What You Need to Know About Prescriptive Analytics


Prescriptive analysis clears the fog of complexity that executives have been coping with when it comes to heuristics. While heuristics helps to promptly assess trade-offs, the risk with them is a mental lens too narrow especially in fluid situations. They miss key data and cause decision errors.

Prescriptive analytics evaluates decision options in uncertain conditions and their expected outcomes and impact. With big data, it crunches data comprehensively, at varying levels of granularity, for decision-makers to bring light to gray areas in decision-making. By reducing latencies in the assessment of options, executives are able to continually reexamine assumptions. They gain a multidimensional situational awareness that minimizes the prospect of executives being blindsided by a narrow view of their choices. Prescriptive analytics rapidly parses data to answer “what-if” questions to anticipate challenges, make course corrections, and preemptively respond to worst-case scenarios.

Prescriptive analysis is the pinnacle in the maturity curve of analytics, and 2016 is expected to be the breakout year for it following the success of predictive analytics. Currently, only 3 percent of users of analytics take advantage of prescriptive analytics, compared to 30 percent who use predictive analytics. “Predictive analytics is more narrowly focused compared to prescriptive analytics. While prescriptive analytics often uses the same raw data as predictive analytics does, but more likely the forecasted data from predictive models, the volume of raw data expands as a gamut of physical data such as bill of materials is also included,” Eric Kelso, vice president of Product Management at River Logic explained to me.


Corporate prescriptive analytics
Corporate strategic and tactical decision making, investments, mergers and acquisitions, and capacity planning all require decisions that can be optimized through the use of prescriptive analytics. All too often, individual departments like sales make deals that could have downsides for another department like finance. Similarly, companies can overlook the opportunity costs of alternatives foregone. An integrated view of decisions ensures that the company overall benefits from prescriptive analytics driven optimization.

My conversation with Eric Kelso revealed a striking business case of business gains realized as a result of prescriptive analytics that uncovered unsuspected opportunities. Cox Industries, a River Logic customer, increased its margin by over 2% after the wood manufacturing company decided to abandon a long-time business unit as its largest customer became more demanding. “What-if analysis revealed that new business opportunities would be much more profitable than keeping that existing business,” Kelso said.


The IoT and real-time prescriptive analytics
The Internet of Things expands the scope for “what-if” analysis with microscopic data for optimization of operations. According to ABI research, the share of revenues from prescriptive analytics with M2M data is expected to rise from 1 percent in 2013 to 9 percent in 2018. Data from the Internet of Things is especially useful for real-time decision making in crisis situations or those prone to be so.

Unsurprisingly, the early uses of prescriptive analytics are in trauma care in the healthcare industry, oil and gas industry, and the supply chain. The Alfred Hospital in Australia developed a real-time response system to cope with the critical first 30 minutes in trauma care. In this short period, a team of physicians, nurses, and other staff have to work as a team, gain situational awareness and attend to injuries before irreparable harm is done. The hospital’s algorithm is the key to rapid and effective response; it is trained with evidence from past cases and medical research. Its real-time data input is vital signs data of the patient as well symptoms and the data on the injury. Based on the real-time data, the algorithm churns out recommendations for therapies. The medical team is readied to respond quickly for surgery with visual display of the information and possible therapies.

Credit: Wikipedia and NOAA
Credit: Wikipedia and NOAA

Consider the potential for prescriptive analytics in oil and gas. Shale oil is characterized by wide variance in productivity of individual oil wells. Drilling is more productive with precision targeting, but that takes more time to plan than boring over a wider area. Precision targeting also tries to find a focal point with clusters of oil surrounding the well bore that will maximize yield. A related consideration is the spacing between the well bores; they are more likely to have an adverse effect on each other if they are too close. The real-time data from acoustic sensors is used to make the assessment of alternative courses of action. It is combined with large volumes of geological and geophysical data, 3D imaging, and structured data of wells to gain insights. The data on the progress of drilling generated in real-time assists in reviewing the initial assumptions and postulating new hypothesis for increasing productivity.


Fitting into the supply chain
Supply chain is the natural sweet spot for real-time optimization with a gamut of operations decisions for capacity utilization, scheduling of cargo deliveries, and demand fluctuations. I spoke to Jim Hayden, Vice-President, Solutions, at sensor company Savi to learn about decisions and trade-offs addressed by prescriptive analytics with real-time and historical data for supply chains. “Our data scientists train our algorithms with historical data and our software triggers them with real-time data feeds of events from the Internet of Things,” Hayden said.

The algorithms assist in providing a variety of services customized for each stakeholder in supply chains. “For manufacturers, Savi’s algorithms enable the optimization of sources of materials and raw materials by estimating the expected time of arrival for each of them,” Hayden said.

By contrast, the retailers want to weigh the trade-offs of timely delivery versus the transportation costs that rise inversely with time allowed to load cargo on trucks. “Real-time data crunching of data helps to match the flow of demand at retail stores with the supply as it is affected by the pace of deliveries,” Hayden explained.

