Silent Might

Silent Might

Taking the strategic approach to implementing RFID technology

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.

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 analysing it to inform business actions is akin to hoarding 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 Defence 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, change 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 analyzes 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 per cent 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 modelling is already used, as well as bring predictive analytics to more real-time, tactical applications.

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