Prescriptive analytics: customized supply chain plan

Prescriptive analytics is applicable to any area of an organization. But it’s especially important for the supply chain, which generates huge amounts of data. The business analytics method collects all possible data and converts them into accurate information, enabling companies to understand what happened and what could happen so that they can devise an optimal course of action.

By using technologies such as big data, artificial intelligence (AI), and data mining, prescriptive analytics helps organizations to optimize their supply plans. Additionally, it enables them to drive and automate decision-making to achieve their set targets.

What is prescriptive analytics?

Prescriptive analytics falls under the category of business analytics. It consists of gathering data, recommending actions, and foreseeing their impact to facilitate decision-making, identifying the best possible solution. Consulting firm Gartner describes prescriptive analytics as the answer to a question: “What can we do to make _______ happen?”

Unlike other kinds of analytics — e.g., descriptive or predictive — prescriptive goes beyond forecasting results. That is, it suggests actions that will result in the greatest benefits for the company. The academic paper Prescriptive analytics: Literature review and research challenges, published in the International Journal of Information Management, indicates that prescriptive analytics “seeks to find the best course of action for the future.” The authors say: “Prescriptive analytics is often considered as the next step towards increasing data analytics maturity and leading to optimized decision-making ahead of time for business performance improvement.”

Prescriptive analytics is based on operations research, predictive analytics, and statistical techniques to quantify the effect of future decisions. By employing current and historical data, it allows companies to assess what is likely to happen and recommend, among other alternatives, an optimal action plan. Prescriptive analysis relies on management systems and algorithms that automate decision-making and improve organizations’ operational efficiency.

The Gartner study Forecast snapshot: Prescriptive analytics software predicted that the prescriptive analytics software market would reach $1.88 billion by 2022, with a compound annual growth rate (CAGR) of 20.6%. According to Gartner, prescriptive analytics is characterized by the use of techniques such as graph analysis, simulations, recommendation engines, heuristics, and machine learning. Prescriptive analytics works by collecting, adapting, and managing data to better leverage resources and boost operating efficiency.

Differences between descriptive, predictive, and prescriptive analytics

Descriptive analytics consists of collecting and analyzing historic data to find out what has happened and determine the current state of the business. Predictive analytics, on the other hand, combines historical data, rules, and advanced algorithms to anticipate or estimate what could occur. Its objective is to forecast and get ahead of future situations.

Finally, prescriptive analytics is considered the third phase of business analytics because it takes information from the two kinds of analytics most commonly employed by companies: descriptive and predictive.

The main difference between descriptive, predictive, and prescriptive analytics lies in the type of intervention made by the organization after analyzing the data. Prescriptive analytics helps businesses know how to optimize their processes and figure out what they can do and how they can do it to meet those targets — or avoid them if they’re negative. As opposed to descriptive and predictive analytics, prescriptive uses simulation and optimization techniques to determine the best course of action for a specific situation.

Prescriptive analytics: supply chain applications

Prescriptive analysis supports decision-making relating to manufacturing and logistics, optimizing a company’s supply chain. The main applications of prescriptive analysis to the supply chain include:

• Consumer trend forecasting: prescriptive analytics can make deductions regarding consumers’ intentions and predict demand patterns to make the right decisions. Calculating consumer trends lets you establish optimal inventory levels to satisfy demand, prevent stockouts, and avoid overstock.
• Promotion of product traceability. Prescriptive knowledge facilitates real-time information on products (traceability) at any point across the supply chain, e.g., their location, transportation conditions, and the logistics and manufacturing processes they’ve been through.
• Greater control over supply chain information: prescriptive analytics paves the way for real-time inventory management, enabling organizations to issue instant supply orders and accurately track their goods, among other advantages.

The McKinsey publication A more resilient supply chain from optimized operations planning explains how prescriptive analytics can benefit companies: “Optimization in operations planning involves determining the optimal choices for a set of decisions in a given business environment and business target. This type of optimization generally works best with prescriptive models that provide the ideal set of decisions as an output.”

Prescriptive analytics to transform the future

In today’s changing, competitive market, consumer trends can lead to supply chain disruption. Against this backdrop, prescriptive analytics has become an ally, helping companies to stand out as leaders in their markets. In the past, organizations used their know-how and experience to make rough estimates — with varying degrees of success — on sales and the product quantities needed to meet demand.

Nowadays, prescriptive analytics is a technological solution to anticipate future logistics scenarios and make decisions based on data analysis. The article What is prescriptive analytics? 6 examples from the Harvard Business School says: “Prescriptive analytics has been called ‘the future of data analytics,’ and for good reason. This type of analysis goes beyond explanations and predictions to recommend the best course of action moving forward.”