Technology capability is the core foundation for agile and intelligent manufacturing. The adoption of digital technologies has helped organizations in more ways than ever – connected products, connected workforce, connected supply chain and connected customers.
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What does it mean to have agile and intelligent manufacturing?
Manufacturing organizations, which have their core competencies reimagined around digital capabilities and rapid adoption of digital technologies such as moving to cloud, embracing automation, robotics and harnessing the abundance of data to drive organization cultural change and enable ecosystem, led collaborative growth. Such organizations are set to be on the path to agile and intelligent manufacturing – such as automotive OEM’s have now embraced digital technologies within the product, across the value chain and are building purpose driven ecosystem partnerships with an aim to provide their customers with mobility solutions.
What is the best strategy to become agile and have intelligent processes?
Manufacturers needs to rethink their systems, processes, products and services in this new paradigm. A good starting point is to keep in mind the outcome that customers would like to achieve and build your business proposition around the customers. For example, Personalization of nutrition and wellness is a rising trend. While there are several value chain players providing point solutions, how can supplement manufacturers tap into this trend to increase their wallet share? This requires a holistic view of tapping into the intelligent and connected capabilities to not just reimagine the processes but also rethink their entire value chain and the ecosystems in which they operate.
What are the benefits of having agile and intelligent processes?
We have seen that companies having adopted the agile and intelligent processes are better positioned to repivot their organizations adapt to the changing business needs and deal with the economic impact of the same. Equipped with a near real time view of their Business health and all their processes they are able to conduct a good impact assessment and take informed decisions with confidence. Such as a chemical company set up a command center to create an end-to-end view from order receipt to fulfillment and related stock orders movements to meet the demand. This system collected and analysed across multiple processes and deployed machine learning models to provide early indicators and prescribe actions, resulting in significant improvement in their schedule adherence and subsequently CSAT. With improved visibility, the order lead-time was a lso reduced, thus freeing up working capital.
What are the challenges of striving to create agile and intelligent processes?
Driving this paradigm shift requires a strategic vision and commitment from the executive leadership. They need to drive the cultural change and the shift from a traditional engineering culture to infuse a digital mindset across all the stakeholders including influencing the ecosystem partners. Finding the right talent and skillset to drive the agile & intelligent manufacturing remains elusive. The skills required are multidimensional, we need a business partner who is not just technology savvy but has a good understanding and contextual knowledge of the business.
Another big challenge is the data itself – ranging from data lifecycle management to defining a comprehensive framework of data exchange between ecosystem partners accounting for ownership, rights , data privacy & security concerns, industry standards for interoperability for real-time actionable data exchange. Other challenges include, managing complexity and Infrastructure requirements that comes with increased usage of technology and operationalizing the IT/OT processes. Training of existing workforce to rapidly adapt technology and adapt to new ways of working.
How can industry 4.0 revolutionize manufacturing?
The combinatorial effect of the Industry 4.0 technologies is revolutionizing the operating model. As more things are being connected and in turn generating vast amounts of data, it allows real time monitoring of the assets and its context in which it is operating. Analysis of data provides deep insights into the operating conditions of the assets. […]
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