The product lifecycle has changed indelibly, and the effective use of data is driving that change every step of the way.
The concept of the product lifecycle has been around for an awfully long time, but as product development has shifted away from the waterfall method of development towards something much more agile, the ability to successfully monitor performance at each stage has become more complex. No longer do products shift smoothly from development to servicing, with very little further development once products hit the market, as now customers have much more complex needs, and expect the servicing they receive to include regular and complex product upgrades based upon their personal needs.
The Internet of Things is central to this as it provides a huge quantity of data to companies about the way in which their products and services are being used. This promises to provide immeasurable benefits at all stages of the life cycle, from more accurate prototyping and development to better understanding of how customers are using the product. In many ways, data is the glue that holds successful project life cycle management together.
This is because our global world makes product management increasingly complex, and with consumer habits demanding not only ongoing servicing, but rapid and personalised responses to changing market conditions, it’s vital to have robust, reliable and up-to-date data on the product throughout its life cycle.
The real-time availability of data is transforming the largely linear life cycle of idea - design - testing - manufacturing, and allowing for a more fluid and agile process. A traditional mantra of product development was that you would conduct analysis of an idea until you were 80% confident that it was a good solution, but that is now shifting to what retired four-star general Colin Powell refers to as the 40-70 rule, which states that once you have enough data to be 40% confident, you can use your instinct to make a decision. If you wait until you’re 70% confident, you’ve taken too long.
This shift in development methodology is in many ways, not a new one, and has its origins in the OODA loop (observe-orient-decide-act) that was developed in the 1970s, and which has helped to spawn lean, agile and scrum methodologies today, but the availability of data has made the cycle time of each iteration that much shorter, and the process of product iteration one that extends long into the lifetime of the product.
For instance, sensors fitted to equipment can monitor usage in real-time and provide predictive maintenance services to the customer. This has precipitated a change in business models for many manufacturers, who have migrated from selling a product towards selling a service, with data fundamental to ensuring that the benefits of their product are delivered reliably and consistently.
This transition has also seen the notion of planned obsolescence phased out, as products are increasingly designed to have a long lifespan so that companies can benefit from service-based income. This is increasingly demanded by customers who often have access to the same level of data as the manufacturers themselves. They are demanding reliable products that work in an energy-efficient way, and it’s up to manufacturers to provide that.
The data that is being generated can then flow back into the product development cycle, with reverse logistics allowing companies to collect data on the operations and usage of a product and then either update the existing line or create new products based upon the intelligence they gather.
Change as the new normal
Nowhere is the data-driven in product life cycle management more evident than in the consumer packaged goods industry, where brands are increasingly tapping into big data to smooth out the product life cycle and bridge any gaps that exist between market research, product development and maintenance.
For many companies, the rise of the internet of things and big data has given them access to such vast quantities of data that the challenge has moved from collecting to understanding the data they have. Companies across the world have reported frustration at their ability to make timely decisions from the data they have, and it’s increasingly this timely response that customers are craving.
This rapid response is often facilitated by getting as close as possible to the end customer. The growing desire for tailored and customised products is underpinning a co-creation of products with customers that is powered by data to help companies understand exactly what customers want. Big data is allowing brands to directly monitor consumer patterns and obtain buyer feedback, with customers a growing presence in the product design phase, whether through crowdsourcing channels or personalisation platforms.
A fragmented market is placing new demands on companies that only data-driven operations can possibly meet, as they provide insights into consumer behaviour and requirements to prevent companies from entering into a guessing game and instead providing precisely what customers want. This not only ensures customers needs are met more effectively, but it also streamlines the product life cycle as guesswork is removed from the development process.
The ability to utilise data to streamline the product life cycle was a central goal of an online tool developed by Hack & Craft to help calculate the lifetime cost of painting residential and commercial properties. The tool has allowed clients to significantly reduce the amount of time required to complete every lifetime calculation, which in turn enables sales teams to perform these calculations in real-time during sales consultations.
The ability to make timely use of data like this holds tremendous promise in facilitating the more adaptive and responsive product life cycle that is now demanded by the market. If companies are able to bring data together and ensure customers are involved throughout then they stand a good chance of not only reducing the costs involved in each stage of the product life cycle but also maximizing revenue.
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