Advanced plants successfully deploy the digital and automation strategies, which integrated into a capability master plan, across the site by allocating the projects and actions down to the line operator, engaging the organization bottom-up. For early-stage plants, strategy implementation is still starting.

All advanced plants have access to a centralized Industry 4.0 program office that has clear ownership to drive enterprise-wide systemic changes, share know-how, initiate and monitor pilots, and scale learnings across the enterprise. This is true for half of the early-stage plants.

Most of the advanced plants have clearly defined and directed efforts to cultivate the right leadership behaviors – such as an agile mindset and continuous experimentation with new technologies – while only a few early-stage plants are devoting resources to this cause.

Most advanced plants have a single, fully integrated IT/OT team with clearly identified roles and governance, eliminating silos and simplifying decision-making, while IT and OT work in silos for most early plants.

Advanced plants have access to centers of excellence that combine data science with advanced production process expertise, while this is true for only a minority of early-stage plants.

Most advanced plants are using digital operational excellence tools for capability development to track, plan, and enhance enterprise-wide collaboration, while only a few early-stage plants have access to this solution.

Most advanced plants in CPG have built real-time condition monitoring capabilities and redefined their workflows accordingly, a maintenance approach that none of the early-stage plants are practicing.

Large volumes of data are needed to produce actionable insights and drive value-added maintenance activities. All advanced plants understand that and put effort into data historization, while only the minority of early-stage plants follow this policy.

Most advanced plants have connected their sensors with asset health monitoring platforms, while only a small minority of the early-stage plants are following this practice.

Data lakes are rapidly adopted by advanced plants to provide high data accessibility for data science teams, while early-stage plants are making their first steps in this area.

Early-stage plants are trying to catch up with advanced plants in building established data warehouses to enable structural data gathering.

The majority of advanced plants have created data flows between their transportation management system and their enterprise resource planning software – a practice that is rare among early-stage plants.

Half of the advanced plants are investing in automating workflows by implementing and integrating management systems for warehouse and yard operations. This practice is a reality for only a small minority of the early-stage plants.

Advanced plants have already introduced automated guided vehicles for high-traffic material movements, while the adoption for early-stage plants is still low.

No advanced or early-stage plants are leveraging autonomous mobile robots at scale across their operations.

The majority of advanced plants have defined and constantly updated cybersecurity contingency plans, while this a reality for the minority of the early-stage plants.

Cutting-edge data analytics techniques such as machine learning for cybersecurity incident prevention are a growing trend among advanced plants, while these are completely new for early-stage plants.

Ever-increasing connected assets mainly found in advanced plants and the need for agile decision-making are addressed by the adoption of edge computing. Early-stage plants are not exploring this technology.

Advanced plants are leading the way toward cloud-based key systems of record, such as Enterprise Resource Planning and Product Lifecycle Management, while early-stage plants are lagging.

Although adoption is low for both types of plants, advanced plants have started using industrial IoT platforms to connect data from multiple systems to increase visibility and enhance decision-making.

Despite the potential opportunities offered by digital twins, even advanced plants lack the data readiness and expertise to define real-time models that can drive timely decisions, effective resource allocation, and throughput optimization.

Advanced plants transition quality management to their manufacturing execution system, standardizing quality workflows in a systemic manner by integrating online and offline tasks, while early-stage plants are trying to adapt to this trend.

The majority of the advanced plants leverage advanced machine
vision systems to automate in-line quality tests, while only the minority of early-stage plants are leveraging these capabilities.

None of the early-stage and only a fifth of the advanced plants have adopted edge computing to enable near real-time data analysis for quality purposes.

Advanced plants widely use handheld devices such as smartphones or tablets on the shop floor focusing on data visualization, reporting, and auditing, while early-stage plants are putting in serious effort to adopt them.

A small proportion of advanced plants leverage augmented reality to offer enhanced work assistance to employees, while this technology is not utilized at all by early-stage plants.

Despite the potential of virtual reality for training to improve workforce capabilities while minimizing training time, it is only adopted by the minority of advanced plants.

Advanced plants’ need for flexibility forces them to explore ways to increase the automation of changeover tasks, while this trend is not common for most of the early-stage plants.

A few advanced plants work on automating customization to reduce manual labor, a practice that is not even in concept phase for early-stage plants.

Most advanced plants leverage end-of-line packaging automation, while the majority of early-stage plants do this process manually.