How AI is improving warehouse performance and mitigating supply chain disruptions

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Improving warehouse performance with artificial intelligence (AI) and machine learning (ML) helps make supply chains more resilient and able to bounce back from disruptions faster. Unfortunately, the severity and frequency of supply chain disruptions are increasing, with McKinsey finding that, on average, companies experience a disruption lasting one to two months every 3.7 years.

Over a decade, the financial fallout from supply chain disruptions in the consumer goods industry can amount to 30% of annual earnings before interest, taxes, depreciation, and amortization (EBITDA). However, Fortune 500 companies with resilient supply chains earned a 7% premium to their stock price and market capitalization.

Resilient supply chains are the shock absorbers that allow e-commerce, retail, grocery, post and parcel businesses to operate despite the accelerating pace of disruption. Strengthening supply chains to make them more resilient pays.

Closing Warehouse Gaps Strengthens Supply Chains

Unexpected delays and undiscovered warehouse errors cost the most to fix and wreak havoc on supply chains. Warehouse managers, planners and shippers rely on decades-old processes based on Microsoft Excel spreadsheets. But, with the cost, pace and severity of disruptions increasing, warehouses cannot respond quickly enough with these manual systems. As a result, "Operations managers spend hours collecting data and manually entering it into Excel spreadsheets, which consumes valuable time in managing and optimizing warehouse operations," Akash said. Jain, managing director of connected enterprise Honeywell for the connected warehouse, at VentureBeat.

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Warehouse accuracy and performance slows further, as decisions made in the warehouse that impact margins, costs, and revenue trade-offs often don't make it to the top floor. Senior managers need to know how split-second decisions about which orders to ship impact inventory carrying costs and total inventory value. Rampant inflation makes inventory valuation one of the costliest risks to manage today.

Supply chain stress testing often reveals the largest and most costly gaps in warehouse performance down to the asset level. Asset performance management (APM) should be at the heart of warehouse management, so that costs, risks and machines used can be optimized with real-time data.

For warehouses to absorb disruptions and keep running, the managers who run them need a continuous stream of near real-time data from supervised ML algorithms to optimize the many constraints of their operations. “Many distribution companies were taken completely by surprise when e-commerce demand took off at the start of the pandemic. Many were working multiple shifts to keep up with demand, with little or no time to maintain machinery and warehouses so they wouldn't break down,” Jain told VentureBeat.