Howard Heppelmann is a software executive with experience in general management, strategy, sales & marketing, product development, professional services, strategic alliances and M&A. He is experienced in defining new market opportunities and implementing effective go-to-market programs that produce accelerated revenue growth and has deep experience in manufacturing digital transformation.

How has your IT operating model changed during the last five years in Manufacturing?

There are a number of critical changes that have taken shape.

1. From a business value point of view, we are looking for projects that can be done in less time with less risk. The clock speed in manufacturing is accelerating, and we can no longer afford to spend 12 months defining a solution and 12 additional months to deploy it only to find out that initial requirements have changed or were not properly modeled. With today’s technology, we are looking to apply more agility in our quest for continuous improvement. That means short projects with clear and rapid ROI.

2. There is a convergence of two traditionally distinct domains: IT and OT. Embedded in the promise of Industry 4.0 and IIoT is the convergence of business data and operational data. This not only has significant impact on the future infrastructure we deploy, but also the applications and the type of technology needed to deploy these type of applications—technologies that were designed to support both disparate sources and structures of information.

3. Hybrid cloud and analytics capabilities. Although most manufacturing applications are still run on premise and some will be for a very long time, the ability to capture and process high volumes of complex machine data at the edge while also being able to forward on subsets to the cloud to support enterprise use cases is changing how we traditionally thought about data centers that were typically located on premise. Also the role of analytics is impacting how we organize to get the most value out of our data. Gone are the days where each department has proprietary rights over its own data.

As we enter the word of big data, we have to develop organization structures that allow us to get the most out of that data.

4. User experiences–Mobile applications and increasing augmented reality deployed on mobile applications are changing how we think about delivering value to our workforce to improve efficiency and quality.

What do you think are the biggest obstacles that Manufacturing technologists face in working in a more agile and outcomes based model?

1. Cultural

2. Inertia–many companies want to become more agile yet continue with old waterfall approaches to drive continuous improvement. For companies to become more agile, they need to adopt agile methodologies as a core element of their continuous improvement initiatives.

3. A perception that technologies that were developed 20 years ago are the path to tomorrow’s agile enterprise. Manufacturers need to modernize their infrastructure to enable a foundation for agile operations. The Strategy for Faster Delivery Embracing agile methodologies. Short iterative projects. Fail fast or succeed rapidly.

What set of skills do you think is required for the Manufacturing technology leaders to be successful in the new enterprise landscape?

1. Expertise in agile methodologies and practices.

2. Edge and big data analytics/ machine learning/predictive analytics (maintenance and quality).

What are the future technology innovations that you are personally excited about in Manufacturing ?

1. Systems of engagement–with modern IoT technology platforms, the traditional rip and replace approach to creating operational improvement will fade away. IoT platforms that are designed to integrate across the existing infrastructure, correlate and analyze disparate sources and structures of both operational data and business application data are able to convert the largely untapped abundance of untapped data into a significant and immediate source of rapid and continuous improvement.

2. Augmented reality – Augmented reality stands to have the most significant impact on workforce efficiency of a generation. The cognitive benefits of visually training and operationally guiding workers through complex tasks have proven to improve efficiency by 30 percent and quality by 90 percent. It also lowers the burden for specialization and training because the workforce can adjust far more rapidly to new tasks with much less initial training. Augmented reality is a true example of convergence between the digital and physical worlds.