People and Culture as the Most Important Factors in Digitalization

Walter Pesenti, Vice President, of Manufacturing Excellence, and And Kyler Tebbutt, Director, Digital Factory Operations, NOVA Chemicals

We believe that the development and sponsorship of a well-defined digital strategy are critical for long-term success in the manufacturing sector. The decision to adopt many digital tools has been de-risked with proven use cases in operations in manufacturing environments and across industries for years. It can be valuable to understand this landscape to select the solutions that are right for the use case and to explore how these solutions, once implemented, can be linked together to unlock new levels of productivity and profitability.

While there are many digitalization options, it's important to review the critical success factors to digitize a business model. Perhaps not surprisingly, most of the success factors are people and culture-centric.

Other critical factors are executive sponsorship, strong adoption of "fail-fast" and taking the position of moving from evolutionary thinking toward revolutionary thinking. Companies need to take a blue-sky approach to digitalization. They must view "what is possible" as if starting from new, instead of focusing on the current state and evolving forward. That being said, any blue-sky strategy must be grounded in implementation reality and affordability. Although the blue-sky approach can be risky, a successfully implemented digital strategy which is based on an effective business case and not the latest fad, can lead to a step change in performance and even accelerate leapfrogging of the competitors.

The move from a traditional business model to a digital business model comes with certain inherent risks. Each of these risks presented below is manageable if considered effectively during the strategy and implementation phases.

Some of the most significant digital opportunities manufacturing environments should explore today are as follows.

Understanding Big Data Analytics

Big Data Analytics is the collection, sampling, integration, analysis, and interpretation of disparate data types (structured and unstructured) and sources (internal operating, internal financial, external demand, external economics, and the like) utilizing new database technologies, analytic and visualization tools to aid decision making. The results of big data analytics are insights previously unavailable to businesses given the extreme volumes of data, the processing capacities of humans, older technologies, and the associated timelines to derive meaningful insights. The types and volumes of data created through digitalization often require huge storage capacities, only truly available in the cloud as well as a data analytic platform to compile and analyze these data sets, to provide actionable insights.

Bad actor analysis for maintenance and reliability programs, channel management analysis, supply chain optimization, network optimization, absenteeism, risk-based accounts payable, exception-based reporting, customer purchasing patterns, and social analytics are some of its present-day uses.

Other opportunities include collection, analysis, and remote access to substantial data from multiple disparate sources that can provide an entity with previously unknown insights into their business operation, and customer demand, and ultimately lead to improved decision-making that drives better operating and financial performance.

Even with these opportunities, there are challenges, like the ability to attract talent, data ownership and protection, data architecture, data contextualization, data quality, sampling, and managing datasets.

Learning About Cloud

Cloud computing is the provision of computing services – data collection, storage, and analytics – through a third party, remotely hosted and accessed server over the internet. Software as a Service (SaaS), Infrastructure as a Service (IaaS), Storage as a Service, and so forth are its current functionalities. AWS and Microsoft are huge players in this space.

The largest wins from cloud computing are both the ability to move fixed costs of IT to variable costs on a pay-as-you-go model with third-party cloud service providers and the ability to access critical data from remote locations without being wired into the corporate network.

The obvious challenges to cloud computing are around guaranteed uptime, sensitive data, and security. In the case of operationally sensitive data, any severe outages or delays can disrupt the operation and be very costly; either in terms of decision-making delays, missed opportunities, and/or the need to shut down a portion of an operation. In terms of competitive data, any security breach can cause data to become public information that provides competitors with key insights into an operation. However, both of these challenges are just as likely with their on-premise solutions and with the world watching. Cloud providers are more capable than ever of being the best of the best at this game.

By far the greatest concern has been around who can get market access or competitively sensitive data when it's in the cloud. The reality is that this is a security and access management challenge that is true with on-premise systems as much as the cloud. The difference is that cloud providers are having to prove their capabilities in this area to stay in business and compete.

Below we present a quick summary of a few other important digital technologies that will impact the manufacturing sector.

There are many opportunities for digitization. The critical component is to understand the potential and consider what digital strategy is right for your business. The technologies provide opportunities but, in the end, the right technologies need to drive value for the business. Companies that do this right realize benefits beyond the point solutions and have the potential to leap-frog competitors and maintain a sustained competitive advantage.