<div id="readability-page-1"><div>CIO &amp; VP at Visteon, Transformation Instigator CIO ▶︎ Monetize your IoT, AI, Big Data Science, DevOps | 3X CIO100 | Fortune 500 IT Strategy 
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In part one of this article, I highlighted that the efficiency, cost, quality and, most importantly, the speed at which software is developed have become the differentiators between companies that are on their way to oblivion and those becoming indomitable market forces. 
Companies not only need to create digitally connected products, but they also need to respond quickly to the growing customer expectations at a much higher velocity. They need to be able to deploy software quickly to the products and production servers that are being used in the field. 
 
This brings us to DevOps. DevOps is the gateway to improved engineering productivity through initiatives such as easy access to best practices, robust deployment toolchains and the reduction of technical debt through flexible architectures. At my company, we are heavily invested in Dockers and Kubernetes orchestration in order to scale our continuous integration/continuous deployment (CI/CD) capabilities. The use of hyper-converged infrastructures and optimized SDWAN techniques is bringing consistency and resiliency to our DevOps infrastructure. 
 
When we make prudent decisions about our platforms, software libraries or integrated development environments (IDE), engineering a new product is easier. The trick is to keep prebuilt standard development environment images in your container registry. Software developers no longer need to talk to other team members or rely on tribal knowledge to set up their environment; they can simply spin a prescribed container image spend time on customer feature delivery immediately, rather than focusing on an IT infrastructure issue. 
 
Once you have a good platform architecture and a DevOps strategy, improving developer productivity is the next key ingredient of an enterprise’s journey to software centricity. Manufacturing and industrial companies are particularly good at managing their plants and equipment with years of industrial engineering knowledge and well-established metrics like OEE (operational equipment effectiveness). By contrast, they do a sub-optimal job of managing the most important software resources — namely, their developers, testers, integrators and architects. There are no established productivity metrics that are a given with the new breed of digital-native competitors. We are still focused on lines of code, the number of commits, etc. Those traditional software productivity metrics are a good starting point but do not really work in a comprehensive manner, especially with the shift toward model-based auto code generations and increased reliance on open-source libraries. 
The need of the hour is to change your mindset from productivity to contribution analytics. Employ modern-day algorithms that can use the power of ML/AI/NLP to determine the sentiment/intent in each code commit comment and the effort behind each software code commit — and appropriately quantify contributions and establish good baselines. A data lake that harvests all important data from multiple source code repositions — as well as task/project management data, including Agile story points, time tracking and defect churn rate — is a great investment. Remember, you cannot fix what you can’t measure, and you can’t monetize what you can’t measure.  
The other important aspect of developing software competency is making an organization-wide commitment to established frameworks like SPICE (software performance improvement and capability determination). There is a specific version for automotive that is called ASPICE (automotive SPICE). These frameworks define processes and best practices for software and software-based system development. The autonomous electric vehicles of the future will have hundreds of millions of lines of software code. A framework like ASPICE can help improve the traceability between customer requirements, design artifacts, software code and test plans. This is especially important for the embedded products in safety-critical domains. 
In conclusion, as products are becoming digital, the advantages that manufacturing companies have always had are fading. These companies must transform into digital enterprises with a software-centric approach to their core design, development and aftermarket strategies. It is still not too late; manufacturing companies that can execute on becoming software-centric can actually have an advantage over newcomers to their markets because of their traditional customer base and domain expertise.  
This is where the role of the IT leaders who can make the shift from product IT to traditional IT becomes critical. As a leader, you need to figure out how to combine your application development lifecycle management with your product lifecycle management in a holistic way. You need to lead the journey to capture your engineering data in model-based formats and build one common, API-based platform across your product development teams. 
Technology leaders who have an embedded software product development background, who understand the power of the platform and its ecosystems, who have a keen sense of market trends and who have the ability to collaborate and co-create with their product development organization will be the transformers of their enterprises. 
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As a leader, you need to figure out how to combine your application development lifecycle management with your product lifecycle management in a holistic way.

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