The worldwide supply chain challenges that plagued companies in multiple industries throughout 2021 are continuing this year. One potentially effective solution for addressing supply and demand issues is to leverage data analytics.
Professional services and consulting firm KPMG in a recent report notes that several major disruptions are currently affecting supply chains. These include the ongoing global logistics disruptions stemming from the COVID-19 pandemic that continue to impact businesses and consumers — as the flow of goods into key markets is restricted by shutdowns of major global ports and airports.
The major logistics disruptions create a ripple effect across global supply chains that ultimately cause goods to pile up in storage, the firm says. Assuming that these disruptions decrease and access to sea and airfreight reverts back to pre-pandemic levels, it will likely take some time before things return to normal, it says.
Other factors contributing to supply chain problems include production delays, over reliance on a limited number of third parties, and labor market shortages. The report also points out that many companies are investing in technologies to automate key nodes within the supply chain.
This year will see an accelerated level of investment, KPMG says, as businesses look to enhance critical supply chain planning capabilities by adopting more advanced “digital enablers” such as cognitive planning and AI-driven predictive analytics.
“The onset of new technology has fundamentally changed the way supply chains operate globally,” the report says. “The consumers are becoming more demanding, and this is leading the supply chains to change and evolve at a faster rate. Modern operations are focused on technology and innovations, and as a result, supply chains are becoming more complex.”
How can organizations best use data analytics to enhance their supply chain management (SCM) efforts? Here are some best practices, according to experts.
Turn data into actionable, simple insights
Most companies are awash in large volumes of data, often stored in diverse systems and databases, says John Abel, CIO at networking technology company Extreme Networks. Supply chains have the added complexity of additional data sources being generated from extended partners such as outsourcing, logistics, and distribution operations, he adds.
“As a result, many struggle to use this data to generate meaningful insights beyond top-level metrics and descriptive statistics,” Abel says. “Data analytics tools can deliver deeper, actionable insights as well as improve accuracy of those insights.”
The foundations for a successful supply chain data analytics strategy include ensuring that internal and external data are brought together in a structured format; focusing the outcome of data projects on what actions need to be taken to move the performance needle; and making sure the results are simple to understand, Abel says.
“The last point is one of the most important,” Abel says. “It is often tempting to focus on the model used rather than the output,” as many technology leaders look to include AI in their processes. “But the more important goal is to focus on generating insights that are clear, explainable, and easy to digest by the business users, not just analytics teams.”
Any report or dashboard being shared with cross-functional teams must be able to tell a clear story that is easily understood. “Otherwise, the benefits of data analytics could be overshadowed by the need for lengthy meetings to explain why they are valuable,” Abel says.
This also works the other way around. “While most data analytics experts don’t have a deep functional knowledge of the business processes and systems that produced that data, they often have a broad knowledge of the upstream and downstream processes and systems,” Abel says. “Successful supply chain analytics projects start from a ‘what does the data tell us’ perspective, but then layer in an in-depth understanding of business processes.”
Partnerships between analytics teams and the business users help develop these explainable insights that can be easily communicated across an organization, Abel says.
Focus analytics on difference-making areas
Supply chain organizations are being inundated with data such as customer orders, item information, equipment utilization, and ever-evolving transportation costs, says Erik Singleton, expert practitioner for global supply chain at consultancy North Highland Worldwide Consulting.
“The key to building a successful, customer-centric supply chain while maximizing operational efficiency is using the right analytics to make data-driven decisions,” Singleton says. He recommends that supply chain organizations focus their analytics on three main areas.
One is demand planning and inventory placement. “Organizations collect millions of rows of transactional data, enabling vigorous analytics on customer buying patterns,” Singleton says. “Leveraging this data to build a robust, analytical algorithm to drive inventory placement throughout the supply chain ensures products are in the right place at the right time.” Businesses should focus analytical resources on forecasting demand patterns between product type, sales channel, and geographical placement.
The second area is efficiency of operations. Customer and order data enables supply chains to maximize asset and workforce utilization by efficiently scheduling resources to accommodate fluctuating demand patterns, Singleton says. “Adjusting labor schedules to ramp up resources during peaks, while scheduling equipment/asset maintenance during valleys, enables businesses to maximize efficiency and reduce operational costs,” he says.
And the third area is order fulfillment path decision-making. “Customers expect supply chains to be more flexible and customer-centric than ever before, with multiple avenues for products to reach the end customer,” Singleton says. Organizations need to balance a multitude of factors, including service expectations, transportation and fulfilment costs, and inventory levels, to determine the best method for order fulfillment.
