Signify Uses aPriori to Drive Procurement Digitalization
As a key part of its digitalization strategy, Signify implemented automated should cost analysis for its procurement team, integrating robust digital manufacturing simulation with the sourcing process while requiring minimal additional effort from users.
In this case study, we look at how Signify is using automated should cost analysis to drive the greatest savings possible (and is now expanding usage to R&D).
The ProblemExpand Should Cost Analysis by 8x to Digitize Procurement
The SolutionUse Digital Manufacturing Simulation to Democratize Should Cost Analysis
Who is Signify?
Headquartered in the Netherlands, Signify is the world leader in lighting for consumers, professionals, and the internet of things. They offer conventional, LED, and connected lighting solutions. Their innovative lighting solutions are supported by 37,000 employees spread across 70 countries. Their 2 billion+ LEDs sold have saved the equivalent carbon emissions of 23 million cars.
Problem: Expand Should Cost Analysis by 8x to Digitize Procurement
As part of its commitment to global leadership in the lighting industry, Signify wants to ensure best-in-class procurement practices, acquiring parts with the best cost, quality, and availability possible. To do so, their procurement team would need to dramatically expand its capabilities for generating should cost analyses for its products purchased from external vendors.
A “should cost” refers to a projection of a given component’s cost if efficient manufacturing and distribution practices are followed. A robust understanding of what a product should cost can help negotiate lower prices, pinpoint opportunities for cost reduction, and even foster more collaborative supplier relationships. Manual should cost estimation, however, is a time-consuming task. The team at Signify knew they needed an automated approach in order to hit their goal of increasing should cost analysis by 8x in order to optimize costs for their diverse portfolio of products.
Signify’s procurement team began a partnership with aPriori in 2017 to generate select should cost models for mechanical parts. This initial engagement proved successful, and Signify believed that aPriori was the right tool to propel their digital procurement strategy forward.
Solution: Use Digital Manufacturing Simulation to Democratize Should Cost Analysis
To reap the benefits of aPriori’s manufacturing insights, Signify procurement professionals only need to upload a 3D CAD file through a cloud-based web application. By simulating manufacturing in a digital factory, aPriori provides fast, highly detailed should cost models that can be configured to reflect the diverse cost structures of real-life suppliers. A procurement professional can request a should cost analysis with only a few additional clicks. The requester receives an e-mailed report with a should cost summary, a more detailed cost breakdown in an attached Excel file, and a link to detailed cost and manufacturability analysis within aPriori.
No supplier is perfect, and not every supplier is able to meet the should costs identified in aPriori. But, by providing a detailed breakdown of why a component costs what it does, detailed should cost models are the perfect foundation for more constructive, collaborative, and fact-based supplier negotiations centered on root causes rather than confrontations or hard bargaining tactics. With data from aPriori, Signify sourcing professionals can collaborate with suppliers to develop a more efficient cost structure that is mutually beneficial. In this way, enhanced should cost analysis can help Signify and its suppliers alike stay highly competitive.
Results: Cost Transparency for Controlled Cost and a Competitive Edge
As part of Signify’s general pursuit of operational streamlining through automation, they leverage aPriori to democratize should cost analysis. This strategy is rooted in their knowledge that automation creates a pipeline capable of handling a greater volume of should cost requests than ever. Accelerating adoption across the organization will be vital to keeping this pipeline full and achieving the greatest possible savings. To do so, they wanted to ensure that every procurement professional is capable of using aPriori to model should costs–even those with zero knowledge of the digital factory.
Their first focus was to make the software easy and fast to use for procurement professionals. Instead of a dedicated should-cost workflow, any buyer can now generate a should-cost model with a few clicks and less than one minute (even automatically attaching it to the RFQ sent out to vendors). This process leaves the full breadth of the digital factory in the background for users without a need for custom configurations.
The procurement team’s second step was to create an automated process for generating should cost models for Signify’s large portfolio of existing parts. This automated process is used to identify outliers, allowing the procurement team to focus on bringing down costs for components with the greatest possible potential for savings. As a third step, moving forward, Signify will be focused on extending should cost modeling capabilities earlier in the product development process, during the R&D phase. Over the long term, aPriori will support Signify’s goal of performing should cost analysis for every product through a simple, automated process.
Looking forward, Signify also has plans to leverage aPriori’s sustainability features to build a Design to Sustainability Culture, integrating carbon costs with their automated should cost modeling strategy.
Learn more about how Signify unlocks the extraordinary potential of light and why they have chosen to partner with aPriori in this 12-minute video clip from our recent Manufacturing Insights Conference.