5 Differences Between Enterprise Manufacturing and Business Intelligence

iBASEtblog Manufacturing Quality5 Differences Between Enterprise Manufacturing and Business Intelligence

Apr

6

5 Differences Between Enterprise Manufacturing and Business Intelligence


5 Differences Between Manufacturing and Business Intelligence
As I pointed out in a previous article, “Manufacturing Intelligence is More Than Just Charts and Gauges, I provided a brief history of enterprise manufacturing intelligence. I explained how it has evolved, what new market entrants now exist, and what the following generation product has become. A common theme is the value of quickly providing visibility of contextualized data valuable insights for real-time decision support. This article will explain how Manufacturing Intelligence (MI) is different from Business Intelligence (BI) and how a combined approach might just offer the best of both worlds. 

Same Question, Different Answers

What if you could see the future? It would make your business invincible. Your top executives could make perfectly timed decisions, and the shop floor would never be disrupted. Supply chains would stay flowing at total, perfect capacity.

While neither MI nor BI is a crystal ball, they both can get you closer to predicting the future of your business. But they do it in different ways because they are designed for various purposes. 

Here are five ways Manufacturing Intelligence differs from BI.

  1. General vs. Specialized Intelligence

Business Intelligence is, by definition, a general-purpose tool. It can be very good at comparing data points and providing a picture of your business at a high level. It has KPIs that are general, such as cost per unit, time to manufacture, and percent of quality rejects. 

As its name suggests, Manufacturing Intelligence is explicitly designed for understanding the performance of specific manufacturing processes. It can lead to a greater understanding of operations at a deeper level. An MI will likely come with a rich set of manufacturing-specific KPIs, like SKUs related to field repairs, correlations between material and production time, the impact of production alternatives on plant equipment performance, and so on.

  1. Data Aggregation

Collecting manufacturing data requires a strong understanding of where the data is, how it must be cleaned, and how to gain access to it. Some BI tools do a good job of interfacing with MES data sources. Still, Manufacturing Intelligence products offer far more out-of-the-box capabilities for data aggregation, particularly if they’re an integrated extension of MES. An effective MI can gather data from all different moving parts of a manufacturing enterprise, from the minor details to significant events.

  1. Contextualized Data

Context is everything in manufacturing. Business Intelligence applications are designed to provide intelligence on general business issues common to most enterprises and industries, such as the cost of parts, profit-per-sale, and revenue-by-region. For that kind of intelligence, not a lot of context is required. The manufacturing floor is another world entirely. Complex manufacturing is an intricate orchestration of workers, parts, robotics, materials, processes, tests, record-keeping, maintenance, and so on. Any shortcoming or problem in one area can ripple across the factory.

Said differently, a BI application might tell you that your productivity and profits are down at a particular factory. An MI application can tell you why. For instance, it might reveal that local material issues are impacting production processes, which in turn are causing more frequent equipment breakdowns. 

  1. Manufacturing Analytics

Manufacturing data is a vital resource, but only if you have the right analytical tools to use it. Continuing with the above example, the connection between material, processes, and equipment repair might never be spotted by a human expert or a BI application, no matter how many spreadsheets, charts, or graphs are available for analysis. There are simply too many moving parts.

The latest MI generation combines a contextual understanding of enterprise manufacturing intelligence, often incorporating AI and machine learning, turning data into a goldmine of insights. A powerful MI analytics engine might go deeper into our problem and discover that the issue is specific to certain worker shifts, equipment performance, or a calibration problem on the line, or any number of other related things. 

  1. Timely Insights & Actionable Intelligence

Most enterprise BI applications can take data from a Manufacturing Execution System (MES) or other systems and provide meaningful and valuable decision-makers metrics. The power of BI is more in being able to give a broader perspective on general business conditions. For this type of analysis, real-time visibility is not as much of a requirement. Alternatively, as the example we’ve been following illustrates, an MI application can generate much faster insights providing operators with far more targeted actions that can be instantly taken. The more complex the manufacturing process, the more accurate this is.

What’s the Right Answer?

What should be clear by now is that enterprise Manufacturing Intelligence is one based on the specialty of purpose and usability in a production environment. In situations where your production operations are relatively stable and straightforward, BI might be your best choice. MI is your better choice in other cases with a greater scope and complexity of complex manufacturing operations. 

One thing that is for sure is that the data collected and intelligence shared from an MI application can always extend the value of an existing BI, providing more significant and more detailed visibility of operations to other department management teams or the executive suite. With the correct MI application, you can learn what happened, why it happened, and then share this intelligence with others to provide broader decision support impacting issues across your company. Once you have this level of insight, you can start to prevent problems before they happen at a broader scale, making better decisions to prepare for what’s coming next.

You’ll never indeed be able to see the future. But with the right technology, like with a combined Business and Manufacturing Intelligence solution, you can react with such speed and accuracy that people might wonder if you can.

New Call-to-action

About Paula Jimenez

Paula Jimenez is a product manager and technologist with more than 20 years of enterprise systems experience. At iBASEt, she plays a crucial role in managing the integration of the company's products with various PLM, ERP, and other enterprise systems.

View All Posts

Add Comment

Your email address will not be published. Required fields are marked *

This site uses Akismet to reduce spam. Learn how your comment data is processed.