With increasing market competitiveness, industry promotes digital transformation from good-to-have to must-have for productivity, energy efficiency, reliability and safety to ensure operational excellence. There is a tremendous opportunity to usher in workforce effectiveness across business functions, partners and customers that ultimately results in top-quartile performance. The last four decades of automation have witnessed manufacturers adapting to the best technology available from time to time. For the process industry, across all sectors, this has meant the implementation of the distributed control system for the main process and programmable logic controllers for the peripheral islands of automation.
The focus of automation has been solely on increasing human productivity and machine efficiency, a natural progression from the industrial revolution. With hardware design remaining almost a constant across continuous and manufacturing automation, software implementation got associated with industry domain knowledge by word of mouth. Most suppliers of automation thus found their niche in specific industries – refining, oil & gas, metal & mining, food & beverages, etc, and gained leadership positions. Engineers have done a commendable job thus far but supporting tasks such as occupational safety, reliability, energy efficiency, operator rounds, contractor management, etc remained manual.
Over the past few years, newer technologies have become available that automate these tasks to a whole new level, creating a digital operational infrastructure (DOI). Older plants need to be transformed to keep up with their newer counterparts to maintain productivity, and thus profitability. Companies aiming for top-quartile performance are searching for answers to put together a digital transformation roadmap that will take them from their current stage of plant automation to DOI and autonomous systems. Operations and maintenance teams are most demanding of DOI to tell them what they don’t already know, rather than merely displaying a lagging key performance indicator (KPI) on a dashboard they have envisaged.
For example, predict developing problems before outright failure, detect inefficiency and emissions, predict process deviation and detect hazards, thus allowing them to take action in time and avoid downtime and losses. The need of the hour is thus to straddle across operations, maintenance, reliability, safety and other production-related functions to bring the same certainty of closed-loop control provided by control systems to other areas of operations. Existing production process controls are implemented by installing sensors, and field instrumentation to input real-time process data to feed control algorithms which then drive outputs to control valves and pumps, thus achieving optimal performance. This closed-loop performance has an enviable proven track record because of the surety it brings. With cross-functional areas such as reliability, safety and energy, it currently does not have the same luxury of real-time data, let alone algorithms, as transactions are manual and events ad-hoc. Additionally, these processes are often time-consuming and variable due to a lot of manual tasks, siloed databases and systems, not to mention cumbersome procedures. A simple maintenance work order may be fraught with unnecessary delays rather than a foregone conclusion from predictive maintenance software.
As computing power and memory turn into commodities, either on-premise or cloud, we are at the cusp of change that is allowing suppliers such as Emerson to innovate and better support customers across all process industries. Emerson has a unique advantage that comes from first-hand experience of plant floor and control room strategies. Working with managers across multiple operational areas from production, sustainability, reliability, and safety along with enabling domains of system data integration and organisational effectiveness, the company plans a digital transformation roadmap.
Digital plant maturity
A process adopted by most organisations to determine their digital maturity has resulted in several indexes to indicate maturity status. A pre-digital plant has primarily paper-based processes with low level or islands of automation that results in reactive ways of working rather than pre-planned response, as indicated in the figure alongside. Next are digital silos, connected plants, and predictive and adaptive with the ultimate goal of being autonomous. Most companies operate predominantly at Level 2 with some approaching Level 3.
Digital maturity model
How do you choose which digital transformation technologies to bring about change and where to start? Think big, start small and scale fast is my mantra for success as it’s important to take the organisation along in this whole process. Meeting with smaller and multiple successes along the way will make the organisation and leadership believe in transformation. The steps outlined below can serve as a guide:
Determine your maturity level. Digital Maturity Model Quick Index is a free online tool that provides a personalised response based on your answers to the survey and provides an indicator of where you are today.
Determine what operational areas or processes are likely to yield the largest return on investment (ROI) and discover what digital transformation projects will tie to your business KPIs.
Evaluate your operations against others in your industry. It can be further enhanced by working with industry advisors to implement an in-depth strategy and roadmap to implement specific projects you have identified.
The Digital Maturity Model (DMM) web chart can be enlightening and provide a starting point in your digital transformation journey. It will indicate capabilities within identified operational areas, where the organisation is and where it could potentially be, then associate that to business value with making that change. Through this process, we can develop a set of potential investments that you could make that are associated with payback for the business.
The worthy goals for digital transformation include:
Production management production is optimised to market conditions. There is continuous visibility to enable active business management. Business areas are optimised in real-time across interconnected systems.
Safety and risk management with zero injuries and zero incidents. Employees are removed from hazardous activities. There is constant monitoring of worker and plant health. Threat prevention and response – cyber and physical – is automated and a part of the culture.
Reliability and maintenance are crucial factors. No unplanned downtime, minimal cost, implementing analytics to predict the health of all equipment, with no reactive repair by taking pre-failure corrective action through automated closed-loop systems.
Energy and environmental sustainability is now a given. The company or site is recognised as a sustainability leader with continuous analytics to predict and prevent releases. Energy consumption is measured and dynamically optimised with production. Energy sources are chosen with a balance between business and shared community goals.
Supply chain management must have continuous real-time decision support system for decision-making. Customer-specific production is the norm.
Systems and data integration is contextualised and available across the enterprise. There is a single standard for master data that feeds all related systems, including hierarchy, naming and attributes. Modifications are reflected immediately in all systems. Data is available across the enterprise for easy access from multiple applications.
