Pathway to digital process innovation — How dynamic capabilities can help process industries
AV: KOTESHWAR CHIRUMALLA
Digitalization and industry 4.0 technologies promise to provide many novel opportunities and benefits to process industry firms. Yet many firms face challenges in adopting and utilizing such technologies for successful process innovations. This article explains how process industry firms can develop a pathway for digital process innovation in a step-by-step manner by building dynamic capabilities.
Process innovations—the development of new or improved production methods, techniques, or technologies—are critical for process industries. The process industries refer to a cluster of manufacturing sectors (mining, steel, pulp and paper, chemical, pharmaceuticals, mineral, and food and beverage). In Sweden, this cluster of sectors contributes approximately SEK 135 bn to the country’s net export value. Production processes in process industries are often capital-intensive, leading to inflexibility when changing process settings. Any kind of poor management in the implementation of process innovation could lead to inappropriate product properties (e.g., durability, strength, colour, appearance), inadequate work processes, competence gaps, etc.
The adoption of digital technologies such as IoT, cloud computing, big data analytics, augmented reality, and AI could enable enormous new opportunities with their real-time connectivity, intelligence, and analytical capabilities. All of the significant production parameters can be recorded using hundreds of sensors, and the predictive models developed from the data could be used to anticipate optimum settings for improving manufacturing processes, material usage, predictive maintenance, and life cycle management of the product. The generation of such extensive data sets as well as historical performance also offers possibilities for proactively enhancing process design and drive innovation. However, firms have yet to explore the potential of many data sources, which creates challenges for seizing opportunities provided by digital technologies. Consequently, results and conclusions drawn from the data analysis may not be completely accurate as such analyses are often performed in a non-reflective way, making digitalization difficult to realize.
”We are going for digitalization and all that, and that’s fine. But you need to know where you are and where you are going before you start running.”
Digital process innovation requires dynamic capabilities
In our study of the digitalization of process innovation within two steel manufacturing firms, we found that process industries must extend the scope of traditional manufacturing and development-related activities to digitally/IT-driven-related activities, which requires building and integrating a whole range of novel capabilities and resources. These capabilities are not fundamentally limited to technology diffusion; they also relate to firms’ strategic change and organizational management, including strategy, organizational structure and processes, resources, and cultural readiness. Dynamic capability, which is a firm’s “ability to integrate, build, and reconfigure internal and external competences to address rapidly changing environments” (Teece, 2007), is a potentially interesting analytical framework for holistically studying such strategic organizational changes. Firms’ lack of dynamic capabilities is one source of inertia for missing the required agility to respond to digital transformations.
Three-phase model moving towards digital process innovation
Our study defined digital process innovation as “the use of a combination of new digital technologies to support the development and implementation process of completely or significantly new production methods, procedures, or techniques by acquiring, integrating, or reconfiguring organizational resources, structures, infrastructure and culture.” (Chirumalla, 2020). We found that process industry firms should complete three phases to become successful in the digitalization of process innovation: preparing the organization for digital process innovation, exploiting the digital opportunities within the organization, and innovating the organization for digital process innovation (see figure 1). In each phase, firms need to acquire and develop specific dynamic capabilities to achieve a mature digital process innovation readiness, which is also supported by specific key enablers. The following section briefly describes each phase and the specific dynamic capabilities that need to be built by process industry firms.
Phase 1 – Preparing the organization for digital process innovation
In the initial phase, firms need to develop a foundational basis to holistically prepare the organization for a smoother transition from “traditional process innovation” to “digital process innovation.”
Dynamic capabilities: In this phase, firms need to develop four dynamic capabilities: management vision and a giraffe’s view, integration of process and IT know-how, agile cross-functional teams, and data-driven sensing. To reconfigure and transform the traditional organizational setup to embrace a digital way of working, firms must prepare for change management and help employees realize that they need to change. Management can do a lot to prepare the organization internally for such change, such as clarifying the firm’s vision, explaining why the change is necessary and what it hopes to achieve with the change, and showing what the future will look like with a few examples and possible scenarios. It is also important to establish a good technical understanding and a common way of working between process developers and IT developers in order to communicate both process know-how and IT know-how effectively. Formulating digitalization cross-functional teams focused on digital initiations across functions will help support the organization in the long term. Firms should also realize the need to be more agile when forming teams made up of various experts to achieve better seizing capability. After establishing these dynamic capabilities, firms can use the available data—OEE measurements, maintenance data, and shutdown times—to visualize various parts of the process on a daily basis.
Key enablers: Firms should establish proactive management practices and define infrastructure and methodologies to promote digitally-enabled process innovation. This radical shift takes time, costs money, and requires a lot of dedicated resources; therefore, management must be on board. Examples of management support include adding appropriate resources to visualize and analyze the processed data; defining the way of working, including collecting, handling, analyzing, and visualizing data; and translating top management’s digital strategy to the local production site management. In addition, firms must build a foundation for technology and digitalization infrastructure as a starting point in order to establish the necessary methodology to connect all essential production equipment and process steps to realize a structured way of working with and managing data.
Phase 2 – Exploiting the digital opportunities in the organization
In the second phase, firms must invest in exploiting their digital opportunities by running, for example, pilot projects or demonstrators in order to analyze data sets, enhance collaboration around the data, and define necessary training mechanisms.
