Decoding the Fundamentals of Digital Transformation in the Industrial Sector

Decoding the Fundamentals of Digital Transformation in the Industrial Sector

Many industries are becoming ever-more cognizant of the need to improve customer experience, yet the industrial sector still appears to be lagging behind. Soeren Bech, vice president of EMEA at Persistent Systems, explores the barriers to innovation in light of the changing role and growing scope of digital transformation in the industrial sector and how organizations can tackle the challenges to improve customer experience and productivity.

Today, there’s a clear need for an industry-wide shift toward digital transformation and a fundamental rethink of what manufacturing means. As opposed to simply making products, the field is now moving toward the notion that added services should be delivered alongside them.

The shift toward smarter industrial services is key to boosting customer experience and is poised to be a major consideration for manufacturers moving forward. However, succeeding entails moving away from legacy tech while consolidating data and streamlining operations. It also means accessing the right cx partners to realize the benefits of digital transformation.

See More: Open Banking APIs Can Make Digital Transformation More Sustainable

Embracing Innovative Industrial Technology

Technical innovation poses a range of possibilities for any industry, and manufacturers are already adapting to improve their services. Rather than simply focusing on producing machinery, organizations are embracing AI/ML technologies to enhance digital interactions with customers and to improve the functionality of the equipment.

 We’re seeing agricultural manufacturers improve their responsiveness by introducing conversational AI, or chatbots, rather than relying on correspondence via email or telephone. Pairing these with human operators allows them to streamline customer service, with clients getting faster responses via tools available around the clock. Meanwhile, support teams benefit from reduced case backlogs, allowing them to focus on more complex requests necessitating human interaction.

Increased automation also allows manufacturers to cut employment-related costs, with reduced need for production workers on-site. However, this needs to be complimented by algorithms trained for maintenance prediction, enabling staff to identify operational problems and replace components. By combining historical and current data from various operations on the production floor, this form of predictive maintenance can finally be realized, with potential failures anticipated and repairs made only when needed.

Making this work requires IoT sensors installed on factory floors, alongside machine learning (ML) and integrated systems, to create interconnected industrial architectures. With these in place, different assets will be able to collect and share data for real-time reporting on the condition of machinery, in addition to tracking performance statistics and providing recommendations on repairs to be performed. 

This prevents long-term wear and tear leading to machinery failures, which entail unplanned reactive maintenance that’s often time-consuming and disruptive. At the same time, manufacturers will also prevent costs incurred by preventive maintenance, where procedures are continually performed to reduce the chances of equipment failure.

Developing predictive intelligence and combining it with data from the cloud will also allow for more sophisticated dashboards on the performance of machinery. It’s possible to use digital twins for this purpose, allowing production workers access to visualizations of machine health and testing results. 

Access to this industrial technology also allows for other ML use cases, presenting further benefits of digital transformation. For example, manufacturers will be more adaptable when forecasting demand, with a predictive analytics workflow informed by data capture across the production floor and information on customer buying habits, supply levels, and material costs. 

This includes forecasting stock levels to anticipate supply requirements from vendors and variations in demand by product. Moreover, manufacturers can also configure algorithms for churn prediction, allowing them to adapt for a better customer experience. 

Barriers to an Elevated Customer Experience 

Reaping the benefits of digital transformation requires engagement with an ever-expanding range of platforms, offering diverse applications. Industrial organizations often struggle with this, especially when addressing data management and security concerns. They must also simplify their industrial technology stacks to ensure the business is aligned with internal operations.

Various industries have come to value personalized outreach across digital channels. Manufacturing is no different, but realizing this requires unifying customer data to achieve the coveted 360-degree view. With access to marketing cloud technologies, it’s possible to centralize data from various source systems, allowing for better insights and personalized engagement that delivers modern, convenient experiences. 

However, properly managing this information entails trust from potential clients. Customers will be more likely to share their data when understanding the benefits of disclosing personal information, and when trusting the organization in question. Implementing the right data protection software is essential to consolidating this trust, especially when considering the increasing prevalence of ransomware attacks.

Manufacturers can’t afford to be public victims of malware-related breaches, not only due to the costs of recovery and potential ransom payments but also the negative impact on the trust of potential clients. This is an increasingly pressing concern, with the number of reported attacks this year having doubled in the UK alone.

Many industrial companies also need to de-silo their internal processes, as many production lines still operate in isolation, each with its own IT solutions and data. This runs counter to the agile, integrated offerings customers are increasingly demanding. When selecting new technologies, manufacturers must consider what platforms best support global operations and reduce overhead by removing those that aren’t applicable.

 See More: 3 Digital Transformation Lessons B2B Companies are Learning the Hard Way

How to Implement New Digital Strategies

To successfully implement new industrial technology, manufacturers should stop wasting time on implementations that become obsolete in the short term. Rather than pre-defined processes that leave little room for discussion, development needs to proceed in a manner that allows businesses to go live faster while adapting to constantly changing requirements. 

In essence, that means moving away from development lifecycles based on Waterfall, with its long-term endpoints, reduced input, and possibly faulty implementation. Instead, industrial organizations must embrace the Agile process, where deliverables are defined in shorter periods, and tech is updated accordingly to avoid obsoletion. 

Manufacturers need to perform audits of their industrial technology in order to assess the extent of their digitization initiatives and identify which platforms need to be replaced. Realizing this means forming relations with cx partners who can advise on the right technologies to adopt and reliable processes that’ll ensure industrial businesses obtain the benefits of digital transformation.

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