• United States



by No Analyst or Consultant

Taking the Strategic Approach to Implementing RFID Technology

Dec 10, 20038 mins
CSO and CISOData and Information Security

By Sanjay Mathur

and Michael Bechtel

A great deal has been written about the flood of information already pouring into corporate data warehouses and the need to mine this data more effectively for business benefit. This need is real and should be a priority. But it is only the beginning of the information – and the opportunity – that is coming.

Many companies today are already exploring the benefits of sensing and tagging technologies, such as radio frequency identification (RFID) tags, to collect observations from the physical world. When combined with wireless communication, these tags and sensors enable what Accenture calls Silent Commerce: objects communicating directly with customers, suppliers, employees-and even each other-to create business value.

Almost any physical item can be embedded with electronic tags and sensors to establish a unique and verifiable identity, store a wealth of information and sense changes in the environment. Imagine a distribution center where the individual products have been tagged during production or packaging, enabling every item, case and pallet to be tracked and their status monitored. RFID readers could track inventory flow into and out of a building as well as update inventory count accordingly. The current, labor-intensive, check-in process will be virtually eliminated, as will many of the clerical functions that are now required to support the inventory receiving process.

Information gathered by Silent Commerce technologies could be used to maintain more accurate inventories resulting in greater efficiency within the warehouse, more accurate order fill rates, improved customer service and faster inventory turns. Silent Commerce can enable manufacturers to remove damaged products “on the fly” from production and logistical chains. Products can indicate if they have been misplaced, lost or stolen, thus reducing shrinkage and enhancing security. Greater volumes of data will allow enterprises to identify patterns earlier than they can today, which in turn will improve forecasting and replenishment applications.

Turning information into insight

Silent Commerce offers immense, real-time, quantities of new data about the physical world. But the challenge for business leaders is not simply to manage an even more intense data overload. Collecting a terabyte of data in a warehouse without analyzing it to inform business actions is akin to hording money rather than investing it: it’s not just how much you have, but what you do with it.

A useful model for thinking about this process is the OODA loop. The US Department of Defense acronym stands for Observe the situation, Orient yourself to it, Decide what to do, and Act. OODA is a loop because action changes the situation, so the process begins again with the observation of the new reality. Success goes to those who move through the loop fastest, because they are most likely to exert control over the situation.

Now apply the OODA loop to business. Today, too much time is typically spent in the Observe stage of the cycle, and too much of the data is historical (sales figures, product performance) rather than current (where is the product in the supply chain?). RFID tags, in conjunction with sensors, changes that by observing for us and reporting those observations in real time. The challenge now is to quickly orient, decide and act.

Orienting is the process of using analytics to understand what the data is saying, so that we can make informed decisions and take the right actions more quickly. The types of analyses that can be performed using Silent Commerce data can be broadly classified in two categories: descriptive analysis, which seeks to understand the information we have, and predictive analysis, which tries to predict information we do not have.

Descriptive analyses set out to find facts already in the data. These analyses can run the gamut from simple totals and averages to complex association rules and clustering algorithms. Before conducting any analysis, it is critical to know what question you are asking of your data, and what action you will take once you know the answer.

Predictive analyses use existing data to predict facts or anticipate events. These models solve for unknown variables by leveraging trends and patterns detected in known data. While there is an inherent element of probability involved, predictive analyses do not have to approach 100 percent accuracy to be valuable. Simply being better than random will add value. For instance, most of us pay attention to the weather forecast, even though it sometimes misses the mark. Silent Commerce data can improve functions such as inventory and demand forecasting, where predictive modeling is already used, as well as bring predictive analytics to more real-time, tactical applications.

Silent Commerce and data management

Silent Commerce implementations and related analytic capabilities will require enterprises to anticipate and accommodate a range of data management issues.

Standards: Companies will need a standard, universal way to identify and describe products regardless of which manufacturer tags them. Leading the development and adoption of several standards which promise to solidify the Silent Commerce landscape are the not-for-profit EPCglobal and its predecessor, the Auto-ID Center, an academic research project headquartered at the Massachusetts Institute of Technology. Electronic Product Codes (EPC) are expected to supersede the familiar Universal Product Code and give companies a globally standardized, integrated, automatic way to track products in real time along the entire supply chain.

Standards for data interchange are also being established in the form of Physical Markup Language (PML). As an instantiation of XML developed to describe real-world objects, Physical Markup Language is becoming the “lingua franca” of Silent Commerce data and analytics. Furthermore, the advent of the Object Naming Service (ONS) supports the widespread distribution of Electronic Product identification Codes data by making Physical Markup Language services addressable and easily found over the Internet.

Distribution and ownership: Currently, most RFID pilots exist in a closed system environment. The ultimate goal, however, is to track items through the entire supply chain. Many companies may initially be hesitant or unwilling to share their data with trading partners. Accenture believes that as companies see the revenue gains or cost savings that early adopters are realizing-even from closed systems-they will be more inclined to share information.

The Uniform Code Council, through its UCCnet not-for-profit organization, is facilitating the creation of contractual obligations regarding data sharing amongst trading partners and trading exchanges. A related issue is who “owns” the massive amounts of event information associated with, and added to an object, as it passes through a supply chain? Enterprises must examine the value of data ownership throughout the life cycle of the product and understand the value trade-offs between sharing data versus remaining in a closed system.

Privacy and security: Tied to data ownership is the issue of privacy for consumers and businesses. Understanding technology limitations will help ameliorate many concerns, such as those surrounding RFID tags in clothing. The Auto-ID Center is organizing a Privacy and Security Special Interest Group to address RFID-related privacy issues, but there will not be one “silver bullet” answer. Companies should start by communicating what they intend to do with the data they collect. Wherever personal or proprietary data is concerned, enterprises need to build trust with the owners of that data, and understand their unique concerns and motivations. With that understanding, organizations can develop and publish guidelines that protect their customers’ personal information while differentiating themselves in the marketplace.

Getting started

Accenture recommends that companies interested in using Silent Commerce to generate new business insights begin pilot applications in a specific area of operation. Pilots help companies identify processes that need improvement, as well as requirements for integrating Silent Commerce technologies with legacy applications and processes. To delineate data needs and ensure a focused approach to solving data management issues, companies should identify what questions they want to answer about specific processes and understand how the answers will help improve their business.

When inexpensive sensors and tags are used throughout the extended enterprise supply chain, businesses will have access to the kind of granular data traditionally associated with customer relationship management. Just as enterprises know the individual details (name, age) and aggregate attributes (segment, lifecycle) of their customer base, Silent Commerce enables the tracking of the micro and macro attributes and behaviors of the physical objects which make up a business.

Unlike customer relationship management however, supply chain analysts, for example, do not yet have the option to purchase third party “demographic information” for their products. The data we analyze will be limited to the data we choose to collect. It is for this very reason that Accenture encourages companies to start tagging now, so they can later ask questions that offer actionable insight and an edge on the competition.

For information about Accenture Technology Labs’ research into Silent Commerce and Information Insight, visit

About the authors

Sanjay Mathur, a senior manager with Accenture Technology Labs, Accenture’s research and development group. He is the global lead for the Labs’ information insight research and development initiative. Mathur’s current projects involve applied business analytics, such as knowledge integration and discovery, dynamic pricing, privacy and trust, and identity and rights management. He can be reached at

Michael Bechtel is a senior consultant with Accenture Technology Labs, research and development group. He focuses primarily on information insight research and development, working to bring advanced data storage, analytics and visualization technologies to clients. He can be reached at