“Leveraging analytics to weigh costs versus customer experience is critical to maintain competitiveness,” Singleton says.
Leverage real-time data to deal with disruptions
As both the size and complexity of supply chains grow globally, it is becoming exponentially more difficult to manage and respond to fluctuations across the supply chain, Abel says.
“With data points changing rapidly, analysis and decision-making is often based on outdated information and further exacerbated by the time needed to effectively analyze the data,” Abel says. “To navigate this successfully, supply chain managers need to develop concurrent planning systems that optimize demand and supply by utilizing advanced analytics and real-time visibility across the supply chain.”
Historically, updates were based on a specific time frame and shared perhaps daily or hourly, Abel says. “But today that is not enough,” he says. “Demand and supply fluctuate constantly, so it is best to have system integrations with key suppliers in order to get updates in real time.”
If something changes at a supplier, organizations need to immediately understand the potential impact so they can make alternative plans to maintain commitments to customers. “The use of advanced analytics on real-time data feeds allows those managing the supply chain to quickly model and assess the impacts of potential disruptions, so they can plan and execute on the fluctuations in demand, supply, and inventory,” Abel says.
These insights can also be used to understand the potential impacts of supply chain constraints on revenue forecasts, Abel says. Near real-time visibility of data such as bookings, shipments, inventory levels, supplier commits, discounting, and pipeline sales opportunities — as well as the real-time analysis of that data — has become critical to an organization’s ability to monitor and manage revenue forecasts.
By using advanced analytics and automation, “these variable data inputs can be used to create tracking models that allow the supply chain teams to react to changes in near real-time, develop contingencies, and deliver a more predictable revenue forecast,” Abel says.
Emphasize data governance and quality
The old adage about information, “garbage in, garbage out,” certainly applies to supply chain data, says Mark Korba, vice president of supply chain and business intelligence at Optimas Solutions, a fastener manufacturer and distributor.
“It is important to validate data, especially since it is coming from a variety of sources,” including customer inventory management systems, demand planning applications, supplier software, and others, Korba says. “Often the data isn’t consistent or managed the same across systems, and therefore lacks integrity.”
Creating an active data governance program is especially important to ensure data integrity throughout the supply chain, Korba says. “A governance program ensures the data aligns properly and strengthens collaboration between supply chain partners,” he says. “There is a lot of public information about setting up data governance programs available.”
Benchmarking a company’s supply chain against known data is particularly important, Korba says. “At Optimas Solutions, our supply chain teams compare their performance to competitors,” he says. “They review industry averages and glean information that will help improve the company’s ability to meet demand.”
Make supply chain analytics broadly available
SCM involves multiple facets of the organization, so analytics capabilities need to be shared liberally.
“Make it easy for everyone involved in the supply chain to get the data and tools that they need,” says Arthur Hu, senior vice president and CIO at computer hardware provider Lenovo. “This first requires breaking down any ‘information silos’ and establishing an integrated end-to-end information system.”
It also means leveraging tools such as machine learning and artificial intelligence to realize the full value of such a data-rich system, Hu says. “When this type of system is in place, managers and operators up and down the supply chain can optimize its performance.”
It’s also important to remember that supply chain analytics use cases don’t know departmental boundaries. “Teams tend to focus on the data that is readily available within their organization,” Hu says. “In doing so, they can miss out on the full data required to truly gain insight into an issue. As a critical platform that touches multiple parts of the business, the supply chain needs to be managed from a holistic perspective.”
For example, in managing product quality, a team should have access not only to the factory “as-produced” configuration and metrics, but also to product development data, component supplier data, and customer feedback data, Hu says. All of this taken together creates a multidimensional picture of what drives quality outcomes, he says.
By ensuring that business leaders at all levels of the organization have access to supply chain data and the ability to interact with it, “companies can set themselves up for success and yield long-term returns that improve their bottom line,” says Stanislav Tatarzuk, vice president of Inventory planning and forecast at ecommerce company CarParts.com.
Data insights “can offer different levels of value to different teams and departments,” Tatarzuk says. “For example, a logistics team may use data to discover bottlenecks and increase efficiencies within their warehouse or distribution center, while a finance department may look at the same data and identify ways to streamline costs and cut back on spending.”
This level of knowledge sharing across an organization not only reduces overall risk, but also enables improved decision-making and performance, Tatarzuk says.
