. In this post, I’ll take as look at the demand side.
Retailers, manufacturers and suppliers have always had to manage wild, unexpected swings in demand because forecasting is an estimate based on the known or planned factors. But long-term disruptions like COVID expose problems at a much broader level as leadership identifies gaps in knowledge. All of this has accelerated the demand for supply-chain visibility. So, how can you plan for the unplanned? Be agile.
Supply chain management systems have addressed demand planning for years and continue to advance in sophistication. And they’re great doing what they do: If demand for a product is going to increase five percent, the platforms will adjust accordingly. The foundation for many of these systems is the assumption that material is moving according to plan, and so they don’t tell you where bottlenecks are. By contrast, let’s consider: What if the port of loading is backlogged and there is a five-day delay? What if the carrier breaks down, causing a seven-hour delay, but you need the product right away? It’s only when you add visibility into the mix that you can really get a handle on demand shocks, and where and how to best address any given scenario.
Best-in-class supply chain visibility requires greater transparency than many companies have been accustomed to. And while many have grown comfortable with transparency in pockets, those who deploy visibility at scale across inbound, inter-company and finished goods have an undeniable edge. By sharing data about the freight that all parties want to arrive on time, suppliers, shippers, carriers and receivers gain the power to do more work with less labor – both at the loading dock and the back office. The question is no longer just, “Where’s my truck?” It’s, “I know where my truck is and when it’s supposed to arrive, so now what?”
Here’s an example from one of our grocery retail customers. The company operates a network of more than 100 large grocery stores across the US, and they work with more than 100 carriers to get food to the stores on time. COVID has made their workers’ lives harder; not only were customers buying things in a panic, but staffing had fallen, too, which made it even harder to keep up. “Our stores were getting completely wiped out, and we were having to react almost every single day,” one manager told us. They faced shortages of all the things you’ve read about: pasta, toilet paper, hand sanitizer. Fortunately, they had recently deployed a visibility system so they, their suppliers, receiving distribution centers and their carriers could all see where their shipments were at any given time.
The crush of consumer buying would normally have swamped their already thin staffing. This time was different. Because they’d urged their business partners to adopt visibility, the shippers, carriers and the grocery chain all rallied to keep up. Now they knew where almost every load was, when it would arrive and why delays were happening. Suddenly, all that understaffing and remote work became much more manageable. The transparency enabled by visibility prevented logistical chaos.
Visibility improves agility, but can visibility reduce operating costs?
The increased transparency for pre-paid inbound orders helped the retailer monitor how often they ordered from different vendors. Day-of-week data analysis revealed buying patterns they had not seen before. The company ultimately saved money by ordering more by the pallet instead of smaller quantities. Buyers learned that frequent ordering would no longer be required to buffer safety stock because visibility gave them confidence that loads would arrive on time and with the correct SKUs. Better still, logistics could detect incorrect lead times that had previously led them to think shipments were late before they’d had a chance to leave the shipping dock.
With this actionable intelligence at their fingertips, the retailer’s OTIF stats improved accordingly, and overall performance soared. “We thought we’d find a lot of issues and opportunities with our vendor partners. We actually found opportunities within ourselves,” that manager said. “There were actually multiple instances where we were creating late trucks. That was a little bit of a gut check. We had to swallow our pride on that one.” In my last post, I discussed how collaborative forecasting hits its stride once everyone in the supply chain is onboard with visibility. This grocery retailer’s network achieved just that in about two years.
The end result? Reduced inventory and overtime from dealing with late trucks and overstaffing, stronger relationships, improved timeliness, and better problem-solving all around. “We’re all looking at the same data,” the manager said. “It’s like I tell our vendors: If you’re on the fence about sharing this much data, come and talk to me. It all works.”
That’s a great outcome that demonstrates the power of real-time supply chain data to help manage fluctuations in demand.
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