Edith Simchi-Levi, Vice President of Operations at OPS Rules Partners and wife of the well-known MIT Professor David Simchi-Levi, recently discussed the topic of supply chain analytics with Dustin Mattison. In that interview, she noted good analytics “allows [companies] not only to understand what their business is doing, but also come up with new solutions and ideas for where they are going.” Using analytics correctly, she observed, provides companies with “a huge advantage.” [“The Role of Analytics in Supply Chain and Operations Strategy,” Dustin Mattison’s Blog, 9 June 2013] One often hears the term “big data analytics” and claims that they can lead to actionable insights; but, often the cover isn’t lifted to show what’s behind the term. When Mattison asked Simchi-Levi to define the term, she stated:
“I would say it’s various mathematical techniques that are used to solve problems, and these problems are, for instance, network design, which can use optimization techniques. If you’re doing warehouse design, you would use simulation. If you’re doing forecasting, it involves a lot of statistical methods. Here we see that there’s a deep need to match not only knowledge of mathematical techniques but also knowledge of the domain. This is why there are a lot of opportunities but also it’s not a very simple thing to do.”
Analyzing numbers has been part of the business landscape since cuneiform marks were first pressed into clay tablets; but, the nature of big data analytics is fundamentally different than analytics used in the past. Primarily this is has to do with the size and types of data available to analyze. Simchi-Levi told Mattison that only in recent years have circumstances emerged and techniques been developed that make “analytics in supply chain operations more important and practical.” She explains:
“The first one is that complexity has increased for companies due to globalization and the Internet. Companies have more product, more locations, more channels, and more markets as a part of the mix. This creates a lot of new challenges that require new tools. … The second thing is that there is more data available … from ERP systems and other sources. The third is that the computing speed has increased, as well as database technologies that speed up data access, so analytics are much more practical and deployable. The other thing is that many new methods and techniques that are around but really haven’t been deployed widely, and they can be a huge asset to improve performance. We know that analytics have played an important role in supply chain operations for a long time, applications in demand forecasting, transportation routing, inventory optimization, and network design. This has been around for a while. There are also areas of new opportunity that can benefit, such as supply chain segmentation, risk management, complexity reduction, manufacturing flexibility, all of which are very important to companies. What we see is that there’s a lot of interest from C-level managers in procurement, supply chain operations in using analytic tools to better understand their operations and provide some much-needed improvement.”
For several years now, supply chain analyst Lora Cecere has recommended that companies “aggressively use downstream data (e.g., point of sale, warehouse withdrawal, loyalty and retail demographic data)” to improve their business. But she cautions, “It is not as easy as it sounds. There are pitfalls and landmines, and major obstacles to overcome.” [“Three Things I Have Learned About Using Downstream Data,” Supply Chain Shaman, 20 December 2010] Cecere writes that one of the challenges is that many companies believe that the data integration provided by ERP systems is sufficient. She believes data “synchronization” is much more important than data integration. She writes:
“To synchronize and use the data, it must be cleaned, harmonized, and enriched based upon a carefully crafted data architecture and road map. Unfortunately, for many companies, they learn too late that ERP is not the de facto enterprise data model. … Stuffing downstream data into ERP and ignoring this process is like stuffing a square peg into a round hole.”
Cecere agrees with Simchi-Levi that analytics can be used in any number of areas to improve the supply chain; areas like: “sales reporting, category management, trade promotion management, replenishment, demand planning, improved transportation planning, cost to serve and client profitability modeling, inventory reduction and obsolescence reduction, item rationalization, shelf compliance sensing, new product launch success and demand orchestration processes (to translate the demand plan into buying strategies).” Like Simchi-Levi she notes that each use case requires a different approach and a different strategy to be effective.
Sid Snitkin, vice president and general manager of ARC Advisory Group, talked about another less discussed use of big data analytics with the editorial staff at SupplyChainBrain — analyzing “asset information.” [“Managing Data Flow from Multiple Assets, 29 June 2011] He told the staff, “It’s imperative for companies to efficiently manage the information generated by their assets, operations and maintenance.” If Ericsson, the Swedish technology firm, is correct, the world may soon see 50 billion devices continuously connected and communicating. The network over which they will likely communicate has been dubbed the “Internet of Things.” For more on that topic, read my post entitled Machine-to-Machine Communication. Snitkin asserts that asset information includes “all the information that’s needed to install, operate, manage, maintain and improve assets. In addition, such information usually includes the history of an asset – how it has been used, for what purpose, and by whom over a certain period. In isolation, that kind of data on one small asset may not seem like much, certainly not something that demands management technology.” As you can imagine, much of that data is going to be unstructured.
In the procurement area, Paul Teague, U.S. contributing editor of Procurement Leaders, reports, “Spend analysis has revolutionized procurement, and now the practice of using the resulting data to make strategic supply chain decisions is a goal many are talking about.” [“A role model for advanced analytics,” 10 September 2012] He continues:
“Ritu Jain, global marketing manager for supply chain at software developer SAS, says procurement has to go beyond just collecting data and using it for day-to-day operations to mining the data for real-time insights and use those insights to make better decisions, such as the impact of increasing prices on demand, and recommending whether a company should produce more of Product A or more of Product B to meet revenue and margin goals. It takes sophisticated technology to do that and, perhaps more importantly, the business acumen and creative insight to read the full implications of the data and apply them to various procurement issues.”
Supply chain analyst John Westerveld makes an important point about who should benefit from the insights provided by supply chain analytics. He writes, it should be “the planners, buyers, master schedulers and analysts. These people need to make the right decisions, quickly every day of the week. They have the right knowledge, but from my conversations, what I see is that their challenge has been that the supply chain tools that are given to them are letting them down.” [“Making the case for Response: knowing the right decision in time to make a difference,” The 21st Century Supply Chain, 13 June 2012] He claims that too often the analytical tools that supply chain professionals are given take too long to run, are presented in a way that makes the results difficult to understand, and you can’t run quick simulations within the necessary decision cycle.
How important are good analytics? Douglas Alexander, Principal Consultant at Component Engineering Consultants, believes “that the greatest advantage a company has over its competitors is knowledge.” [“Supply Chain Basics: Know Yourself & the Enemy,” EBN, 6 July 2012] He explains:
“The company that has a thorough understanding of where it and its competitors are in the larger scheme of things has the best opportunity to take that knowledge and turn it into its greatest advantage. No matter the nature of your ‘business,’ the goal is to exceed your competitor’s performance. … If your business has excellent data gathering processes and records, you will increase your competitive potential by making the corrective adjustments necessary to keep your company’s health at peak levels. Just knowing where you are starting from can help you get to where you are going. Your company should be organized such that you have this data readily available and kept up-to-date. As you analyze these numbers, you will get a feeling of what you need to change and what it will take to get to where you want to be.”
The number of ways that big data analytics can be used to help a company improve its operations and become more profitable is only limited by our imaginations.