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Supply Chains, Big Data, and Point-of-Sale for EDA and IP

These issues were addressed by supply-chain, product-lifecycle-management, board-design, and chip-design services companies.

We live in a tumultuous world in terms of disruptive technologies, natural disasters, and global politics. Do chip designers need to worry about such seemingly external influences, as manifested by the global semiconductor manufacturing and supply chain? What help will come from “Big Data” analytics? Will EDA/IP (chip companies) ever be this tightly coupled with end-product manufacturers? I asked these questions of professionals in the manufacturing-supply-chain, product-lifecycle-management (PLM), and board- and chip-design services industries, respectively: Geoff Annesley, CTO at Serus; Brian Haacke, High Tech Industry Sales Director, Dassault Systemes; Michael Ford, Marketing Development Manager, Mentor Graphics–Valor; and Naveed Sherwani, President and CEO of Open Silicon. What follows is a portion of their remarks. –JB  

Blyler: Do chip designers really need to worry about the seemingly external influences of the global semiconductor manufacturing and supply chains?

Haacke: Designers do care about manufacturing with a primary focus on the impact of design rules provided by the foundries. The more design rules for which they are compliant, the more flexible they can be when choosing a foundry – and mitigating risks if some natural disaster impacts one foundry over another. Regarding supply-chain influence, there are many aspects to consider. Designers would not be impacted by material-supply disruptions because they typically do not “design in” any of the materials used in manufacturing. However, a closed-loop feedback to designers on manufacturing test results can improve responsiveness to design-related issues impacting yield ramp-up – especially if that feedback is tied to requirements and design intelligence.

Sherwani: It doesn’t require an earthquake or other natural disaster. In the coming move from traditional single-die chips to the era of 2.5-dimensional (2.5D) stacked dies, everything changes. With 2.5D, naked dies have to be tested, placed on interposers, and then positioned into a single package. The industry has never tested or sold anything like this before. I think it will disrupt the normal supply chain and its well-understood chain of command.

Annesley: Design needs to be linked to execution in the global market. You need a feedback mechanism for companies to decide the best price and combination of packaging and manufacturing processes that result in the lowest-cost chip. That is a good example of tying back execution data to the design process and vice versa. For example, you have the material information for your design – be it chip or board. You may have alternates that you need to use (e.g., due to natural disasters). It’s important for companies to track what actual alternates were picked for every component build. Then they will have traceability and accountability with respect to the specifications.

Ford: Designers are motivated to create a product that meets the criteria set in terms of technologies, materials, costs, quality, life expectancy, etc. There is significant influence on this from the manufacturing-production side, which – if not known by the designer – can result in product variations and the product not living up to expectations. Designing a product with some knowledge of the materials to be used and the actual production environment would allow the designer to design-in features that promoted better production quality, lower manufacturing cost, or reduced variation. Typically, though, this does not happen except in rare cases, as the technologies of material choice and manufacturing capability are not visible in a way that designers can understand. This is a clear opportunity for improvement.

Blyler: One supply-chain trend is the increased use of “Big Data” analytics to allow companies to connect between very different databases. In doing so, they can discover clues to improve supply-chain performance. Comments?

Haacke: “Big Data” analytics isn’t just a good idea. Nor is it just about connecting disparate data sources.  To be competitive, companies must be able to have visibility into their supply-chain data and make informed decisions based on the intelligent correlation of requirements, design, simulation, test results, and yield data. Connecting data sets is a start. Yet it is the marriage of operational and design intelligence that enables effective analytics to improve traceability, root-cause analysis, and time-to-yield ramp-up.

Ford: This can be useful. The real issue today is that the end-product distribution chain is shrinking, due to Internet sales and quickly changing fashionable technology products. This leads to many product variations – the changing demand profile of which comes closer to the factory than in the past. Factories are then asked to be agile, supplying different quantities of products with short notice of changes. This really puts pressure on their supply chain to source materials more quickly and effectively. Otherwise, there is a large increase of inventory at the factory, which cripples the operation on costs. Managing the changing demand from the customer and translating it into short-term raw-material availability is a growing issue today.

Annesley: Data mining and analytics are necessary to do predictive analysis (e.g., to foresee shortages in the supply chain). The resulting operational metrics include such things as yield, test, cycle-time, and on-time delivery trending – all the actuals on how you are performing. The real-time metrics and calculations can be used to do alert notifications (e.g., when you are drifting from your inventory targets). Then there is the longer term, where we collect the statistics on how the supply chain is used.

Blyler: Another trend is the use of point-of-sale (POS) data from retailers to adjust supply chain and manufacturing. Will EDA/IP (chip companies) ever be this tightly coupled with the end-product manufacturers?

Haacke: This is a good question – one that I’ve gone back and forth on. Ultimately, I don’t see much relevance to POS [as related to direct business-to-consumer (B2C) chip sales] being of any significant source of demand input to EDA/IP companies. The coordination required to track this data through every device – using a given chip – would be an enormous effort. So I don’t think there is any near-term future in which they are tightly coupled. However, I do see other possibilities for these companies to anticipate the demands in the marketplace by monitoring the end-consumer “experience” with products that contain their chips and/or IP. This data could be used to anticipate how consumers and competitors will act in the future.

Today, I think “social listening” may not be obvious to companies – especially the further down the supply chain they are from the end consumer. Still, with the right tools in place, EDA/IP companies can add the thoughts and ideas of their customers and competitors to their pool of “Big Data.” This data could then be part of their analytics and correlation of cause-and-effect events that drive effective decision-making and produce competitive advantages.

Ford: I am not sure about chips themselves. But ultimately, the answer would be “yes.” Still, the issue comes down to agility and the resistance to making changes. In printed-circuit-board (PCB) production – with good management tools – we can manage the changes to schedules and allocation of operations to work orders as demands change. For the chip areas, I think it will depend on how agile the processes are to be able to adjust volumes (move to alternate machines or reassign production cells). 

Blyler: Thank you.

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