Effectively managing supply chains has perhaps never been more important for organizations. The coronavirus pandemic has created significant market distruptions, shifting the way consumers and businesses purchase products and creating challenges for manufacturers to receive the materials they need to meet demand.
Some organizations are finding that data analytics and related technologies such as artificial intelligence (AI) and machine learning hold the key to supply chain management excellence. Whether it’s a matter of ensuring supply chain integrity or navigating rapid growth and complexity, here is how several organizations are putting analytics to work to beneficial results.
NASA: Maintaining supply chain integrity
In 2013, NASA was directed by the U.S. Congress to improve its supply chain risk management processes, specifically as it relates to technology purchases of more than $800 million across IT and operational technology.
Analytics has played a big role in helping the space agency comply with this initiative. Key has been the aeronautics and space research agency’s adoption of an AI-powered supply chain risk management tool from Interos to provide analytics and insight, says Kanitra Tyler, head of NASA’s Supply Chain Risk Management (SCRM) Service. “Our success with this tool ultimately led to our program becoming a shared service across NASA,” she says.
SCRM’s centrally vetted product list allows all centers and mission directorates within NASA to share information collected by the platform, while still using their own risk management processes to make buy or no buy decisions based on their individual risk profiles and tolerances.
“Maintaining supply chain integrity is critical to NASA,” Tyler says. “We have restrictions on products and components from sensitive countries, and zero-tolerance for counterfeit or compromised products. Maintaining that visibility into our supply chain hasn’t always been easy.”
With the pandemic presenting major challenges to supply chains across the world, the need to address disruptions quickly and efficiently has become more critical than ever, Tyler says.
“COVID-19 has also increased the potential for risks to arise, especially at the sub-tier supplier level,” Tyler says. “We have been able to leverage analytics to effectively navigate these risks through an ability to provide multi-tier, multi-factor visibility for real-time assessments of NASA’s entire supply chain through the pandemic.”
Traditionally, maintaining supply chain integrity at NASA has been done manually with individual analysts investigating suppliers one at a time. “To keep pace with the increasing rate of technological change, we knew we needed something that could provide greater visibility, free up manual resources, and enable us to deliver information and analytics on our products and professional services to our various programs on a continuous basis,” Tyler says.
The goal of the risk management service is to identify and mitigate risk up front, enabling various NASA programs to focus on their goals and not have to worry about the security and integrity of IT components.
Using analytics provided by the Interos platform, NASA has been able to automate processes and greatly increase the visibility of its supply chain.
“The analytics function that machine learning gives us has saved us almost countless hours here, enabling us to do much of the work automatically and continuously, freeing up human resources to conduct targeted deep-dive investigations where needed,” Tyler says.
Since adopting the analytics platform, SCRM has identified more than 4,500 suppliers it wasn’t previously aware of. “We also discovered some 400 of those suppliers who were located in sensitive countries, many of whom also had financial solvency issues, and identified a key sub-tier supplier that was a front for [a] prohibited Russian company,” Tyler says.
SCRM can now compare suppliers based on equitable criteria. “Previously, we were largely reliant on the evaluations of outside institutions like the intelligence agencies to help us determine risk,” Tyler says. “Using machine learning and analytics has given us an objective and consistent standard for assessing them and benchmarks them against NASA’s specific risk tolerances.”
Intermountain Healthcare: Improved fulfillment metrics
Intermountain Healthcare, which operates 24 hospitals and 215 clinics in Utah, Idaho, and Nevada, has a massive supply chain and logistics system.
Most of what Intermountain’s Supply Chain Organization (SCO) does can be enhanced by data analytics, says James Selfridge, data analytics manager.
“Real-time, electronic dashboards are essential for us to manage our daily work,” Selfridge says. “Not only is this important for the SCO; it is also important to our customers and our suppliers. Data makes sure we are enhancing the good and fixing the bad. All of the work that our data analytics department is doing for the SCO helps us make better decisions and lower costs.”
The SCO needs to have access to all analytics platforms in use at the company for a complete picture of what’s happening in the supply chain, Selfridge says. Data analysts use tools from several vendors to create reports based on incoming data.
Data analytics has provided key insights into how the CSO is performing in a number of processes.
One is “perfect order fulfillment,” a supply chain measurement of the percentage of orders delivered to the correct location, with the right products, at the right time, in the desired condition, in the proper package, in the correct quantity, with the right documentation, to the right customer, with the correct invoice.
Others are the reprocessing of disposable or single-use medical devices; materials management, which includes anything to do with current supplies and logistics within the system; a pandemic dashboard, which is the company’s daily summary of its COVID-19 data, including supplies dedicated to virus tracking.
“By having these key insights, the SCO can see current state and start to perform countermeasures to improve in areas to reduce cost, improve efficiencies and safety, or increase quality,” Selfridge says. Materials managers can use analytics to track the performance of their staffs on a daily basis and adjust the level of workloads and/or labor accordingly, he says.
Analytics data has been a key performance metric in making decisions during the pandemic, based on product availability. “This is increasingly important to all leaders at Intermountain Healthcare, as we navigate” during the health crisis, Selfridge says. Overall, “analytics is a significant contributor to our SCO success,” he says.
