Knowledge Management is Integral to Product Development
Owing to rapid commoditization of products and increasing competition, ‘time to market,’ ‘first time right,’ and ‘cost competitiveness’ are emerging as key differentiators for automotive manufacturers. Today, the race is not just about creating new products, but doing so faster than the competition. Hence, organizations need to leverage current and past marketing information, design data, manufacturing operations data, and testing and service data, to build a constantly evolving knowledge repository that they can tap into; to create new products quickly.
During a typical product development process that starts with strategizing the product to designing, validating and testing it, and finally ends with the product being phased out; large volumes of data is produced. This data may be processed and used as a reusable knowledge base to provide real time intelligence to the product development teams. Hence, managing this knowledge sourced from various data sources and using it in the product development process, offers great potential for improving process efficiencies.
Emerging Trends in Product Development
Based on our observations of the emerging landscape, we believe that conventional product development processes are paving the way for newer and more robust ways to ensure customer acceptance of a product long before it is launched, that is, while it is being designed.
- Crowdsourcing and social media to play a big role in collaborative product development
- Big Data analytics close-looping with knowledge base
- Use of telematics in assimilating knowledge for product development
- Internal and external collaboration for knowledge sharing
How Big Data can help in NPD
The increasing proliferation of computers and embedded electronics in the manufacturing industry has resulted in the generation of a huge amount of structured and unstructured data. The emerging challenge for organizations is to derive meaningful insights from available data and re-apply it intelligently. This involves collection of data from direct and indirect sources, analyzing and synthesizing it along with relevant enterprise data, to derive meaningful information and intelligence, converting it into a useful knowledge base, storing it and finally delivering it to end users. A good approach to analyze Big Data and the consequent knowledge management is shown in the figure.
Here are some high level takeaways for manufacturing organizations planning to embark on this journey:
Read this paper to know more on how manufacturers should manage knowledge and use it to smoothen their product development process.
- Set a clear vision for knowledge management in your organization
- Perform a thorough assessment of your current status, pain areas and future requirements
- Devise a well thought out long term strategy for knowledge management
- Prepare a roadmap to achieve the vision and successful implementation of strategies
- Implement knowledge management as a business transformation program with the right amount of focus on business processes, technology, infrastructure and organization change management