Product Lifecycle Management (PLM) is the process of managing the design, development, and delivery of [...]
Product Lifecycle Management (PLM) is the process of managing the design, development, and delivery of a product from idea generation, concept prototyping, engineering, manufacturing and beyond. Prior to Internet of Things (IoT), typical product lifecycles tracked by traditional PLM mostly covered pre-production states – manufacturing, distribution, shipping, purchasing, etc. Any relevant active life (operational) states such as system errors, failures or product issues that occur after the product has been deployed are manually reported by the customers. The resulting aftermarket services are reactive, lead to unpleasant customer experiences, and manufacturers and service providers typically incur high (avoidable) operational costs. This was the ‘ship and pray’ era. The core problem was that since the product was not connected there wasn’t any data stream from the product, hence manufacturers were blind to how the products were being used, how they were performing, what conditions were causing failures/issues etc.
Traditional tactics to alleviate this challenge included focus groups, surveys, field trials and after-the-fact observations. This lack of visibility in product usage data during the entire active life of the product resulted in delays in issue resolution, unnecessary truck rolls, avoidable system failures, loss of business, and ultimately, customer (dissatisfaction) churn.
IoT-enabled products have dramatically changed this process – now, with connected objects, the PLM process extends from pre-production all the way to live deployment. The data from connected products enables actionable lifecycle insights that allows product management, sales, marketing, operations, and C-level to gain end-to-end visibility on product performance and usage, build a better understanding of customer’s actual engagement and interactions, optimize their aftermarket services programs, and focus their future product roadmap.
Using IoT analytics platforms, new active-life lifecycle states can be automatically inferred and analysed. For example, distribution of product registrations/updates, time to activation, time to feature usage, various levels of use over time and by location, deeper analysis of inactivity, linking product performance to customer claims/issues/returns, comparing usage behaviour to customer churn, end-of-life analysis etc.
These are all meaningful insights that can enable new services and fundamentally improve existing process, including:
mnubo’s SmartObjects platform is one of the first industry solutions to include out-of-the-box Product Lifecycle Analytics tailored specifically to connected product manufacturers.
Interested in taking a deeper dive into our product Lifecycle Analytics Library ?