Prescriptive Analytics

Prescriptive Analytics is the area of data analytics that focuses on finding the best course of action in a scenario given the available data. It’s related to both descriptive analytics and predictive analytics but emphasizes actionable insights instead of data monitoring.

It’s related to both descriptive analytics and predictive analytics but emphasizes actionable insights instead of data monitoring. Whereas descriptive analytics offers BI insights into what has happened, and predictive analytics focuses on forecasting possible outcomes, prescriptive analytics aims to find the best solution given a variety of choices. Additionally, the field also empowers companies to make decisions based on optimizing the result of future events or risks, and provides a model to study them.

Prescriptive Analytics is the area of data analytics that focuses on finding the best course of action in a scenario given the available data. It’s related to both descriptive analytics and predictive analytics but emphasizes actionable insights instead of data monitoring. Whereas descriptive analytics offers BI insights into what has happened, and predictive analytics focuses on forecasting possible outcomes, prescriptive analytics aims to find the best solution given a variety of choices. Additionally, the field also empowers companies to make decisions based on optimizing the result of future events or risks, and provides a model to study them. big data basics Prescriptive analytics gathers data from a variety of both descriptive and predictive sources for its models and applies them to the process of decision-making. This includes combining existing conditions and possible decisions to determine how each would impact the future. Moreover, it can measure the impact of a decision based on different possible future scenarios. The field borrows heavily from mathematics and computer science, using a variety of statistical methods to create and re-create possible decision patterns that could affect an organization in different ways.

SCM and Prescriptive Analytics

For supply chain planning processes that need recommendations for more efficient and data-based decision-making, prescriptive analytics is recommended. It can employ techniques like AI and machine learning. An example is a business that wants advice on a possible outcome and what suggested actions they should take.

To understand why prescriptive analytics is recommended for supply chain planning, it helps to understand how businesses use analytics for processing data.

Example – Cummins

One way to use prescriptive analytics is with mobile diagnostics to improve the customer experience and determine future spending patterns. Cummins is a Fortune 500 company that designs and manufactures alternative fuel engines, diesel fuel engines, and power generation products.

Cummins uses automation techniques gathered from data points to up-sell and cross-sell services. Technicians alert customers remotely if they are due for oil changes or need filters replaced and get their responses in real-time.

The data gathered from consumer spending habits and personalization opportunities gives the business clear insight into targeted sales approaches for influencing customer purchasing decisions and future profitability.

Implementing Prescriptive Analytics

Implementing prescriptive analytics technology is not a major endeavor supply chain leaders should fear. It’s actually a crucial analytics approach that can help supply chains decrease disruptions, improve efficiency, and drive profitability for the business as a whole —which, together, could land supply chain managers that well-deserved seat in the boardroom. Below are four areas where prescriptive analytics can and has helped truly transform supply chains.

Correlating supply chain output to the financials of the business is one of the most impactful methods supply chain managers can employ. The problem with existing tools and infrastructure is that they don’t allow visibility between the supply chain and the finance department, making it difficult to accurately depict the supply chain’s impact on the bottom line.

Adding advanced analytics to supply chain reporting is the only analytics method that allows management to consider different financial factors, such as the variable rate of return for cash flow. Once these aspects are considered and identified, you will be better equipped to develop a solid supply chain management plan that is also financially valuable.

Agile and Accurate in Making Decisions – The dynamic environment of business requires a lot of decision-making done, more often than not, in a short time frame. Each decision has implications in terms of how customer demands are met, how factories are run, how employees get paid, and so on. While legacy supply chain systems have their place and are essential to running the company, they don’t really give managers the ability to make better, more informed decisions. Most of these tools focus on the operational aspects of the supply chain, rather than the value and impact of the operation.

In order to make informed decisions, supply chain managers need to account for constraints that reflect reality. Advanced capabilities like prescriptive analytics provide a platform to make decisions more fact-based. Supply chain managers will better understand the implications of decisions by running different scenarios, ultimately settling on the outcome that best impacts the entire company. This process is extremely hard to do unless you have an integrated model and an advanced platform that allows for certain constraints and scenarios.

End-to-End Visibility – End-to-end supply chain visibility solutions continue to increase as companies adopt more robust advanced analytics platforms. As a natural progression of implementing prescriptive analytics and considering the impact of decisions, integration and collaboration increase, silos are eliminated, and supply chain managers start to adopt an outside-in way of thinking. Enhanced end-to-end visibility leads to reduced costs, fewer risks, more accurate forecasting, and the agility to face changing market demands and unexpected disruptions.

Not only does advanced analytics benefit the entire company, but it also betters the career of supply chain staff. Employees become more empowered as they make decisions that are of global impact to the company. They become more efficient as they rely on a digital assistant of sorts to help them make more informed decisions. Supply chain managers play a more strategic role as they collaborate across the organization.

While many companies are making great strides in implementing prescriptive analytics in their supply chain operations, there is still a long way to go. The market is ripe for the next round of supply chain technologies, and businesses will only continue to improve efficiency, increase savings, and improve the lives of their supply chain staff as they take a more strategic approach to their data.