Humans yearn for a glimpse of the future. This yearning is why people visit oracles, study the stars, consult shamans, get their palms read, stare into crystal balls, turn over tarot cards, and/or study the writings of seers like Nostradamus. Business leaders are not immune to this compulsion; but, their methods for divining the future are different. For decades, companies have gathered smart people together to perform what-if analysis, identify trends, and produce forecasts. Each of these activities still has value. Today, in addition to those activities, new analytical tools are available to help business leaders predict what could happen in the future. Those tools became possible with the creation of large datasets (aka big data) and the maturation of artificial intelligence (AI). Humans long to know more about the future because the unknown makes us uncomfortable — or, even worse, fearful.

In business, uncertainty can affect profits. Business consultant Greg Petro observes, “We are living in uncertain times. Looming risks like wars, environmental and labor regulations, natural disasters and transportation challenges are placing many retail supply chains and retail profit centers at the mercy of the unforeseen.”[1] If that doesn’t make you yearn to know more about the future, it should. Petro goes on to predict companies failing to grasp the future risk ending up in history’s dustbin. He writes, “Real-time is no longer fast enough. Modern [organizations] that build predictive analytics and automation into the supply chain can benefit from being able to predict future needs and facilitate necessary changes to their operations, eliminating time-wasting steps and staying ahead of the modern era while guarding against catastrophe before it happens.”

Types of analytics

Predictive analytics — the type of analytics to which Petro refers — are one of four types of analytics now available to enterprises. What you want from your data determines which kind of analytics you need to apply. George Karapalidis (@gkarapalidis), head of data science at Vertical Leap, explains the purpose of each type of analytics.[2] They are:

1. Descriptive analytics. Descriptive analytics can help discover what happened in the past. As Karapalidis puts it, “Before we learn where to go, we need to know where we came from. That’s the key question descriptive analytics solutions tackle.”

2. Diagnostic analytics. Diagnostic analytics can help explain why something happened. Karapalidis writes, “Diagnostic analytics tools help you uncover the root cause of some problems.”

3. Predictive Analytics. “Predictive analytics,” writes Karapalidis, “‘joins the dots’ between the accumulated and analyzed data points, conveying what and why something happened, into models suggesting what can happen next. It indicates the probability of certain outcomes with high accuracy and takes the guesswork out of your decision-making process.” Mark Dunn, Director of Nexis Data as a Service at Nexis Solutions, adds, “As machine learning and predictive analytics become more sophisticated, companies can base decisions on evidence, and deep learning will push the boundaries even more, with better problem-solving and language comprehension.”[3]

4. Prescriptive analytics. Prescriptive analytics informs you what you should do to achieve a particular outcome. It’s a type of analytics made possible by the emergence of cognitive computing technology. Karapalidis notes, “Prescriptive analytics is yet to move from the margins to the mainstream. It’s an emerging area of analysis attempting to answer the complex question of ‘what actions to take if I want to get outcome A?’ Prescriptive tools come up with multiple future outcomes based on your current/past actions; match those futures with your goal and advise you on the action you need to apply.”

Of course, to extract value using analytics you need the right data for the right model. In this article, I want to focus on the third type of analytics — predictive analytics.

The business value of predictive analytics

John Impellizzeri, an Assistant Professor of Professional Practice at the Rutgers Business School and former supply chain executive, predicts analytics will become a future enterprise differentiator.[4] He explains, “Twenty years ago, Boston Consulting Group’s Harold Sirkin warned that competition is no longer ‘company versus company but supply chain versus supply chain,’ signaling a new era of supply chain relevance. As we sit here today, perhaps we can refine Sirkin’s hypothesis to a battle over the competency of ‘supply chain analytics versus supply chain analytics’.” As Petro noted above, predictive analytics may be the most important type of analytics for business success. Gary Nakanelua, a Director at Blueprint Consulting Services, notes predictive analytics (like other types of analytics) are only valuable when properly applied. He writes, “For years, the application of predictive analytics in supply chain management has been described as ‘transformative,’ a ‘big opportunity,’ the ‘new business intelligence,’ and even ‘the holy grail.’ However, in conversations, there is often confusion on where and how predictive analytics can be applied.”[5] Among potential applications of predictive analytics are:

• Predicting business location. Although not every business is looking for a place to locate, when that decision is necessary, predicative analytics can help. Whether you’re looking for a retail location with access to the best clientele or a factory location with the best access to employees, resources, and transportation, predictive analytics can make the decision easier.
• Customer demand. Few, if any, factors are more important than getting customer demand correct. Nakanelua explains how predictive analytics can help achieve this objective. He writes, “The machine learning employed in predictive analytics models allows for large amounts of structured ERP and supply chain management data to be processed with seemingly disparate data such as consumer sentiment data which derives from Facebook, Twitter, Pinterest, Instagram and macroeconomic indicators such as GDP, unemployment, Leading Indicators Index, etc. Other disparate data include IoT device data, demographics, weather and other domain-specific factors like production lines, engineering changes, etc. However, predictive analytics is not just about collecting data, but enabling actionable insight into how, when, or why customers make purchases.”
• Fraud detection. Companies lose millions of dollars to fraudulent activity every year. Predictive analytics can help identify fraudulent activity so action can be taken.
• Price optimization. Retailers are always looking to maximize both sales and profits. Predictive analytics can help find the correct price point to achieve that goal.
• Inventory optimization. Nakanelua notes, “Inventory optimization helps reduce inventory distortion, a challenge that stems from out-of-stock and overstock inventory situations.”
• Operations maximization. Ashish Kumar, a data science professional, writes, “Marketing and customers are extremely important, yes. However, at the end of the day, the products and services have to be delivered with maximum operational efficiency. … Predictive analytics can prove to be an integral part of the planning and execution stages of operations.”[6]

Concluding thoughts

“Employing advanced analytics not only helps retailers and brands make decisions on bringing the right product to market at the right price the first time around,” writes Petro, “but [they] can speed up time to market and eliminate inefficiencies across the entire supply chain.” Nakanelua concludes, “Predictive analytics will help you identify trends, understand your customers’ purchase habits, predict purchase behavior, and drive strategic decision-making. If you’re not employing predictive analytics in your supply chain management strategy, put away your complicated spreadsheets and isolated databases and start considering the transition today.” Kumar adds, “Predictive analytics is something that is within reach for just about anyone and is waiting for its advantages to be exploited.” Isn’t it about time you started seeing the future?

Footnotes
[1] Greg Petro, “Economic Uncertainty Drives Next Phase In Supply Chain — Predictive Analytics,” Forbes, 4 November 2018.
[2] George Karapalidis, “Examining the four types of big data analytics,” The Drum, 18 December 2018.
[3] Mark Dunn, “Unlocking the potential of Big Data,” ITProPortal, 12 December 2018.
[4] John Impellizzeri, “Future of retail may be decided by supply chain analytics,” Rutgers Business School, 5 March 2018.
[5] Gary Nakanelua, “A Primer on Predictive Analytics and Supply Chain Management,” Consulting, 12 September 2017.
[6] Ashish Kumar, “5 Ways how Predictive Analytics Can Help You,” Datafloq, 13 March 2018.