5 Lesser Known Facts About Advanced Analytics

5 Lesser Known Facts About Advanced Analytics

5 Lesser Known Facts About Advanced AnalyticsWhat happens when you’ve been doing the same job for many years? Even if you began your work with little or no expertise in your field—let’s say supply chain management—no doubt by the time you’ve spent a few years performing the same or a similar set of tasks you gain new skills and improve the ones that you already have. Because you understand the supply chain better than you did when you were more junior, you’re better able to predict how it will react to different disruptions, and you can more quickly and more easily make snap decisions to preserve your supply chain’s agility and maintain optimal performance and on-time deliveries. In short, you learn, and by learning you make relevant production and shipping processes run that much more smoothly, more efficiently, and more profitably.


The ability of planners within an organization to learn and grow their skills is crucial to the success of any manufacturing business, but it’s no longer enough for humans to grow and develop for a company to thrive. These days, it's equally crucial that your technology do the same thing, becoming smarter and more reactive as it spends more time interacting with various data and systems. In the era of Industry 4.0, this degree of technological self-improvement is made possible by advanced analytics. Here are some lesser known facts about it: 

1. Advanced analytics can be divided into two categories

Advanced analytics can act as a significant driver of added value cross-operationally by suggesting improvements to supply chain processes and taking on some autonomous decision-making for smaller tasks like restocking order and scheduling transportation. What many don’t realize, however, is that there are two distinct categories of analytics that businesses can leverage: 

  • Predictive analytics gather existing data and real-time information about pricing, demand, and even traffic reports in order to create more accurate forecasts. With these predictions made available across all touch-points on a company’s value chain, planners can take a more proactive approach while having to navigate fewer unexpected bottlenecks and disruptions.
  • Prescriptive analytics, on the other hand, can be trained on data sets pertaining to specific business processes in order to yield specific, actionable insights into various value streams. This type of analytics can help to uncover existing areas of waste and inefficiency and recommend potential changes for optimizing the relevant processes.


2. It’s an important part of Industry 4.0

Businesses have always used analytics to determine the effectiveness of their operations, so what makes advanced analytics so special? Simply put, modern big data analytics are the first ones to integrate with new Industry 4.0 workflows and environments. As Industry 4.0 adoption leads to the rise of the smart factory, connecting all touchpoints throughout the supply chain into adaptive, interoperable systems and processes, new metrics and methods of data integration are a must. Without these more modern analytics frameworks, manufacturers run the risk of implementing a multitude of organizational changes, or changes to production processes, without being able to evaluate their effectiveness. The result is that the boosts in efficiency and visibility that should be staples of Industry 4.0 systems are under-utilized, causing businesses to miss out on crucial opportunities to add value and gain a competitive advantage.  

3. It’s related to machine learning

As alluded to above, modern big data analytics enables production and transport processes to learn and grow over time, even sometimes learning to make small decisions without human intervention. Part of what makes this degree of autonomous improvement possible is integration with machine learning technology. Machine learning is a term that generates a lot of hype, but at heart it’s a process whereby various algorithms are trained on large quantities of data in order to find relationships between different factors that would otherwise be difficult to discern. In manufacturing, this could lead to the revelation that unreliable inventory restocking is consistently leading planners to overestimate demand, or that machine or fleet maintenance is being scheduled too frequently. In many cases, modern analytics will rely on precisely this technology to create improved predictions and suggestions for operational improvements.

4. It can increase speed to market

For many, analytics are about determining the success of a given initiative once it’s already been rolled out, but in the era of Industry 4.0, analytics can actually increase speed to market by enabling the entire supply chain to predict and respond to issues more quickly and more directly. The relevant factor here is that supply chain planners can avoid the slowdowns and disruptions that typically crop up in any variant rich production process, while gaining a better sense of how to replicate past successes. Essentially, big data analytics can drive intelligent planning processes that add value at through all supply stream touchpoints. 

5. Advanced analytics promotes visibility

We’ve spoken about prescriptive and predictive analytics, but what many don’t realize is that big data analytics solutions can also improve the accessibility of data cross-operationally by automatically generating data visualizations that can be understood by non-technical decision-makers. The result is that new insights and predictions are made available not just to data scientists who are able to unravel complex outputs, but to anyone with a basic understanding of a given company’s operations, making suggested changes much easier to understand and implement. Many manufacturers currently lack the supply chain visibility to truly take advantage of Industry 4.0, but with big data analytics they can work toward real-time integration, end-to-end (E2E) visibility, and other crucial value added propositions.  

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