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Supply chain woes continue to plague organisations around the world and in virtually all sectors. For some, leveraging data and analytics tools is proving to be an effective way to address the challenges.
Disruptions to global supply chains due to the COVID-19 pandemic have been significant. As consulting firm Deloitte notes, the free movement and operation of people, raw materials, finished goods, and factory operations have been stymied.
“Direct supply chains have experienced challenges, and so have extended supply chain partners such as third-party and fourth-party vendors — the suppliers of suppliers,” the firm says.
Enterprises face multiple risks throughout their supply chains, Deloitte says, including shortened product life cycles and rapidly changing consumer preferences; increasing volatility and availability of resources; heightened regulatory enforcement and noncompliance penalties; and shifting economic landscapes with significant supplier consolidation.
Technology can’t resolve every supply chain issue. Goods need to be produced and moved from point to point. But the latest analytics tools, powered by machine learning algorithms, can help companies predict demand more effectively, enabling them to adjust production and shipping operations.
Here’s how Lenovo is succeeding at using data analytics to improve supply chain operations.
Supporting an increasingly complex supply chain
Lenovo has been addressing the challenges of its global supply chain due to the pandemic by leveraging advanced forecasting technology and data analytics, says Arthur Hu, senior vice president and CIO.
Lenovo’s supply chain once focused primarily on logistics, information flow, and business flow, Hu says. But the company’s transformation into a full-service technology provider “has meant that our supply chain, once focused primarily on devices, has become increasingly complex, with more diversified customer demands, more complex products, and the need for more efficient and agile operations and service,” he says.
In the past year, the supply chain team has worked with 2,000 suppliers to deliver more than 130 million Lenovo devices.
Given the shift, the company’s supply chain team decided to revamp its operations, taking an “intelligent transformation” approach.
“A cross-functional team worked to transform Lenovo’s supply chain operations into a data-driven, intelligent ecosystem,” Hu says. “The new system provides real-time data, intelligent analysis and decision-making support that allow our businesses to deliver on their promises to customers more effectively and efficiently than ever before.”
The vendor built a Cost Forecasting Engine (CFE) system to provide faster and more accurate forecasting for procurement, manufacturing, and sales costs throughout its supply chain operations.
Using the system in combination with linear regression and XGBoost (eXtreme Gradient Boosting), an open-source software library that acts as a machine learning algorithm, Lenovo’s managers can establish the maximum and minimum threshold to avoid extremes that affect cost accuracy.
The technology can make cost comparisons to reduce the impact of month-to-month cost fluctuations for hardware components, and provide a basis for managers to make business strategy decisions, Hu says.
The CFE now supports procurement and production cost-forecasting for more than 70 per cent of Lenovo’s entire global supply chain, Hu says, and cost-of-sale forecasting for more than 90 per cent of the supply chain. Compared with manual cost maintenance, cycle cost-forecasting efficiency has improved by about 12 per cent. The cost-accuracy rate remains about 95 per cent, he says.
