Purshottam Purswani

The author is the Chief Architect Business Technology Innovation Centre at Atos in India

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Optimising Real-Time Prescriptive Analytics for Instantaneous Outcomes

Prescriptive Analytics can help combine all isolated business functions/processes with the help of analytical business models and optimization techniques to enable meaningful and feasible future decisions by solving different scenarios to select the best possible outcome.

With the mainstream adoption of Big Data analytics, organizations are able to benefit by running advanced analytical algorithms on their huge volumes of structured and unstructured data. Today, organizations are leveraging analytics in all business processes including HR, Finance, Customer Service, Marketing and others to improve decision making with minimal risk and maximized benefits. CXOs are now empowered with significant insights to be able to answer the following questions more efficiently and scientifically without involving personal judgement, sentiments or opinion –

a. Understand what went wrong by analysing real-time and historical data (Descriptive Analytics),

b. What could go wrong by analysing and building co-relation on past data patterns and trends (Predictive Analytics),

c. What to do, so that nothing could go wrong and suggesting a course of action to achieve this by running scenario analysis combined with multiple optimisation techniques (Prescriptive Analytics).

Descriptive and Predictive analytics have matured over the years and have seamless adoption in almost all industry sectors for achieving business goals and objectives linked to cost, revenue, customer satisfaction and competitive edge, etc. The biggest benefactors from Predictive Analytics are e-commerce, retail and all other customer service oriented sectors. Organizations such as Amazon, Walmart, Flipkart are using predictive analytics to identify their customer behaviour, purchase patterns, and forecasting inventory level amongst other things. Market trends show that more than 90% of organizations are using Descriptive Analytics and approximately 30% are using Predictive Analytics.

“Prescriptive Analytics” is relatively complex in nature considering the amount of computational power and data set required to execute tasks related to simulations, machine learning, Artificial Intelligence, mathematical models, business process, configurations and optimizations (stochastic) techniques in real-time, and to translate these forecasts into actionable plans and ways to implement them. Penetration of Prescriptive Analytics into mainstream business is just 5-10%, the main reason being due to the complexities involved.

However, prescriptive analytics has the potential to transform an organisation’s way of working in the areas of fraud prevention, cyber-security, supply chain optimization and resource optimization at the click of a button. As Gartner estimates, Prescriptive Analytics market has the potential to reach $1.1 billion by the year 2019. Prescriptive analytics runs on the nexus of business processes, analytical decision models, data and optimization to predict best possible achievable outcome depending on the computational scenarios.

Prescriptive Analytics are best suited for the following scenarios:

1. Decisions or planning made in an isolated environment

Sales and Operation Planning (S&OP) is a critical process in almost every organisation, but is often driven by the isolated decision making in the areas of marketing, sales, procurement, finance etc.

Prescriptive Analytics can help combine all isolated business functions/processes with the help of analytical business models and optimization techniques to enable meaningful and feasible future decisions by solving different scenarios to select the best possible outcome.

2. When organisations have a highly dynamic operating environment and experimenting is too costly or risky

Industry sectors like finance, oil and energy, and healthcare are some of the best sectors which operate under highly dynamic operational environments and are driven by complex decision-making.

Here, Prescriptive Analytics can help combine parameters like government regulations, oil price, constraints, patient records and medical information from the highly dynamic operating environment and business process and rules to build and execute optimised analytical models to achieve the best possible outcome. Here, future opportunities could be understood with mitigated future risk as predictions are continuously updated with new sets of data.

3. Too many data sets, variables, options to analyse

The aviation sector offers perishable services and therefore relies on the accurate demand and supply prediction on multiple factors like, demographics, demand level, supply level, regulations, etc. to determine real-time ticket prices, customised services to valuable customers and route optimisation.

Here, Prescriptive analytics can be employed to analyse all the influential variables, factors and constraints with the help of optimization techniques and analytical models to predict the best possible outcome such as best ticket fare and suggesting the shortest route.

Prescriptive analytics can be optimised by having a clear vision on the following parameters:

1. Objectives – Organisations need to have clear and optimised business objectives (e.g. 10% reduction in operation cost within next 5 years)
2. Decisions – Decision set needed to achieve objective (e.g. re-negotiation with vendors for raw material, reduction in workforce, reducing downtime)
3. Constraints – Restrictions, external forces or limits that can influence the decision (e.g. rising material cost, future demand, machinery health)
4. Include broader set of business process
5. Include larger set of business problems
6. Optimised KPIs and “What-if” scenarios

In a nutshell, Prescriptive Analytics is the future of Big Data which can not only help organisations to understand events which happened in the past but also to predict the outcome of possible scenarios based on past data and assist organisations to choose the best course of action to achieve the desired results.

Prescriptive Analytics can greatly assist quick decision-making and puts the emphasis on generating actionable intelligence at speed, enabling immediate response based on insights derived from deep analytics of incoming data streams. Real-Time Prescriptive Analytics and Artificial Intelligence will have a major impact in the automation, optimization and flexibility of connected ecosystems as we head towards 2020. This will particularly be the case in industrial supply chains as they become increasingly collaborative and responsive to market demands.

Disclaimer: The views expressed in the article above are those of the authors' and do not necessarily represent or reflect the views of this publishing house

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