Supply Chain Data Visibility Paramount as Industry Lurches into Next Chapter
Key Takeaways from This Article:
COVID-19 has unveiled the fragility of a global supply chain predicated on lowest-cost principles.
Increasing supply chain data visibility is a priority for logistics organizations looking to improve resilience.
Supply chain recovery hinges on incorporating robust data analytics and other data-driven tools into business operations to increase efficiency, reduce costs and proactively manage risk.
The COVID-19 crisis has hit the global supply chain from all sides. Shortages for in-demand products abound, while shipment delays are common and production lines run at a fraction of capacity. Erratic consumer demand adds further dysfunction.
Supply chains will survive COVID-19, of course, but not without interim pain and structural change. Practitioners must develop a data analytics strategy that gives them insight into supply chain aberrations before catastrophe sets in.
“If we’re going to be able to prepare for these types of events in future, we have to identify appropriate sources of information that we should focus on all the time — not just when [crisis] manifests,” said Randy Bradley, associate professor of information systems and supply chain management at the University of Tennessee.
Shortages, consumer hoarding and delays all signal a tipping point. “We can’t operate the way we have,” said Milind Balaji, a senior supply chain manager at an East Coast manufacturer of toilet tissue, diapers, pulp and other products. “There is just too much risk.”
“We can’t operate the way we have. there is just too much risk.” Milind Balaji, senior supply chain manager, manufacturing
Supply Chain Faces Perfect Storm
Product supply shortages are also contributing to supply chain distress. Many product components are sourced from Asia, which experienced a major slowdown in production in early 2020.
Today, China represents some 16% of global GDP. Many components for electronics, retail and many other consumables are made in China, so the slowdown in production has hamstrung the flow of these goods. Compare China’s share today with 2002, during the SARS epidemic, when China represented only 4% of world GDP.
A second factor is unprecedented shifts in consumer spending. Demand has soared for groceries and toilet tissue but also for products such as Peloton bikes, board games and cleaning supplies. E-commerce increased 49% in April, according to Adobe’s Digital Economy Index, given shuttered brick-and-mortar stores. Meanwhile, restaurants, office parks and entertainment venues have been closed, skewing commercial demand.
Erratic supply and demand have strained logistics as well. Shipping from Asia is delayed by weeks given soft demand for apparel and other products, and some suppliers are turning to the airline industry — with air travel down by 95% — to reduce delays. Trucking delays are rife, and by some accounts, demand for short-haul deliveries has increased 30%.
All these factors have placed a vice grip on a supply chain that was running too lean to withstand the COVID-19 crisis. “We’ve seen how wide and how thin and how at-risk our global supply chains are,” said Guy Courtin, a former analyst at Constellation Research. “All of that works perfectly well when things are OK. But with a black-swan event, it just magnifies the risk.”
For the supply chain to find its footing, practitioners need supply chain data analytics to make it through the other side of the crisis.
“This whole disruption has thrown a lot of focus on supply chain technology — or the digital side of supply chain — which includes not just technology but analytics, processes and methodologies,” said Kumar Singh, a chief analytics adviser at Jagah.AI, a provider of analytics-based warehousing technology.
“We’ve seen how wide and how thin and how at-risk our global supply chains are,” — Guy Courtin, former analyst, Constellation Research
Data Visibility in the Supply Chain
Increasingly, companies recognize that a lean manufacturing approach works only when supply chain participants have full visibility.
Today, many manufacturers lack that awareness. They don’t know how much demand exists, how much supply they have or where their products are once they ship.
“Right now, our biggest problem is visibility,” Balaji said. “We don’t know where things are.”
He said that, today, just-in-time manufacturing has placed pressure on manufacturers to digitize and get their data house in order. To reduce costs and shipping delays, and to optimize inventory, manufacturers like his need to know precisely when, for example, to manufacture boxes that its retailer customer will use to ship the products it sells.
“The box has to go to the product, and the product goes in a box, the box goes to a retailer. No one wants to hold inventory. It’s all exacerbating the need for having systems that can talk to one another, and it also can give people a lot of real-time information.”
Balaji said that the company has worked on two fronts to gain supply chain data visibility. First, it is working on a data consolidation project so that departments speak the same language regarding product supply and whereabouts. Legacy systems couldn’t talk to one another..
Machine Learning for Greater Logistics Insight
Second, Balaji’s company has struggled to locate its products as they were shipped. The maritime industry — 90% of world trade travels by sea — is notoriously manual. Sometimes, the company uses IoT-connected containers on ships, but with COVID-19, many ships encounter several weeks of delay, and many shipments leave port without containers — and, thus, without tracking.
To combat that problem, Balaji said that the company uses Clearmetal, a data analytics logistics service that calculates average logistics times for air, ship, trucking and so on, to estimate delivery times for customers.
“Our customers want to know, ‘Where is my shipment?’ at every step of the way,” Balaji said. “Right now, we can’t really tell that well, so we use machine learning. Our data analytics company, Clearmetal, can take all these routes and analyze them to provide a high level of confidence about when a shipment will arrive.”
Balaji said that the tool also enables the company to plug in factors such as geopolitical environment, natural disasters and so on.
Experts say this is exactly the kind of supply chain data proficiency that companies need to develop. Many companies, for example, have considered drastic revisions to their sourcing strategies to reduce reliance on far-flung regions. But those new sourcing strategies must be informed by an accurate data platform that can conduct risk analysis.
“There are ways to do this analysis in the tool,” Singh said, “putting in numbers on tariffs, landing costs, logistical costs, local labor costs, local geopolitical risks to figure out what is the best network modeling tool to understand what the plans should be and the impact on costs. Using these tools help companies prevent this kind of disruption from happening in the future.”
Control Tower Technology to Enable Supply Chain Control
Another key supply chain data tool, say experts, is control tower technology, which monitors supply-and-demand factors with granularity via a single view in a dashboard. Companies can weigh risks based on multifactor analysis and act on data nearly in real time.
But control tower technology takes time to build and deploy and often involves a considerable learning curves.
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