A Digitalization Journey Starts by Embracing Operational Data
Overcoming the Mindset Gap is the first challenge on the path toward an Augmented Factory.
In the process manufacturing industry, the Digitalization Journey is a transitional shift from passively collecting and storing data to using it for more efficient and agile operations. This journey can be quite rewarding. People learn to embrace advancements in technology, such as modern historians and advanced industrial analytics software platforms. Meanwhile, as manufactures add technology, their level of analytics maturity also grows.
Companies also encounter challenges that must be addressed to ensure a successful journey. In this first of a series of blogs about the Digitalization Journey, we will explore how manufacturers move from the Automated Factory to the Data-Driven Factory phase. During this leg of the journey, companies begin to introduce technology that empowers operational experts to make data-driven decisions. It also will explore ways companies can overcome their first challenge: The Mindset Gap.
The Automated Factory
New factories are delivered as Automated Factories, and most companies have been in this phase for years. Software, such as Supervisory Control and Data Acquisition (SCADA) systems, help control complex production processes. SCADA systems were designed to collect data and monitor processes. Eventually, historians were added to SCADA systems to store the enormous amount of data they were collecting. They originally were used to fulfill regulatory requirements but have proven to be more valuable. Manufacturers began to realize that the industrial data in their historians could provide information on process behavior. However, accessing and using the data was very difficult.
Factories began using Manufacturing Execution Systems (MES) in the early 1990s to bridge the gap between SCADA systems and Enterprise Resource Planning (ERP) software. They also promised to provide analytics, such as KPI data, to improve plant operations. These systems have been able to provide advanced capabilities, but they are expensive and sometimes require extensive engineering.
Instead, operational experts began using spreadsheets to search through sensor-generated data. This helps them find the root cause analysis of some issues. However, spreadsheets are not designed to analyze large data sets. Files also become large and hard to manage, so there’s a greater risk of human error. For the 2-5% most critical systems, operational experts plot trend lines on paper and overlay them against a window to analyze timeframes with similar behavior. However, this requires multidisciplinary teams, so it can take months before engineers have actionable information.
These solutions have proven to be impractical for getting insights quickly. Companies began to realize that they needed a user-friendly solution that provided engineers with actionable insights. The introduction of an advanced industrial analytics platform provided this solution, but operational experts have not always been quick to adopt it.
The Mindset Gap
Digitalization can be overwhelming, and change can be hard to accept. As a result, people might resist technology after many years of doing their job without it. This is the Mindset Gap. Manufacturing companies must overcome it before they can transition from the Automated to Data-Driven factory phases.
Often, organizations find that change management methods are helpful. Business Horizons suggests that companies follow these four recommendations to overcome resistance to change:
1. Enhance communication by improving transparency and dialogue around the reasons for change, the benefits it will bring, and the effect it will have on individuals and teams.
2. Involve employees in the change process to give them a sense of ownership and control, which can reduce resistance and increase buy-in.
3. Provide training and resources to help employees develop the skills they need to adapt to new ways of working and offer support to address concerns and challenges.
4. Ensure that leaders at all levels are fully committed to the change and are demonstrating the attitudes and behaviors they wish to see in their teams.
With leaders on board, the best approach is to set clear goals tied to a long-term vision. Then, start with small projects and learn from successes and failures to grow over time. Selecting the right people to be key users of the technology and coaches who can help is important, as well as establishing a steering committee to oversee the transition from using old to new methods. Working toward quantified results helps demonstrate value from using the new technology.
Arkema Creates Change Agents
Advanced industrial analytics software might not be on the plant floor in an Automated Factory, but its users were there before accelerating their digitalization journeys. Specialty chemical company Arkema, which has moved into the Augmented Factory phase, encountered a Mindset Gap when it decided to become data driven.
Arkema realized that its key software users make the best change agents. Leaders focused their efforts on those users to be drivers of change throughout the company. After creating a list of the software’s superstars, its leaders sent surveys to users.
“I just wanted their feedback on everything that they might need or might want,” said Nina MAS SOLER, Arkema’s Digital Champion at the time. “If you have any issues or if you want some more information, you have the website. You can directly see the use cases from the other users.”
The results were compelling. Based on them, Nina and her colleague created an internal community website that included a “Key User Finder.” This allowed less-experienced software users to locate a more-skilled colleague when they needed assistance.
Toward a Data-Driven Factory
In the next phase of the journey, companies begin to embrace data-driven decision-making. As their level of analytics maturity grows, they also start to want even more information about process behavior. But more challenges await them as they begin to quench the thirst for data. In the next story, we will explore what it means to be a Data-Driven Factory.