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28 CSCMP’s Supply Chain Quarterly [QUARTER 4/2011] www.SupplyChainQuarterly.com www.SupplyChainQuarterly.com [QUARTER 4/2011] CSCMP’s Supply Chain Quarterly 29

[BY THOMAS H. DAVENPORT AND JERRY O’DWYER]

FINANCE GLOBAL LOGISTICS MANUFACTURING PROCUREMENT [STRATEGY] TECHNOLOGY

MANY COMPANIES TODAY are aggres-sively employing analytics—the sys-tematic use of quantitative and statis-tical decision methods—in theirbusinesses. There are many differ-ent application domains for ana-lytics, ranging from marketing tohuman resources to finance. It isonly natural, then, that the

next generation of supply chainsshould incorporate a higher and more sophis-

ticated level of analytics.Applying analytics in supply chain management is

not a new idea. The U.S. military adopted a variety oflogistical models in World War II, and companiesadopted related approaches in the postwar period.UPS, for example, established a logistical analyticsgroup in 1954. Since then, many companies have suc-cessfully employed analytical approaches to distribu-tion networks, inventory optimization, forecasting,demand planning, risk management, and other appli-cations. Large retailers, such as Wal-Mart Stores andTarget, have had considerable success with supplychain analytics, often working in collaboration withsuppliers. And carriers like UPS, FedEx, andSchneider National wouldn’t dream of managing theiroperations without a variety of analytical models.

Yet supply chain-related analytics activities haveplateaued in many organizations in recent years.Other than the occasional re-tuning of supply net-works that has principally focused on cost manage-ment, companies have not taken advantage of all thatsupply chain analytics can offer to their businesses.Further, even when analytical tools are available tofront-line supply chain personnel, the tools often gounused because of a lack of skills or understanding.

We believe that there will be a set of new frontiersin supply chain analytics that will lead to dramatical-ly higher levels of performance. If companies are toachieve these rewards, however, they will have to bemore ambitious in their analytical goals and invest-ments. In this article we describe a number of rela-

tively new domains for supply chain analytics as wellas the opportunities and primary obstacles for each.We also describe several ways in which the day-to-dayusage of supply chain analytics will change in thefuture.

Connect demand and supply in real timeOne of the most important attributes of next-genera-tion supply chain analytics is that they will addressissues beyond the supply chain. To optimize opera-tions, companies need to link their supply chains withmetrics and analytics on the demand side. For exam-ple, at the simplest level, price changes or promotionsfor products will change demand and hence therequired supply of those products. Similarly, changesin the availability of products and components shouldbe reflected in marketing and sales processes.

This integration of supply and demand was pio-neered in the 1990s by Dell Computer, which wasable to suggest to call-center customers ways to short-en delivery time or take advantage of excess invento-ry. This was mostly dependent on human decisionmaking: manufacturing supervisors would track sup-ply levels and notify sales and marketing managers,who would then promote or downplay particularitems and configurations based on their availability.But in a real-time, online business environment, com-panies will need to have analytical models in placethat will continuously integrate supply and demandwithout human intervention. Such models would, forexample, automatically extend offers and promotionsto customers based on the availability of inventoryand components. There has been a shortage of initia-tives in this area since Dell’s pioneering work, but thedirection for future innovations is clear.

The analytics needed for such models are not terri-bly difficult, though they would require considerableiteration and tuning. The primary obstacle to imple-mentation generally is a lack of collaboration amongthe organizational groups that are responsible for sup-ply and demand. Another difficult issue is the integra-tion of all the necessary data, which often comes from

Tap into thepower of

analytics

New ways of applying supply chain analytics can lead to dramatically higher levels of performance.

Here’s where to find the best opportunities.

light, temperature, tilt angle, gravitational forces, andwhether a package has been opened. They can trans-fer data in real time via cellular networks. Obviously,the potential to identify supply chain problems in realtime and take immediate corrective action is greatlyenhanced with this technology. We have only begunto consider how analytics might be used to enhancethe value of ILC-derived data.

