Incorporating advanced analytics can help stakeholders to adapt new business opportunities and also reap many benefits from it.
What do P&G, Jaguar, Land Rover, Unilever, Johnson & Johnson, or Rolls Royce have in common? Yes, they are all leading product companies of the world. But apart from that, they all vouch for the transformative power of having a cutting-edge product lifecycle management (PLM) approach in place to facilitate top-notch customer experience, boosts R&D, manufactures new goods, and fuels efficiency.
According to Forrester, by 2020, 85 percent of customers expect companies to automatically personalize deliverables and proactively take care of its needs. So, PLM is primarily about ensuring differentiated, superior customer experience by driving innovation, achieving faster time-to-market, delivering quality, while keeping costs in line. While IoT (Internet of Things), AI, ML, NLP and other such innovative technologies offer real-time information, challenges (detecting failures, predicting loss, calculating correlations and prioritizing solutions with cost limits) remain.
Generate more value from data and enable insightful decision making
While organizations worry about the cost of new products and ROI, customers care about the product’s price/value ratio and quality. Advanced analytics can help both the stakeholders to adapt to new business opportunities. Incorporating advanced analytics into the process can reap many benefits: companies can fine-tune their market forecasts, predict failures and estimate downtime, creating more value for the business and their customers.
Road-blocks in product lifecycle management
Executives managing the product development process must think through some critical decision-making points when strategizing for the digital future. A few of them are:
- How to overcome constant technological disruptions to deliver customer experience that excels?
- Which internal/external factors will influence the product’s performance?
- How to leverage the available skill sets, technology and knowledge to drive organizational efficiencies through smarter operations?
Many companies still lack the arsenal of digital tools required for smooth functioning and must rely on guesswork or trial and error.
How does analytics come to the rescue?
Historically, organizations have long relied on traditional product development methodologies such as FMEA (failure mode and effect analysis), DOE (design of experiment), mean time between failure analysis and value stream analysis.
With ever-increasing volume of data coming in today, conventional technologies fall short and disruptions are widespread. But innovative companies know that data-driven insights play a role across all functions of the product lifecycle and strategize accordingly to maximize the value derived from the investment.
For example, Netflix’s sustained success comes as no surprise for companies that understand the value of leveraging advanced analytics, machine learning and algorithms to drive powerful customer conversations. Netflix has something that is more valuable than money: Contextual Information. Using this data, their recommendation algorithm suggests the most relevant content to its users based on their preferences. The resulting customer experience is exceptional.