Sanmina: Rapid response to demand changes
Over the past several years, electronics manufacturing services provider Sanmina has invested in data analytics and integrated supply chain technologies to deliver end-to-end visibility and planning capabilities.
“Traditional models for supply chain planning that use standard MRP [material requirements planning] systems have limited value in meeting today’s supply chain needs,” says Manesh Patel, CIO and senior vice president at Sanmina. “Our customers expect response times to demand changes that are within minutes and hours, not days or weeks.”
The timely transmission of those changes across Sanmina’s 60-plus manufacturing sites in 22 countries and to thousands of downstream suppliers is essential for meeting delivery schedules. In addition, the ability to provide near real-time communications across the supply network ensures that Sanmina’s plants and its suppliers are building the right products at the right time, reducing excess inventory and unnecessary costs.
Sanmina has consolidated the data silos that historically supported various functional groups that manage different segments of the supply chain, Patel says. The combination of the latest data warehouse technologies, supply chain planning systems, and consolidated data are the foundation of its supply chain platform.
The company uses a supply chain planning platform called RapidResponse from Kinaxis to provide insight into the impact of demand changes within minutes, leveraging its in-memory engine that processes large datasets in the range of hundreds of gigabytes.
“The ability for our management and employees to improve the speed and quality of decisions regarding supply, demand, and manufacturing activity is the most significant benefit,” Patel says. “In the past, the time and effort expended to access and process information to provide analysis was significant.”
Working from a common dataset has reduced the need for reconciliation and data verification, Patel says, and enables the company to move from just reporting information to using analysis to predict future performance. “Knowing where key metrics are headed, not only at a summary level but also at a detail level, empowers our teams to make decisions that improve our business,” he says.
Analytics has helped Sanmina deal with pandemic-related shifts. “At the beginning of the pandemic, different parts of the world were shutting down at different times,” Patel says. For example, as the pandemic impacted Malaysia, Sanmina was able to quickly identify which suppliers were shut down and analyze how this would impact its various plants, customers, and products worldwide.
“We were able to move fast and make data-driven decisions that enabled us to quickly address the disruption with our customers,” Patel says. “Having an end-to-end capability to perform a deep level of analysis within hours instead of days is a key differentiator that few companies have.”
The combination of analytics, good data management practices, and the move to a data-driven culture have started to revolutionize the supply chain at Sanmina, as well as other aspects of its business, Patel says.
“Having access to high-quality information that is delivered on demand is changing our behaviors in ways that are driving performance improvements and leading to greater adoption,” Patel says. “Over the years, our supply chain has steadily grown more challenging due to multiple factors, including product complexity, extended supply chains, and demand fluctuations. We are now able to address those challenges at the scope, scale, and speed required.”
Extreme Networks: Supporting growth and complexity
Sometimes the adoption of analytics for supply chain management is primarily driven by a rapid increase in business complexity and growth. That’s the case with Extreme Networks, a provider of networking hardware and software
Beginning in 2016 the company completed four acquisitions in less than three years, including the wireless local area networking business from Zebra Technologies, campus networking business from Avaya, data center business from Brocade, and all of Aerohive Networks.
“As with any acquisition, merging supply chains was an incredibly complex operational and technical challenge, when you consider the different systems, suppliers, and partners involved in manufacturing, fulfillment, and deployment,” says Norman Rice, COO at Extreme.
Among the challenges: There was no single “source of the truth,” many manual workarounds, and a reliance on Microsoft Excel spreadsheets and phone calls for planning and execution. “We took the opportunity to overhaul our legacy process, redesign our structure, and leverage new supply chain technology — including analytics capabilities — to increase operational efficiency and align our global logistics operation,” Rice says.
In addition to creating a new Supply Chain Center of Excellence with dedicated supply chain and IT experts, Extreme deployed an inventory management system from Kinaxis to complement its in-house systems and other platforms.
The cloud-based platform enabled real-time concurrent planning across demand, supply, inventory, customer service, and order fulfillment. It also allowed for user-configurable parameters, rules, and analytics to optimize global supply chain operations. “This was all scoped, developed, and implemented over an eight-month period while integrating two new acquisitions,” Rice says.
One of the primary benefits of the analytics capabilities is that Extreme was able to better understand supply and inventory fluctuations by product SKU and region. With increased visibility, the company saw a 92 percent reduction in product constraints. “Also, our optimized inventory management allowed us to consolidate our vendor managed inventory into fewer warehouses, leading to a 51 percent reduction in quarterly distribution center run rate costs,” Rice says.
Even before the pandemic, the combination of automated, centralized inventory management and real-time supply chain analytics was critical during the tumultuous trade wars on tariffs in 2019, Rice says. “Had we not established a centralized view into our inventory, we would have been hard pressed to survive the tariffs, let alone the pandemic,” he says. “With clear visibility of our inventory and global logistics operations, Extreme weathered the storm. Now, as we navigate the evolving COVID-19 pandemic, analytics has made our supply chain more agile and more resilient in the wake of massive disruptions.”