Analytics in the supply chain

Business intelligence includes both data access and reporting, and analytics. Each of these approaches addresses a range of questions about an organisation's business activities. The questions that analytics can answer represent the higher-value and more proactive end of this spectrum.

Competing on analytics

At a time when companies in many industries offer similar products and use comparable technology, high-performance business processes are among the last remaining points of differentiation. Many of the previous bases for competition are no longer available. Unique geographical advantage doesn't matter in global competition, and protective regulation is largely gone. Proprietary technologies are rapidly copied and breakthrough innovation in products or services seems increasingly difficult to achieve. What's left as a basis for competition is to execute your business with maximum efficiency and effectiveness, and to make the smartest business decisions possible. And analytical competitors wring every last drop of value from business processes and key decisions.

Analytics can support almost any business process. Yet organisations that want to be competitive must have some attribute at which they are better than anyone else in their industry - a distinctive capability. This usually involves some sort of business process or some type of decision. Maybe you strive to make money by being better at identifying profitable and loyal customers than your competition, and charging them the optimal price for your product or service. If so, analytics are probably the answer to being the best at it. Perhaps you sell commodity products and need to have the lowest possible level of inventory while not preventing your customer from being able to find your product on the shelf; if so, analytics are often the key to supply chain optimisation.

Analytical competitors, then, are organisations that have selected one or a few distinctive capabilities on which to base their strategies, and then have applied extensive data, statistical and quantitative analysis, and fact-based decision making to support the selected capabilities. Analytics themselves don't constitute a strategy, but using them to optimise a distinctive business capability certainly does.

Some industries are clearly more amenable to analytics than others. If your business generates lots of transaction data - such as in financial services, travel and transportation, or gaming - competing on analytics is a natural strategy (though many firms still don't do it). But if your business model is based on hard-to-measure factors - like style, as in the fashion business, or human relationships, as in the executive search industry - it would take much more groundbreaking work to compete on analytics.

Of course, any quantitative analysis relies upon a series of assumptions. When the conditions behind the assumptions no longer apply, the analyses should no longer be employed. For example, Capital One and other credit card companies make analytical predictions about customers' willingness to repay their balances under conditions of general economic prosperity. If the economy took a sharp downturn, the predictions would no longer apply, and it would be dangerous to continue using them.

The key message is that the frontier of decisions that can be treated analytically is always moving forward. Areas of decision making that were once well suited for intuition accumulate data and analytical rigour over time, and intuition becomes sub-optimal.

Competing on analytics with external processes: customer and supplier applications

Analytics took a great leap forward when companies began using them to improve their external processes - those related to managing and responding to customer demand and supplier relationships. Unlike internal processes that lie completely within the organisation's direct control, externally focused processes require cooperation from outsiders, as well as their resources.

For years, operations management specialists have created algorithms to help companies keep minimal levels of inventory on hand while preventing stock-outs - among other supply chain challenges. And manufacturing firms have long relied on sophisticated mathematical models to forecast demand, manage inventory, and optimise manufacturing processes.

They also pursued quality-focused initiatives such as Six Sigma and kaizen (the Japanese strategy for continuous improvement), tools for which data analysis is an integral part of the methodology.

Analytical competitors, however, take the use of analytics much further than most companies. In many cases, they are pushing not only data but also the results of analyses to their customers. Our information suggests that they are also integrating their systems more thoroughly and sharing data with their suppliers. As companies integrate data on products, customers, and prices, they find new opportunities that arise by aligning and integrating the activities of supply and demand. Instead of conducting post hoc analyses that allow them to correct future actions, they generate and analyse process data in near-real time and adjust their processes dynamically.

Supplier-facing processes

Contemporary supply chain processes blur the line between customer- and supplier-oriented processes. In some cases, customers penetrate deep into and across an organisation, reaching all the way to suppliers. In other cases, companies are managing logistics for their customers.

• View Typical analytical applications in supply chains

Connecting customers and suppliers

Some examples of how analytics can be used to connect customers and suppliers are shown below.

Wal-Mart

Wal-Mart is the mother of all supply chain analytics competitors. The company collects massive amounts of sales and inventory data (583 terabytes - almost 0.6 million gigabytes - as of April 2006) into a single integrated technology platform. Its managers routinely analyse manifold aspects of its supply chain, and store managers use analytical tools to optimise product assortment, examining not only detailed sales data but also qualitative factors such as the opportunity to tailor assortments to local community needs.

The most distinctive element of Wal-Mart's supply chain data is its availability to suppliers. Wal-Mart buys products from more than 17,400 suppliers in 80 countries, and each one uses the company's retail link system to track the movement of its products - in fact, the system's use is mandatory. In aggregate, suppliers run 21 million queries on the data warehouse every year, covering such data as daily sales, shipments, purchase orders, invoices, claims, returns, forecasts, radio frequency ID deployments, and more.

Suppliers also have access to the modular category assortment planning system, which they can use to create store-specific modular layouts of products. The layouts are based on sales data, store traits, and data on 10 consumer segments. Some suppliers have created more than 1,000 modular layouts.

As Wal-Mart's data warehouse introduced additional information about customer behaviour, applications using its massive database began to extend well beyond the supply chain. Wal-Mart now collects more data about more consumers than anyone else in the private sector. Wal-Mart marketers mine this data to ensure that customers have the products they want, when they want them, and at the right price.

For example, they've learned that before a hurricane, consumers stock up on food items that don't require cooking or refrigeration, the top seller being strawberry Pop-Tarts. We fully expect that Wal-Mart asks Kellogg to rush shipments of them to stores just before a hurricane hits. In short, there are many analytical applications behind Wal-Mart's success as the world's largest retailer.