Dynamic capabilities: In this phase, firms need to develop four dynamic capabilities: ability to navigate visually abstract and detailed views with data, support for scenario-planning practices, ability to leverage collaborative engagement with data, and strategic training mechanisms. Digitalization provides firms with a greater opportunity to get an overview of and details on the situation and production plant quickly. Digitalization provides rational and interdependent process correlations for errors as well as for key performance indicators (KPI). For example, a product quality error is considered the tip of the iceberg; digitalized tools make it possible to identify the factors that might affect quality outcome. Firms can use digitalization to enable “what-if” scenarios to help them determine what to produce and how to introduce new processes. Firms can also compare stored historical data for different problems, incidents, and defects to live data in order to determine how to prevent future problems. Moreover, with digitalization, firms can involve more people—not only experts, but also operators—in identifying and solving problems. By visualizing the process data and making it available to every employee, firms can identify the possibilities of leveraging social interactions and discussing root causes. Finally, firms should consider educational training and ways to change people’s mindsets as essential efforts and take steps to promote thinking outside the box. In this way, firms can recognize that digitalization is not a silver bullet; people need to come together to find opportunities in the new working setup.
Key enabler: Planning a digital matureness for each function and department plays a key role in this phase. Firms need to develop a matrix to understand and plan the digital maturity in each function and department. There are a lot of variations in the way several functions look at the strategy and drive their own initiatives. Process developers, process technicians, and IT technicians need to speak the same language to ensure that the right specifications are translated into the development of tools.
Phase 3 – Innovating the organization for digital process innovation
In the third phase, firms are able to innovate their organization by achieving a full-scale data-driven way of working for process innovations.
Dynamic capabilities: In this phase, firms need to develop four dynamic capabilities: ability to formulate data layers from KPI to the basic level, a data-enabled feedback loop mechanism, a bottom-up and evolutionary approach, and a continuous digital improvement strategy. Forming a good data layer is critical for handling data and investigating problems to improve production processes, product quality, and delivery performance. Firms need to connect all the right data and ensure that they interact throughout the process to develop a broader overview and determine if causes correlate to each other. Digital support is a feedback loop that allows firms to predict problems and give early guidance (temperatures, vibrations, sounds, etc.) through warnings or alerts. Firms can also suggest a structured way of building data-driven operations and workstations, following a module-based approach. Such an approach allows for the categorization of products, machines, workstations, and operations in a structured way, making it possible to turn data back and forth in a standardized way. In addition, firms need to adopt a continuous digital improvement strategy to succeed with seizing opportunities.
”The digital solution could help you see things that you can’t see with your naked eye or with a testing. If the system could predict and give you guidance in one way— “This is how you should try to do it”—it could be a great help.”
Key enabler: Firms need to prepare to work with more predictive and analytical data as the data available will be enormous. Placing all available data from the factory in layers makes it possible to see the overview of the plant transparently on a screen (e.g., different colored lights indicating issues). Firms also need to develop the capability to acquire the big picture and dig deeper, as needed (e.g., navigation of abstract/detailed views).
How can industry benefit from the three-phase model?
Our findings offer several useful insights for production/plant managers, technical directors, and process development leaders in the process industries who are seeking to improve performance in process innovation with the support of digitalization. The proposed three-phase model and dynamic capability approach could help these leaders holistically understand their process innovation work and analyze their firms’ distinct resources, processes, structures, and infrastructure. Accordingly, managers can either develop a clear vision for the digital process innovation work or build/modify their firms’ digitalization maturity roadmap through the targeted modification of resources and capabilities. Our study findings can also support managers making strategic investment decisions on data gathering, analytics capabilities, and cloud-based platforms as well as when planning competence development and training programs for smart factories. Finally, the results from the study can be used as guidelines for managers seeking to initiate a dialogue on capability building with multiple stakeholders within the firm.
> Chirumalla, K. (2020). Building digitally-enabled process innovation in the process industries:A dynamic capabilities approach. In: Technovation. Forthcoming.
> Lager, T. and Chirumalla, K. (2020). Innovation and production management in the process industries: An extended editorial viewpoint and a way forward. Journal of Business Chemistry, 17(3): 1-16.
> Teece, D.J. (2007). Explicating dynamic capabilities: the nature and microfoundations of (sustainable)enterprise performance. Strategic Management Journal, 28,1319-1350
The study was conducted in the context of the XPRES (Excellence in Production Research) framework at Mälardalen University Eskilstuna and Digital Stambanan project. The XPRES is a joint initiative between KTH, MDH and RISE. It was one of two strategic initiatives within Manufacturing engineering in Sweden by the government in 2010. Digitala Stambanan is a collaborative project between the Strategic Innovation Programs Production 2030 and PiiA funded within the framework of the Collaborative Program Connected Industry and Smart Materials. We would like to thank our funders and partner companies whose support made this study possible.
Koteshwar Chirumalla is an associate professor and subject representative in product and process development at Mälardalen University (MDH). His research interests include industrialization of new products and production technologies, digital transformation, servitization, and knowledge management. He is currently leading a research projects on topics: digital twin ecosystems, second life of electric vehicle batteries, and digitalization of manufacturing. He has published around 50 articles in leading international journals, conferences, book chapters and industry handbooks.