Improving analytical “literacy”The next-generation approaches to supply chain ana-lytics involve not only new applications but also newways to ensure that analytics are used to make strate-gic and tactical decisions. Unfortunately, better deci-sion making in supply chain management is oftenhindered by the inability of managers and front-linepersonnel to understand and apply analytical models.

We have encountered several companies that hadconsiderably upgraded the analytical capabilities oftheir information systems (for example, by addingadvanced planning and optimization modules forenterprise resource planning [ERP] systems) but hadmade no changes in associated personnel or their ana-lytical skills. As one supply chain manager told us,“We need only half the people to do the work withthese new tools, but they need to be twice as smart.”For supply chain personnel to become smarter aboutanalytics, they must be educated about analytics andtheir implications, retrained, or in some situationseven replaced.

There are a variety of approaches to achieving thedesired level of analytical literacy. The motor carrierSchneider National, for example, has developed a sim-ulation-based game to communicate the importanceof analytical thinking in dispatching trucks and trail-ers. The goal of the game is to minimize variable costsfor a given amount of revenue while maximizing thedriver’s time on the road. Decisions to accept loads ormove empty trucks are made by the players, who areaided by decision-support tools. Schneider uses thegame to help its own personnel understand the valueof analytical decision aids, to communicate thedynamics of the business, and to change the mindsetof employees from “order takers” to “profit makers.”Some Schneider customers have also played the game.

Another way to facilitate the understanding of sup-ply chain analytics is through simpler applicationswith narrow functionality. Increasingly referred to as“analytical apps,” these tools are similar to the appli-cations found on smartphones. They support a singledecision and often are industry-specific. Several busi-ness intelligence and analytics software vendors areintroducing them, and they promise to make the useof analytics much simpler and available to users whodo not have extensive analytical or technological

skills. Analytical apps that have already been devel-oped for supply chain functions include tools for sup-plier evaluation, inventory performance analysis,transportation analytics, and transportation contractcompliance. There undoubtedly will be many othersover the next several years.

Perhaps the only way to guarantee the use of ana-lytics in supply chain management is to embed theminto supply chain-oriented systems and processes. Nohuman would be involved in the decision unless thereis an exception. For example, certain supply chaindecisions made at least partially on the basis of statis-tics and probability (such as available-to-promiseinventory, or the likelihood that an ordered productwill be returned by the customer) could be embeddedin an order management system. Vendors of ERP sys-tems expect to have such capabilities in the next sev-eral years.

The future of supply chain analyticsThe use of such tools as ERP systems, the Internet,RFID, and telematics is becoming more common, andmore organizations are generating considerableamounts of high-quality data. Now that companieshave more and better data than ever before, it is onlynatural that they would begin to use it to analyze,optimize, and make predictions about their supplychains.

The most common analytical activities thus farhave been descriptive—straightforward reports aboutwhat has happened in the past. But in future supplychains, we expect to see more prediction and evenprescription—that is, optimization and testing modelsthat tell supply chain managers what they should doto improve performance.

Employing emerging supply chain technologies andprocess improvements has always been an importantpath to competitive advantage. We believe the nextmajor approach to supply chain-based competitionwill involve the extensive use of analytics. r

Endnote:1. Claire Swedberg, “Daisy Brand Benefits From

RFID Analytics,” RFID Journal, January 8, 2008.(http://www.rfidjournal.com/article/view/3860)

www.SupplyChainQuarterly.com [QUARTER 4/2011] CSCMP’s Supply Chain Quarterly 3130 CSCMP’s Supply Chain Quarterly [QUARTER 4/2011] www.SupplyChainQuarterly.com

[TAP INTO THE POWER OF ANALYTICS]

multiple transaction systems, in a way that allowscompanies to make informed decisions in real time.

Analyze supplier riskMany companies recognize that the success of theiroperations is highly dependent upon their suppliers.Yet supplier risk analytics have hardly moved beyondsimple metrics and reports in most organizations. Themost sophisticated approaches to supplier risk moni-toring and management—used by companies thatheavily depend on external suppliers and contractmanufacturers, such as Cisco Systems—are onlysomewhat more analytical.