Amazon

Wal-Mart may be the world's largest retailer, but it knows where all its stores are located. Amazon.com's business model, in contrast, requires the company to manage a constant flow of new products, suppliers, customers, and promotions, as well as deliver orders directly to its customers by promised dates. With one of the most complex supply chain problems in business, Amazon.com recruited Gang Yu, a professor of management science and a software entrepreneur who is one of the world's leading authorities on optimisation analytics, as the head of its global supply chain.

Yu and his team began by integrating all the elements of Amazon's supply chain in order to coordinate supplier sourcing decisions. To achieve the optimal sourcing strategy (determining the right mix of joint replenishment, coordinated replenishment, and single sourcing) as well as manage all the logistics to get a product from manufacturer to customer, Amazon.com applies advanced optimisation and supply chain management methodologies and techniques across its fulfilment, capacity expansion, inventory management, procurement, and logistics functions.

For example, after experimenting with a variety of packaged software solutions and techniques, Yu and his team concluded that no existing approach to modelling and managing supply chains would fit their needs. They ultimately invented a proprietary inventory model employing non-stationary stochastic (ie random) optimisation techniques, which allows them to model and optimise the many variables associated with their highly dynamic, fast-growing business.

Amazon.com sells over 30 categories of goods, from books to groceries to industrial and scientific tools. The company has a variety of fulfilment centres for different goods. When Amazon.com launches a new goods category, it uses analytics to plan the supply chain for the goods and leverage the company's existing systems and processes. To do so, it forecasts demand and capacity at the national level and fulfilment centre level for each stock-keeping unit (SKU).

Its supply chain analysts try to optimise order quantities to satisfy constraints and minimise holding, shipping, and stock-out costs. In order to optimise its consumer goods supply chain, for example, it uses an 'integral minimum cost flow problem with side constraints'; to round off fractional shipments, it uses a 'multiple knapsack problem using the greedy algorithm'.

Logistics management

Sometimes a service company uses analytics with such skill and execution that entire lines of business can be created:

UPS

United Parcel Service (UPS) took this route in 1986, when it formed UPS Logistics, a wholly-owned subsidiary of UPS Supply Chain Solutions. UPS Logistics provides routing, scheduling, and dispatching systems for businesses with private fleets and wholesale distribution. The company claims to have over 1000 clients that use its services daily. This approach, captured in the 'Don't you worry about a thing' campaign, is enabling UPS to expand its reputation from reliable shipping to reliable handling of clients' logistics value chains.

Of course, UPS has been an analytical competitor in supply chains for many years. In 1954 its then chief executive officer (CEO) noted, "Without operations research we'd be analysing our problems intuitively only". The company has long been known in its industry for truck route optimisation and, more recently, aeroplane route optimisation. The incumbent CEO, Mike Eskew, founded UPS's current operations research group in 1987. By 2003 he announced that he expected savings from optimisation of $600 million annually. Describing the importance of route optimisation, he said: "It's vital that we manage our networks around the world the best way that we can. When things don't go exactly the way we expected because volumes change or weather gets in the way, we have to think of the best ways to recover and still keep our service levels."

FedEx

FedEx has also embraced both analytics and the move to providing full logistics outsourcing services to companies. While UPS and FedEx both provide customers with a full range of IT-based analytical tools, FedEx provides these applications to firms that do not engage its full logistics services, leading one analyst to observe, "FedEx is as much a technology company as a shipping company."

UPS and FedEx have become so efficient and effective in all aspects of the logistics of shipping that other companies have found it to their economic advantage to outsource their entire logistics operations.

CEMEX

Another company helping its customers manage logistics is CEMEX, the leading global supplier of cement. Cement is highly perishable; it begins to set as soon as a truck is loaded, and the producer has limited time to get it to its destination. In Mexico, traffic, weather, and an unpredictable labour market make it incredibly hard to plan deliveries accurately. So a contractor might have concrete ready for delivery when the site isn't ready, or work crews might be at a standstill because the concrete hasn't arrived.

CEMEX realised that it could increase market share and charge a premium to time-conscious contractors by reducing delivery time on orders. To figure out how to accomplish that goal, CEMEX staffers studied FedEx, pizza delivery companies, and ambulance squads. Following this research, Cemex equipped most of its concrete-mixing trucks in Mexico with global positioning satellite locators and used predictive analytics to improve its delivery processes. This approach allows dispatchers to cut the average response time for changed orders from three hours to 20 minutes. Not only did this system increase truck productivity by 35%, it also wedded customers firmly to the brand.

CEMEX's strategy was powerful because the company changed its focus from the sale of a commodity to the sale of something customers really cared about. In short, the unit of business shifted from cubic yards to the delivery window. This was a simple change in one sense. But CEMEX then oriented its information, logistics, and delivery infrastructure around the delivery window concept, creating far-reaching changes in the company and eventually throughout the industry.

Conclusion

Analytical competitors have recognised that the lines between supply and demand have blurred. As a result, they are using sophisticated analytics in their supply chain and customer-facing processes to create distinctive capabilities that help them serve their customers better and work with their suppliers more effectively.

The discipline of supply chain management has deep roots in analytical mastery; companies that have excelled in this area have a decades-long history of using quantitative analysis to optimise logistics. Companies getting a later start, however, have clear opportunities to embrace an analytical approach to customer relationship management and other demand processes.

This article is reprinted by permission of Harvard Business School Press. Excerpt from 'Competing on analytics: the new science of winning' by Thomas Davenport and Jeanne Harris. Copyright 2007 Harvard Business School Publishing Corporation. All rights reserved.

Authors

Jeanne Harris is executive research fellow and director of research for the Accenture Institute for High Performance Business.

Thomas Davenport is the president's distinguished professor of information technology and management at Babson College.

This article was published by the Finance and Management Faculty (Issue 143, April 2007).