Finally, the multimodal shipping companies want to control the demurrage costs incurred at the ports for the storage of cargo before it is moved out by trucks. They can minimize these extra costs that are tied to the use of a ship while it is in port, by synchronizing inbound shipments at the port with the outbound haulage by trucks. The application uses marine traffic, shipping, sensor and satellite data to estimate the expected time of arrival for ships and the estimated time delays at customs, "depending on the type of cargo to synchronize the availability of trucks to shorten the time spent at the port,” Hayden said.

Algorithm driven prescriptive analytics lowers the latencies in decision-making caused by assimilation of complex and large volumes of data. It reduces the data to simple metrics to evaluate trade-offs and visualizes the impact for rapid assimilation. The early use cases can only whet the appetite for more.

Kishore Jethanandani, Market Researcher

Kishore Jethanandani's current professional focus is content development and communications of complex subject matter for clients in the technology and financial services industry in the San Francisco Bay and Boston area. His career has evolved from industrial economic research with an accent on policy reform to a business journalist while he was in India to marketing writing and research in the USA. A technology buff and a futurist, he has an instinct for spotting emerging technology and market trends and their likely impact on business strategy and competition.  His personal slogan is "A hedgehog who senses the future". My website can be found at www.futuristlens.com

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Re: Interesting perspective on the trend towards prescriptive analytics
  • 3/23/2016 10:08:43 AM
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I would also like to hear more about the case of abandoning a client in favor taking on a new one. I'm wondering can the results be objectively shown to be a result of the analyticss or maybe just a chance result. It would be interesting to compile case studies and see just what's reliable or not in these decisions.

Re: Beyond prescriptive analytics: cultural analytics
  • 3/22/2016 7:18:02 AM
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@Kishore, you told me it would be instructive to know what I was able to achieve way back.  Well, I've been thinking about writing a blog on this one.

Re: Beyond prescriptive analytics: cultural analytics
  • 3/21/2016 1:36:09 PM
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Kishore, the OR project I was involved with was about the supply chain. I could say it was part of strategy planning we used to examine various trade-offs and options. 

Re: Beyond prescriptive analytics: cultural analytics
  • 3/21/2016 12:10:22 PM
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@jmyerson2: I was responding to the question as it was framed which was at a basic level. I understand your point about operations research which is becoming more widespread with the availability of data in real-time. It is interesting that you tried to do it way back. It would be instructive to know what you were able to achieve then and could not do with a limited toolset.

 

The example of supply chain illustrates that point. OR is being done for supply chain for manufacturers, transporters and retail companies. 

 

However, operations research is not the only use case. Strategic planning is also a way to use prescriptive analytics because again you examine multiple options. In general, it is about looking at trade-offs. For those of us who have studied economics, this is fairly straightforward but I might have assumed too much. 

Re: Beyond prescriptive analytics: cultural analytics
  • 3/21/2016 6:09:52 AM
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Kishore, your distinctions are good ones for those who don't know.  But I already knew the distinctions. See my previous comment about that I was selected out of 500 employees by the Director of Operations Research to build prespective analytics models from the results of the OR -- decades ago.

Beyond prescriptive analytics: cultural analytics
  • 3/20/2016 10:15:55 PM
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@Terry, prescriptive analytics is not new.  The era of big data created a boon in operations research.  The volume and variety of big data helps OR build prescriptive analytics models.  These models however are not the final stage of development in the analytics evolution.  The next stage is cultural analytics (think IBM Watson machine). How do I know about the applying big data to OR. My answer is an easy one.  I was selected (out of 500 employees) by the Director of OR to build "prescriptive analytics models" a couple of decades ago. 

Re: Making distinctions
  • 3/19/2016 12:27:48 PM
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Hi Terry: Great question to ask to clarify the basics of analytics. There are three types of analytics--descriptive, predictive, and prescriptive. Descriptive is like reporting--summary statistics of the data. Typically, it will lay out the dependent and independent variables in a tabular form. An example would be the gender and preferences for say types of shoes. Predictive statistics has forecasts such as how much demand will be generated based on historical data and real-time data on traffic. Prescriptive is about alternative courses of action and the outcomes you could expect. So it will tell you, for example, what decisions to take if your predicted sales are falling short of the desired outcome. Should you expand your territory to expand sales. If yes, what would be the impact on margins if you invested more in marketing to more regions. Hope this helps. If not, feel free to let me know where it is not clear.

Making distinctions
  • 3/19/2016 11:24:42 AM
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I don't mean to quibble or be obtuse here, Kishore... but aren't all analytics essentially prescriptive analytics? Or is the position you're taking here is only as it's applied to Big Data? If you could further clarify the distinction, thaty would be helpful.

Interesting perspective on the trend towards prescriptive analytics
  • 3/17/2016 6:07:12 PM
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Good overview on prescriptive analytics, particularly noting the influence from algorthms as well as the increased spending. I think many industries have realized analytics as essential - they are developing an appreciation for advanced analytics as a necessary next step. I liked the example in this post regarding the decision of a current client vs pursuing better opportunities with new businesses.

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