Thor Olavsrud
Senior Writer

Supply chain analytics: 3 success stories

Feb 22, 2022
AnalyticsSupply ChainSupply Chain Management Software

With supply chain disruptions ongoing, organizations are leaning on analytics to glean insights into their supply chains and operations, as these three supply chain analytics examples attest.

supply chain management data - ERP - Enterprise Resource Planning
Credit: Thinkstock

The past year has seen a multitude of industries around the globe suffering supply chain disruptions, with little sign of that letting up in 2022. To navigate these supply chain issues, organizations are increasingly turning to analytics to gain better insight into their supply chains and operations.

Supply chain analytics draw data from procurement, inventory management, order management, warehouse management and fulfillment, transportation management, and other operations applications to provide the organization with insight into every step of its supply chain. These insights can be used to quick adjustments in the moment but can also be used to support long-term strategic decision-making.

Here are three examples of how organizations are using supply chain analytics effectively today.

Predictive analytics gives UPS insight into its logistics network

On average, UPS delivers roughly 21 million packages on any given day. That number gets far bigger in December. In the past, the shipping multinational has relied on historical data and know-how from expert planners to track package status. Today, it uses the Harmonized Enterprise Analytics Tool (HEAT), a business intelligence platform, to capture and analyze customer data, operational data, and planning data to track the real-time status of every package as it moves across the company’s shipping network.

“HEAT helps us make better decisions in the way that we move packages across our network, the way that we plan our network, and the way that we provide information to our customers,” says Juan Perez, chief information and engineering officer of UPS. “It analyzes millions and millions of data points every single day to ensure that we are constantly providing the most-up-to-date information as to the status of a package, which then feeds all kinds of other systems that allow us to do better planning and better management of the network, better support in the way that we process packages across the organization.”

The HEAT platform analyzes more than 5.3 petabytes of data per week. It leverages predictive analytics, machine learning, and multi-model forecasting with proprietary randomness and seasonality growth factors to support forecasting, operations visibility, optimization, and reporting.

Perez’s advice: Think of your data strategy as a journey, rather than a destination.

“As big as we are and as good as we are with using data, one thing that I know is that the journey towards having a solid data strategy doesn’t really have an immediate end here for us,” Perez says. “We have to be constantly and constructively dissatisfied with the state of our technology and the state of our data, so we can be constantly making improvements to support the business.”

PepsiCo turns to predictive analytics to predict out-of-stocks

Food and beverage company PepsiCo is using analytics and machine learning to predict out-of-stocks and alert retailers to reorder.

“Certain products were flying off the shelves for various reasons early in the pandemic,” says Jason Fertel, ecommerce engineering head at PepsiCo. “Folks wanted to get as much oatmeal as possible, for example.”

Fortunately for PepsiCo, Fertel and his engineering organization within PepsiCo eCommerce had already been working on providing workflow automation for managing search marketing operations in the form of the company’s Sales Intelligence Platform. The platform combines retailer data with PepsiCo’s supply chain data to predict when items will go out of stock and prompt users to make purchases to replenish them.

Fertel’s advice: Find early adopters who are excited about your project and adopt a laser focus on a particular business problem.

“We want to do a lot of things, but we very much focused on out-of-stocks,” Fertel says. “There’s a whole slew of different verticals and sales intelligence that we go into, but we’ve been highly focused initially on out-of-stocks and I think that’s helped us find success.”

Pfizer’s digital transformation helps it manage its supply chain

Pharmaceutical titan Pfizer says its Global Supply – Digital Operations Center (DOC) project, has been critical to the company’s ability to manufacture and supply the Pfizer-BioNTech Covid-19 vaccine around the world.

The project is a “cockpit” for Pfizer operations, providing a shared view of end-to-end manufacturing and supply operational performance data for the company. Pfizer says the data has helped it identify opportunities to reduce up to 10% of cycle-time in some manufacturing areas and to maintain critical supply continuity for patients reliant on Pfizer pharmaceuticals.

“This solution has transformed how manufacturing colleagues collaborate and make decisions, providing tools to enable them to predict an issue before it happens and adjust in real time,” says Lidia Fonseca, executive vice president and chief digital and technology officer at Pfizer. “The DOC allows teams to mine data to provide analysis on variations compared to previously estimated standard lead-times, enabling further improvement opportunities.”

Fonseca’s advice: It’s all about culture. Pfizer’s move toward supply chain analytics has helped it transform into a leaner, more science-driven, more patient-focused organization. Success has required clearly communicating the company’s digital strategy to inspire employees’ support and participation.

“Our culture was instrumental in encouraging our employees to be courageous and think differently to accomplish what we previously would not have imagined possible.”