Scaling digital and analytics-enabled improvements in chemicals and agriculture

Productivity in process industries depends on scaling technology and traditional operating disciplines at the same time.

Our experiences over the past few years suggest that most process industry companies—also known as heavy manufacturing—have experimented with digital tools and advanced analytics. Many have reported successful use cases.

Despite this enthusiasm, these digital tools have delivered underwhelming results for many companies’ overall performance: 72 percent of respondents reported that their digital transformation stalled before achieving at-scale impact. A recurring reason for this is companies’ failure to scale digital tools and advanced analytics throughout their networks of production facilities, which are often global in size.

To reap the full benefits of digital and analytics tools, companies in process industries should invest in scaling digital tools and processes throughout their facilities while ensuring that operational improvements can be easily shared and adapted to different contexts. Operational improvements in production processes, maintenance productivity, and reliability are critical to scale globally. These insights are not novel, but they produce outsize results when deployed properly.

The challenges of scaling

Scaling has been a challenge for process industries because different facilities often produce unique combinations of products and serve different regions or markets. This means production requirements are highly variable; as a result, decision makers have historically declined to explore whether advances at one plant could be applied to others.

This assumption makes it difficult to bring about holistic improvements in productivity. Because individual plants find bespoke solutions, only the projects that can yield the highest single-plant value can justify their costs. These bespoke solutions are tailored to the plants that generate the most value—which, in turn, are the only plants that reap the benefits.

As digital and analytics tools have become more accessible, many decision makers have attempted to make holistic improvements by applying these tools universally. The results are generally disappointing because even versatile tools should be adapted to specific needs. Companies’ progress can also stall because of challenges in building their digital capabilities, establishing agile ways of working, and managing change.

Scaling new technology throughout global networks

Committing to scaling digital and analytics tools requires an organization-wide commitment to sharing advancements and the combinations of lean and digital technologies they find useful.

Making scalable solutions possible

Creating scalable solutions requires a culture and infrastructure that facilitates the sharing of knowledge and tools throughout a global network. This necessarily represents a shift from a plant-level or even regional perspective to a network-level perspective.

Creating scalable solutions requires a culture and infrastructure that facilitates the sharing of knowledge and tools throughout a global network.

Technology tools are foundational in supporting this approach to creating scalable solutions. Critically, company leaders should make centralized decisions to invest in shared technological tools and infrastructure; consolidate their data in the infrastructure so entries are unique, documented, and accessible to everyone in the network; and help users adopt those tools and approaches.

Companies do not have to undergo a transformation to create this infrastructure. Incrementally adopting infrastructure elements and tools is often enough to make progress. The key is to move away from bespoke, site-specific tools and solutions and toward a portfolio of shared tools for the entire enterprise that can be used in different combinations as needed in specific local efforts.

In other words, decision makers implementing changes should ensure that the elements of their initiatives can be used, translated, and implemented at other facilities in the network. The ways to make this mindset shift may vary. Top-down investments, encouragement, and positive reinforcement may be appropriate before sharing knowledge, possibly in a centralized resource, becomes the default.

Optimizing production processes

When properly implemented at scale, tech-enabled operations can simultaneously optimize yield, energy, throughput, and quality. We estimate that companies in process industries could capture an extra 20 to 30 percent in throughput, 2 to 5 percent in yield, and 5 to 10 percent in energy costs in batch processes—in addition to progress from previous operational improvement initiatives.

Sidebar

The most productive plants should lead the way in sharing their knowledge and insights and should contribute disproportionately to companies’ pool of digital and analytics knowledge. Because digital and analytics tools are not one-size-fits-all, especially in process industries, teams across plants should be trained to identify the challenges and needs of their sites, to know what tools exist, and to understand how those tools can be configured to address similar problems across sites.

To be sure, digital and analytics solutions should be combined with nondigital tools. Simplicity is often ideal, and the best combination of tools is not necessarily the most technologically advanced. In fact, simpler, less technologically advanced tools tend to be less costly to implement and can be easier to scale. (For an example, see sidebar, “Generating more value from a production line.”)

Increasing maintenance productivity and reliability

Besides combining digital and analytics tools with nondigital methods to significantly streamline their work, companies in process industries use advanced tools to improve reliability and equipment availability. We estimate that companies could reduce equipment downtime by more than 20 percent by using advanced tools.

To increase equipment’s productive time and manage costs, analytics and digital tools can help decision makers identify the causes of reliability issues, shorten the duration and costs of outages, reduce the frequency of plant turnarounds, and predict and mitigate failures. Predictive maintenance techniques (such as using data to predict when equipment failures are likely) are promising. However, most companies’ approaches are relatively basic, including sensors that sound the alarm when equipment exceeds preset vibration and temperature thresholds.

An approach that could have a more holistic impact would be to focus on root-cause analysis using networkwide data harvested from a common set of tools so data is comparable across sites. Companies in process industries often focus on maintenance challenges at individual sites, but centralized maintenance data from a shared data pool is the best way to make data-driven insights accessible. In this way, scale can help identify the causes of problems and help decision makers spread the solutions throughout a network. In fact, companies outside of process industries successfully deploying these approaches have achieved additional EBITDA profitability of more than 10 percent and 20 to 30 percent higher ROA.

Although digital tools can provide significant value, companies should avoid transplanting their previous process to the digital environment by default. They should instead redesign their maintenance processes to take advantage of new technology and train and motivate all relevant users on maintenance and operations teams to adopt new tools.


The challenges of scaling have held process industries back from enjoying the full benefits of digital and analytics tools. Combining digital and analytics solutions with traditional operating discipline can be a fruitful way to start.

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