Data estate modernization holds the key to smarter and more sustainable chemical processing
Process manufacturers have spent years and millions of dollars revamping factories and scaling smart plants. These enterprises recognize that intelligent solutions and connected operations — from the factory floor to the back office — will help reduce costs, improve efficiencies and sharpen their competitive edge.
Chemical processors are no exception. Like many process manufacturers, chemical processors are also implementing digital capabilities, such as IoT sensors, robotics and digital twins, to enable smarter, safer and more sustainable processes. However, the catalysts behind chemical processors’ transformation initiatives slightly vary from others in the broader industrial manufacturing sector. Historically, energy-intensive processes, asset-heavy operations and high costs of safety incidents have driven the chemicals industry to adopt digital technologies. More recently, the industry is contending with increased energy costs, supply chain disruptions and worsening talent crunch with limited intake of new talent.
The resulting emphasis on digital transformation and smart manufacturing has exposed a common gap for chemical processors — effective use of operational data. Many companies do not have a strategic path to data estate modernization. Digital transformation requires data estates with secure and efficient collection, selective storage and cost-effective management of data in cloud or hybrid environments. Thus, chemical processors must first modernize their data estates before they can truly make headway with their smart manufacturing plans.
Identifying barriers
To begin solving this issue, it is important to explore the various interrelated challenges behind data estate modernization for many chemicals enterprises. The most common underlying issue stems from the heavy legacy burden in chemical processing. Years of mergers and acquisition with limited integration means operations are littered with variability in processes, equipment, instrumentation and site IT infrastructure. This variability makes uniform capture of operational insights difficult and cost prohibitive. Organizations struggle to collect relevant data from older machines, transport data into cloud or hybrid environments, and weave the data into homogenous analytical insights.
Another complicating barrier for chemical processors is the variety in operational sites in terms of size, complexity, tenancy (owner/tenant), geographical location and network connectivity. This makes collection, management and utilization of data for intelligent operations very difficult. Cloud technologies sometimes help overcome this barrier, but it is difficult to justify the required localized investment without a consistent strategy.
Lastly, data science talent and skill gaps pose considerable barriers. Executing smart manufacturing strategies involves modeling complex systems that are most effectively modeled as data models instead of mathematical and physics-based models. This requires data scientists who understand not only state-of-the-art data science technologies, but also complex manufacturing equipment as well as the system context in which they operate and the institutional knowledge crucial for extracting insights. Talent with such skillset combination is hard to find (and retain) and requires years to cultivate internally.
Addressing challenges
Digital transformation and increasing adoption of smart manufacturing is now an established trend in the chemicals industry. With visibility and support all the way to board-of-directors level, the barriers to investments in technology projects are now lower than they have ever been. The environment is right to implement supporting IT improvements, starting with a broad-based data estate modernization.
There are many approaches chemical companies may take based on their context, but there are few underlying tenets that can inform data estate modernization strategies to support digital transformation of chemical processing operations.
- User-centric and process-driven digital transformation: IT departments should be relentlessly focused on consumers of data in their approach to digital transformation. Use cases centered around improving user experience in day-to-day operations add the most value in a digital solution. For example, connected worker use cases that allow maintenance notifications and updates to critical master data from the field not only improve user experience, but also the quality of data available for operations. Similarly, data models should be designed to span broad, end-to-end processes to deliver the benefits of digital transformation. For example, asset performance improvement requires an ability for end-to-end performance analytics that can be delivered with integrated, centralized cloud data repositories.
- Holistic data architecture: When envisioning the data estate architecture, companies should account for how data travels throughout the organization, and minimize data stranded in disconnected assets. This consideration includes internal systems for ERP, cybersecurity, engineering, plant operations, organizational knowledge management and collaboration, etc. Roadmaps for storage, computing and network infrastructure are also essential. External consideration includes supply chain partners as well as technology providers and their roadmaps.
- Standardization: Process standardization is often overlooked in data architecture. Data strategies should be implemented in conjunction with the adoption of industry standard processes. This addresses the inherent barrier of variety across legacy operations of chemical processors and improves the ability for collaboration with industry peers.
An overarching success factor in data estate modernization is that it needed to be an organization-wide imperative. To realize the inherent benefits of business growth, reduction in operational and product cost, increased operational performance and sustainability, data should be not only the business of IT departments, but of all operational units and departments. This organization-wide data centricity requires consistent, value-centric messaging from executive leadership.
The good news is that data estate modernization, and the digital transformation it enables, addresses long-standing challenges for the chemicals industry. It helps to address the talent crunch by enhancing virtual collaboration with experts across a company, while also improving the industry’s image to attract new talent. The nimble and agile operations make possible sustainability initiatives that tend to have smaller payoffs over longer time horizons. However, to see the benefits, chemical processing leaders must accelerate their data estate modernization strategies.
Vivek Sandell is Principal & Chemicals Segment Leader at Capgemini Americas.
