A decade ago, I was a design engineer at a truck manufacturer. The company’s USP (unique selling point) was its customer-centricity rather than being the lowest cost manufacturer. At that time, an order was secured to supply twelve customized trucks to the mining industry – more power, and ruggedness and a thermal shield in the underbody. There was about a month-long negotiation with the customer and internal feasibility check to ensure that commitment could definitely be delivered while meeting target margins.
A talented design engineer, though a slight introvert, led the personalization project from the front, realizing the value to all stakeholders. He personally drove close collaboration with procurement and manufacturing. For the next two months, he became integral to the assembly line and was seen moving along with each work-in-progress truck, continuously guiding the workers on modified parts and manufacturing steps. For about a year, he was the go-to person for any service issues or guidance on similar orders. Then he, along with his accumulated knowledge, left for a bigger role. After that, every such decision led to long cross-department meetings. The value, as well as the effort of delivering personalized products, was immense.
Returning to the present, customers (and people in general) are becoming more mindful of their needs and aspirations and what completes them. They are ready to spend a little more to get these things, but they want the same responsiveness and ordering experience as they experience with mass products. They also want guidance to create their own version with real-time compatibility and compliance checks. The personalization fulfillment is slowly leading to machine learning-driven intelligent configuration and Industry 4.0-driven on-demand production, sometimes using manufacturing-as-a-service or 3D printing.
This is not just the way to the customer’s heart but also their wallet. There are typically three steps to a personalized-products-at-scale business model:
- Design management of a personalized product by R&D and synchronization with downstream activities
- Customize products online with intelligent suggestions, compliance checks, and price quotes
- Manufacturing of the product with minimal human intervention and adding to downstream communication
In this first blog of the series, I will cover the first topic in detail, using the example of a motorbike (the same principles apply to more complex products).
To enable personalization, R&D, in collaboration with other departments, creates an intelligent super BOM or master BOM with these key features:
- Consolidate all options: Create a single multidisciplinary product definition encompassing mechanical, electrical, electronic, and software aspects of the product. All parts and software across all the supported variants are listed in the super BOM.
- Restrict personalization levels: For each part, assembly, and software, precise values of various parameters are mentioned to provide a selection threshold to the customer—e.g., diameter of the tires, width of the handle, etc. Compatibility between parts, products, ecosystems, and software is included in the super BOM.
- Check compliance: Run a real-time check for the material, product, and market.
- Estimate price: Pricing of various features, considering part costs, assembly costs, product lifecycle stage, markup, current response, etc.
- Commit lead times: Strategic sourcing agreements with vendors for supply of customized parts, along with their price, quality, and lead time.
- Guide manufacturing: Production engineering for all possible variants is available for all possible variants with the ability to manufacture via Industry 4.0 processes. Production engineering includes manufacturing steps and routings (workstation and assembly line allocations). Assembly steps and lines for different product types may differ.
- Design for 3D printing: In cases where 3D manufacturing is used for complex shapes, the ability to control mechanical, electrical and other properties at each point of the part and merge multiple parts will simplify assembly. This is where machine learning-based real-time simulations and recommendations can help deliver better products.
- Maintain digital twin for service/continuous product improvement: Leverage end-to-end traceable and complete data of every personalized product (as designed, built, delivered, maintained, etc.), genealogy (source, quality, lot, etc.), usage (ambient conditions, work hours), break down data, performance data, etc., to generate patterns and improve service and product design.
Embark on your personalization journey
Has your R&D organization created a road map to support personalization? Start a discussion with your team about the current and desired state of R&D processes using the framework with this white paper.