We live in an age dominated by digital content. The volume of data which modern enterprises have to process, interpret, and reconfigure on a regular basis is nothing short of massive. With the internet of things (IoT) growing more and more prevalent, the amount of data is rapidly increasing, particularly when it comes to supply chain systems.
This is why many businesses are turning to business intelligence such as predictive, descriptive, and prescriptive analytics. These types of analytics provide more efficient ways to extract value from operational information. Through data analysis, they support decision making, streamline customer communications, and can even boost revenue.
In fact, substantial ROIs are a major reward for manufacturers who invest in clever ways of optimizing their supply chains. However, in order to do this, you need business intelligence software that can process large amounts of historical data and provide insights on future sales trends.
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Getting to Know Your Business
This is an integral part of optimizing S&OP (sales operation and planning) strategies. After all, the only way to ensure that manufacturing levels are profitable is to make logical, informed predictions about demand. In short, you need to create a data-driven culture.
However, it isn’t always easy to find the right analytics tools. There are a lot of choices out there, and considering all of the different options, it can be an intimidating process. For small businesses, the recommendation is to split the market into its three main product types.
These are predictive, descriptive, and prescriptive analytics. The first thing to understand is that, while they can be used in isolation, the best results come with a cohesive merger of all three. When applied correctly, they have the power not just to cooperate, but also to diversify your data analysis.
Why the Big Picture Is Important
In fact, if you want your business to have a holistic perspective on the market and its place within it, a watertight analytic setup is essential. It helps businesses shrink operating costs, increase sales, expand their product range, and bring them closer to their customers.
The simplest way to look at predictive, descriptive, and prescriptive analytics is to think of them as solutions to three key questions.
Descriptive analytics ask about the past. They want to know what has been happening to the business and how this is likely to affect future sales. Predictive analytics, on the other hand, ask about the future. These are concerned with what outcomes can happen and what outcomes are most likely. Finally, prescriptive tools ask about the present. It wants to know the best course of action for right now. In other words, they’re the decision makers.
The Goals and Gains of Analytics
When you look at analytics in this way, it becomes easier to understand why they’re most valuable when implemented as a unified system. When isolated, the narrative is incomplete. You miss out on the insights needed to improve decision-making.
In the next section, we’ll talk a little more about the distinctions between the three and why they’re important. Analytics tools don’t just ask their own questions; they use different data extraction techniques to find the answers.
Descriptive Analytics Look Into the Past
The clue is in the name when it comes to descriptive analytics. They process large amounts of data and reconfigure it into easily-interpretable forms. This information could be made up of any statistic, event, trend, or specific timeframe from your manufacturing past.
The aim is, of course, to learn from the past. One common example is analyzing seasonal purchasing trends to determine the best time to launch a new product. As consumers are creatures of habit, looking at historical data is an effective way to predict their responses.
Descriptive statistics can demonstrate everything from total stock inventory to the progress of sales figures over the course of several years. They can show the typical spend amount of customers and whether this sum is likely to increase at certain times.
Predictive Analytics See into the Future
Predictive and descriptive analytics have oppositional objectives. However, they’re very closely related. This is because you need accurate information about the past to make predictions for the future. Predictive tools attempt to fill in gaps in the available data.
They take historical data from CRM, POS, HR, and ERP systems and use it to highlight patterns. Then, algorithms, statistical models and machine learning are employed to capture the correlations between targeted data sets.
The most common commercial example is a credit score. Banks uses historical information to predict whether or not a candidate is likely to keep up with payments. It works in much the same way for manufacturers, except that they’re usually trying to find out if products will sell.
Prescriptive Analytics Find the Right Solution
Of predictive, descriptive, and prescriptive analytics, the latter is the most recent addition to the business intelligence landscape. These tools enable companies to view potential decisions and, based on both current and historical data, follow them through to a likely outcome.
Like predictive analytics, prescriptive analytics won’t be right 100% of the time, because they work with estimates. However, they provide the best way of ‘seeing into the future’ and determining the viability of decisions before they’re made.
The difference between the two is that prescriptive analytics offers opinions as to why a particular outcome is likely. They can then offer recommendations based on this information. To achieve this, they use algorithms, machine learning, and computational modeling.
As you can see, analytics solutions work together to build a story. It’s a story about what your business has, is likely to, and could achieve. With this narrative for guidance, you can make decisions that are fully informed by your data.