One example is the creation of a supplier resiliencyscore based on several variables. The variables arebased on logic (for instance, reports of bad weathernear suppliers’ manufacturing locations). If thevariables or the overall resiliency scores sug-gest a problem, companies can thenpursue secondary sourcingor work with existing sup-pliers to identify alternatelocations. These scoringmodels increasingly incorpo-rate relatively subjective factors,such as perceived economic andpolitical risk. But while supplierrisk and resiliency scores are undeni-ably useful tools, with few exceptionsthey are not yet based on statistical analysis.

Of particular interest to many companies now iswhether critical suppliers that weathered the last eco-nomic downturn will be capable of meeting increaseddemand during an upturn. Analytic tools that incor-porate public, third-party data can help companiesassess this risk.

As companies accumulate more experience withsupplier risk, they can begin to create predictive sta-tistical models that are based on actual supplier fail-ures. This would, of course, require tracking and ana-lyzing a sufficient number of actual supplier failures toallow them to accurately identify attributes associatedwith failure.

Interestingly, the current leaders in statisticallyassessing supplier risk generally are not the manufac-turers but the firms that insure them against such risk.Because the insurance industry has a strong actuarialtradition, firms such as Aon and Marsh have devel-oped statistical models of the likelihood of supply andsupplier risks. The key variables considered in thesemodels are the frequency and severity of those risks.

Take advantage of sensors One of the primary drivers of analytics in organiza-tions is the availability of extensive data. As their use

expands, new sensors—in particular, radio frequencyidentification (RFID)—will make dramatic amountsof data increasingly available for the next generationof supply chains.

For more than a decade, supply chain managershave been bombarded with warnings that RFIDdevices and networks will change their lives. Thus far,however, the high price of RFID technology has pre-vented widespread deployment from taking place. Butprices for RFID tags and readers continue to fall,albeit slowly, and the adoption rate is gradually rising.

At some point in the next several years, most man-ufacturers and retailers are expected to deploy somedegree of RFID capability. When that happens, agreat deal of RFID-generated data will be available foranalysis. Initial applications using RFID data will pri-

marily be transactional, but shortlythereafter organizations will want to

monitor and optimize the efficiencyand effectiveness of their RFID

networks. This set of applica-tions will demand the use of

sophisticated supply chainanalytics.

Some companies haveemployed RFID analytics for several

years. For example, Daisy Brand, adairy products manufacturer in the

United States, began using RFID analyt-ics in 2007 to track how long it takes products toreach the store shelf as well as replenishment rates.Prediction of replenishment rates is particularlyimportant during promotions. In addition to RFIDdata, Daisy Brand also makes extensive use of Wal-Mart Stores’ Retail Link data, which provides suppli-ers with weekly point-of-sale and inventory informa-tion, in its analyses.1

Sensors for more expensive and substantial supplychain assets are already in wide use. Some major carri-ers, for example, are deploying geographic positioningsystem (GPS)-based telematics devices in trucks andtrains. These devices provide a wide variety of dataabout driving behavior, speeds under various condi-tions, traffic, and fuel consumption. Companies suchas UPS and Schneider have already employed telem-atics data to redesign logistical networks in whole or inpart. UPS, in fact, is using telematics data to redesignand optimize its entire delivery network for only thethird time in its more than 100-year history.

Other types of sensors are likely to lead to a flood ofadditional data—and opportunities to analyze it.RFID and telematics sensors primarily track location,but so-called ILC (identification, location, condition)sensors can also monitor the condition of goods in thesupply chain. ILC sensors monitor such variables as

THOMAS H. DAVENPORT (TDAVENPORT@BABSON.EDU) IS RESEARCH

DIRECTOR OF THE INTERNATIONAL INSTITUTE FOR ANALYTICS,

PRESIDENT’S DISTINGUISHED PROFESSOR OF IT AND MANAGEMENT AT

BABSON COLLEGE, AND A SENIOR ADVISOR TO DELOITTE. JERRY

O’DWYER (JODWYER@DELOITTE.COM) IS A PRINCIPAL WITH DELOITTE

CONSULTING LLP, WHERE HE LEADS DELOITTE ANALYTICS FOR THE

STRATEGY AND OPERATIONS PRACTICE.