The Role of Prescriptive Analytics
There has been a lot of hype surrounding prescriptive analytics over the past few years, but with few real-world applications.
However, this is about to change as industries including healthcare, manufacturing, logistics, retail, hospitality, financial services and telecommunications all fully embrace the technology. The goal is to radically improve efficiency, reduce costs, create new revenue opportunities, and take customer satisfaction and loyalty to new levels.
In the era before prescriptive maintenance, manufacturers could only run equipment until it failed, or estimate its useful life and then retire it before it broke. Retailers only realized that their inventories were low when they ran out, or when someone went to the warehouse to conduct a stock-check. Financial institutions lost millions or billions of dollars due to fraud, which was only discovered long after the fact. All these problems can now be sidelined through the power of prescriptive analytics.
Prescriptive Analytics Going Mainstream
What has pushed prescriptive analytics mainstream is the availability of masses of data from the thousands of sensors that constitute the Internet of Things and the ability to use it for continuous prediction without intervention.
Nowadays, we can analyze and act on data gathered from continuously monitoring the actual conditions and actions of equipment, staff, inventories, trades, and anything else that impacts a business. The aggregate amount of data is mind-boggling (described in terabytes, petabytes and exabytes) and it continues to grow at an exponential rate.
The key advance in building prescriptive analytics has been the use of statistical models with historical data. Predictive analytics can now be deployed so that shipping delays can be prevented by foreseeing bottlenecks, fraud can be stopped by detecting early red flags, and equipment failure can be anticipated by conducting frequent checks and maintenance. In the retail business, store operators can order more inventory before it runs out. In the healthcare industry, physicians can quickly identify high-risk patients and intervene preemptively.
The Impact of Prescriptive Analytics
Prescriptive analytics can be applied to numerous industries and other facets of business. For example, it can be used to determine when a retailer’s competitors are likely to be lowering prices, prompting automatic preemptive action via digital shelf-edge labels. Indeed, within a store, sensors can also automatically signal when shelves are likely to be low on goods, alerting staff via smart badges.
In the logistics industry, prescriptive analytics allow supply chain managers to receive a definitive time of arrival for shipping, based on a dynamic statistical prediction model.
In manufacturing, data streaming from single components or entire pieces of equipment can be used to predict the possibility of future failures, allowing the arrival of new components to be synchronized with that of the repair technician.
In healthcare, alerts can be sent to the primary provider or subspecialist when a patient may be undergoing a life threatening cardiovascular event.
Taking Advantage of the Influx of Data
The key requirement, of course, for successful deployment of prescriptive analytics, is for an enterprise to be able to analyze vast fast flows of Big Data. These will stream through from its own operations and from relevant sources in its customer-based, market or news channels. The volumes are so big that they cannot be fathomed without the use of data scientists, computing power, or algorithms.
Data specialists, who employ computer and math skills along with their own curiosity and creativity, mine mountains of data to find competitive opportunities – and to predict likely future outcomes.
Data scientists, however, are in short supply, which is why firms need to become more creative. They need to integrate data science more tightly with IT departments and build teams that include computer experts, mathematicians, statisticians, and business specialists. All these talents are needed if an enterprise is to crack the Big Data code and really derive value from it.
A Brief Look into the Future
As Big Data becomes more accessible – in part through increased adoption of open data standards – and as analytical tools become more readily available, more enterprises will enjoy the benefits of prescriptive analytics.
We will soon see some game-changing use cases where goods are automatically ordered and delivered to the warehouse before a sales campaign causes a shortage or where a diagnostic imaging study is ordered before the patient begins experiencing signs or symptoms of an ailment.
Haulage trucks will meet ships as they arrive at a port and deliver their products on time, with every traffic and weather delay taken into account. A continuous, smoothly flowing logistics network will result from the greater synchronization that is enabled by a prescriptive analytics capability.
In the financial markets, trading patterns will set off alarm bells about the threat of insider trading, allowing a bank to take action before regulatory breaches occur.
Prescriptive analytics using data from sensors fitted to a patient will even give doctors the ability to call a man with a heart condition and tell him to get to hospital immediately because he is going to go into cardiac arrest tomorrow and they need to intervene to save his life.
This is prescriptive analytics. And in an uncertain world, one thing we can predict is that it will deliver huge benefits for any business that has the foresight to invest in it.
