Bridging the Organizational Gap in Pursuit of an Augmented Factory

Machine learning models and AI-powered systems become possible when the organization collaborates.

The Organizational Gap

By the time a manufacturer reaches advanced levels of analytics maturity, it is already enjoying better operational health. Many improvements have been made throughout the plant, and not just to the performance of production. Data silos have been unlocked, engineers have insights on operations, and the organization sees the value of using more types of data.

There’s still more to be gained. Leveraging artificial intelligence, machine learning, and other data science techniques opens more opportunities to use data for operational improvements. Along a Digitalization Journey, companies that have reached this level are in the Augmented Factory phase. It includes the use of advanced industrial analytics software, real-time monitoring, and predictive insights. Manufacturers can expect significant business value from their performance-improvement projects.

But before a company can reach the Augmented Factory phase, they encounter another gap. This gap within the organization must be closed to reach the next level of analytics maturity. In this blog, we will explore how to bridge the “Organizational Gap.”

What Is the Organizational Gap?

As factories begin to use advanced data techniques, they also discover that they must work with other departments throughout the organization. Many people are used to working independently or with other members of their own domains, but they are not used to working across the organization. This creates a gap not only between the departments but also with the ability to complete projects that require collaboration.

Some of the reasons for an Organizational Gap include:

  • A lack of clear direction. When leadership does not provide clear strategic objectives, it can lead to confusion and inconsistent efforts across different teams.
  • Failure to prioritize projects. Some projects have high value that is not always understood. As a result, they do not get the priority they should and might even become missed opportunities.
  • Misalignment with other goals. Projects created in silos are isolated from broader business goals, which could put them in conflict with the rest of the organization. Because the organization also might see these projects as wasted resources, misalignment could jeopardize the funding and support for other projects.
  • Inability to adapt. Rapid technological advancements and shifts in market conditions require flexibility. Difficulties with changes can hinder an organization’s ability to stay competitive and responsive to new opportunities.

Strategies to Help Close the Organizational Gap

To close the Organizational Gap, companies should remain as agile as possible. Agility encourages continuous improvement. Moreover, agile working practices, such as continuous learning and experimentation, are important for achieving higher levels of analytics maturity.

Another consideration is the type of organizational model that the company will use. As shown in the figure below, “Organizational Models for Digitalization,” there are generally three types of models that an organization might use for data and related projects. In a completely decentralized model, business units develop analytics projects independently. In the opposite, a completely centralized model, all improvement projects are developed by a central team. The models developed by the central team also requires skills in statistics and data science tools that are not available to local sites.

Therefore, a blended organizational model works best. In this approach, the local sites are responsible for continuous improvement. However, members of central teams serve as facilitators. They set direction of data-driven projects and share best practices without taking full control. This way, engineers at local sites can make their own improvements while staying aligned with the company’s overall goals.

Organizational Model

The blended approach facilitates collaboration and allows engineers at local sites to address their specific needs. However, it also requires support from the entire organization. Companies need to develop an internal strategy that includes common goals. Then the organization must set clear guidelines with best practice approaches for the development of data-driven projects. This way, the projects are created more efficiently and effectively and are more likely to receive stakeholder support for continuous improvement.

Benefits of Closing the Gap

There are many benefits to closing the Organizational Gap. Among them, the entire organization can do more with operational data. This includes the development of machine learning models and using other systems powered by AI. Manufacturers that use an advanced industrial analytics platform also find that they are already well on their way to closing the Organizational Gap and reaching a high level of analytics maturity.

For example, engineers at Bayer Crop Science achieved a 10% increase in production capacity. When the starting material for a process turned into a sticky mass during the early stages of production, they used TrendMiner to provide insights that led them to find the root cause. Using a combination of time-series and contextual data, they were then able to resolve the issue with the starting material.

Meanwhile, Clariant data scientists have used TrendMiner to create machine learning models. They have helped the chemical company achieve a 10% decrease in batch cycle time and a 9% decrease in energy consumption. Engineers used the platform to gather relevant insights about production. Then, data scientists used TrendMiner’s Python notebook environment to develop models from that data.


There are still many more opportunities on the road ahead. While an Augmented Factory is the end of Industry 4.0, it is just the beginning of a Digitalization Journey. Companies Augmented Factories can see the future of Industry 5.0. With the help of TrendMiner and advanced industrial analytics, they are also prepared for what comes next.

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