Organisational effectiveness is important too. Empowered workers are freed up to drive even more value for the manufacturing sites. The team takes a larger role in meeting KPIs. Collaboration is embedded in culture and work processes. Persona-based alerts and notifications help instant response.
Technologies and tools
When you have completed the DMM exercise, determined your maturity level, decided where you want to be in a given timeframe, it is time to lead deployment with functional collaboration, technology selection, information architectures, execution plans, and the most important of all, return on investment (ROI). In business, I always believe that if a solution is not economical, it’s not a solution. First, your spending needs to be on the right tools and technologies as you need to make sure your investments are tied to the desired business outcomes. Second, you need to be able to deploy quickly, vet the technology, and scale when appropriate. At every step, success criteria will have to be met with all the key stakeholders in agreement.
Finally, you need easy implementation, which means the implementation of intuitive tools or experts who can accelerate your implementation so that you can quickly realise the benefits. Systems, data and people spread across the enterprise and isolated in silos makes it difficult to identify issues impacting operational performance at the plant and business performance across the enterprise. The next generation of OT data management needs to seamlessly connect people, processes and data to deliver a unified strategy across the enterprise that brings long-term value and a return on your investment.
The DOI or ecosystem you want to deploy has to easily connect to multiple applications and bring your data into a central repository where it is automatically connected to critical data management tools such as Artificial Intelligence (AI), Machine Learning (ML)-based analytics, and integration to CMMS enabling closed workflows. For most process equipment, rule-based AI with cause and effect based on first principles is more robust and deterministic than pattern recognition. It is important to understand this before ML can be applied to balance analytics tasks with the probabilistic outcome.
Modern OT data management has to address the below requirements minimally:
Connect with diverse data sources, industry protocols.
Flexible and adaptable for nearly every file format.
Virtually unlimited scalability, while contextualising data.
Make the most of decision support and AI or ML analytics platforms
Role and responsibility-based secure access, including external partners
Reporting and information are available to anyone, anywhere within the plant or enterprise as per roles and responsibilities. There are automated live KPI generation for a comprehensive cascading set of metrics with strong leading indicators in use with live near-time performance indicators. Performance of the whole operation is monitored using integrated predictive analytics and prescriptive expert systems, providing continuous dynamic KPI targets.
A modern digital operational infrastructure (DOI) platform aggregates the silos of data across the enterprise which may include a network of servers and software programs to quickly make use of technology investments without distracting plant personnel from their daily responsibilities. Thus, DOI can be built starting with existing systems. As stated earlier, addressing the low-hanging fruits is a good strategy as one starts small, is motivated by quick successes and scales big. In the past, many digital transformation projects stalled after starting or failed to scale while trying to do everything at once with big plans that the organisation may not have been ready for. Digital transformation is about cultural adaptation; thus the strategy and roadmap will have to be owned by users.
Key applications in the DOI must include:
Portal view your assets through a single pane of glass and gain access to your single source of truth. Communicate with clarity as each person sees only applicable data while alerts on mobile devices keep everyone connected in real-time. The user interface is similar across desktops, tablets, or smart phones for ease of use and collaboration. Persona-based alerts and notifications depending on roles and responsibilities deliver information to the right person at the right time.
Analytics is out-of-the-box process data analytics software that interprets operational data and information scattered across the plants, eliminating the need for gathering, analysing and reasoning over data and information from control systems, databases and plant applications. Using Artificial Intelligence and Machine Learning techniques, it can detect abnormal behaviour of processes and assets in real-time and predict failures. This shortens the decision-making process, preventing further performance deviation and safety issues while maximising plant efficiency.
Data Lake is a modern operational technology (OT) platform with data connectivity, data management & data repository solution built to accelerate your digital transformation programs. Inspired by noSQL databases, it must integrate with anything, store everything, and scale out easily.
Connectors securely connect and collect disparate data sources. Integrate any enterprise application with interface servers. Meet organisational requirements for the flow of data while adhering to IEC 62443 cybersecurity best practices of independent IT and OT systems. This clear line of responsibility also simplifies collaboration by avoiding overlaps and gaps. Easily transfer historical data from an external historian into the Data Lake or to another historical destination.
As you hit the ground running with an implementation plan after all of the above exercises – your assessment of the organisation, a DMM roadmap with agreed KPIs to all stakeholders, tools and technology chosen for implementation – you want to see immediate results and a glimpse of targeted ROI being realised. Operational excellence then is not a buzzword anymore as leadership expects you to deliver the improved KPIs from digital transformation into newer and higher benchmarks as a new normal is established. In conclusion, within the seven operational areas identified, you must look for best-in-class behaviour that drives value. Organisations that implemented DX, wholly or partially, have experienced a change in essential competencies as stated below:
Change in management
This brings top-quartile performance with a motivated and empowered workforce with huge gains in productivity across people, processes and technology. Post the pandemic, as the global economy begins to recover and competition increases, the complexity of some of the plant assets, the cost of their maintenance and their direct impact on production, the need to maximise asset availability while managing their efficiency and performance is no longer an option. Expertise required during times of need from the most qualified staff is not always available due to reasons of attrition or retirement. DOI thus needs to step up to close this gap for effective operations management. In the process industry, Emerson’s offering of products, services and software platform is a coordinated approach to realise this potential.