Empowering IIoT Transformation through Leadership Support

Empowering IIoT Transformation through Leadership Support

Transformation can be slow, particularly for industries seeking to integrate IIoT into their operations. This integration often occurs at a more constrained pace, requiring careful planning and implementation of transformative plans to ensure its success. Addressing the need for change is just one step of the transformation process, and getting the ball rolling starts with a big push from executive buy-in.

Navigating the complexities of obtaining executive support for IIoT initiatives can pose a significant challenge in today’s complex economic landscape. Digital data is the cornerstone of success or failure and the most valuable asset for organizations today. It paves the way for smoother progress towards comprehensive IIoT evolution, complete digital transformation, and successful AI initiatives.

For numerous companies, staying competitive in the rapidly expanding IoT and IIoT industry and prioritizing digital transformation are key strategic advantages. Only 7% of companies have fully implemented their digital transformation strategies, and 55% of companies without a digital transformation plan believe they have less than a year before they start to lose market share.

Analyzing and Quantifying Existing Operational Issues

Many companies simply review the top and bottom line of operational Key Performance Indicators (KPIs) as the only measurement of success and rely on middle-management to optimize their disciplines. Focusing solely on KPI’s without directly identifying them at the process and equipment level due to limited data availability or using manual processes and legacy equipment can hinder the accurate assessment of performance and impede targeted improvements.

The lowest common denominator in understanding core operational issues is first gaining a real-time view of all equipment. Ideally, the type of data needed to determine machine health includes where it is, how it is functioning, what environmental factors affect it, when it needs preventive maintenance, or if it is being used safely. With the proper tools, organizations can efficiently identify currently connected and connectable equipment versus those that are analog or legacy and not capable of delivering digital data.

The identification and comprehension of process health is a function of correlating the machine health data identified above and adding the digital tracking of all assets. Having the data to understand where raw materials, specialized tools, equipment, finished goods, and people are at any given time, can be used to identify inefficiencies and waste.

Strategic Alignment with Key Stakeholders

To gain project acceptance and ultimately ensure project success will rely heavily on identifying all key stakeholders, nurturing an on-going level of mutual trust and maintaining a strong focus on targeted end results. This involves a full disclosure of desired outcomes and a willingness to adapt to individual departmental nuances. Begin with a cross-department kickoff/planning meeting to identify interested parties, open projects, and available resources. Invite participation through a discovery meeting, focusing on establishing the core team, primary department, cross-department dependencies, and consolidating open projects or shareable resources.

Once the core team is established and a dedicated communication platform is identified (e.g. Slack, Asana, Teams, etc.), the development of individual departmental requirements and nuances can enhance the overall completeness of the plan and provide the added benefit of gaining individual personal buy-in.

Establishing personal success metrics across the core team and facilitating weekly update meetings will help create quantification as well as qualification driven goals.

The Influence of Data on Business Case Development

The quantification process is crucial, potentially even more so than qualification, when seeking approval from executives across various organizational levels and spanning multiple departments. Establishing a core set of baseline metrics is key to identifying immediate impact and determining where pivots or further exploration are needed to achieve KPIs. These metrics also provide a starting point for setting growth and improvement goals.

Identifying all digital data blind spots at the outset highlights the scale of the problem. While many companies have Artificial Intelligence (AI) and Business Intelligence (BI) initiatives, their success depends on the quality of the source data. Consolidating these initiatives to address digital data blind spots strengthens the data-driven business case.

Once a critical mass of baselines is established, projecting Return On Investment (ROI)  from both a quantification and qualification perspective becomes possible. Using more graphs than tables of numbers can enhance buy-in from senior executives.

Uncovering Untapped Data for Digital Transformation Support

Selecting the ideal IIoT solution for your business involves balancing your needs with the capabilities of each technology. From the high-tech, precise tracking to the more labor intensive manual methods, there’s a tool for every job. The key is to match your choice to your operation’s size, complexity, and specific tracking requirements, ensuring your processes are as efficient and safe as possible. But with fewer than 30% of a company’s technology vendors actively involved in their digital transformation, now is the time to secure executive buy-in and set your organization on a path for success.

About the author

This article was written Bryan Merckling, CEO and Founder at Thinaer.