The Computing of Manufacturing Data Infrastructure
Assembly Line
The Future of Manufacturing: Data as Infrastructure
Modern manufacturers are deeply understanding that data is the critical infrastructure that will continue to transform their entire manufacturing process. Fifteen years ago, software engineers had a similar problem: they had to spend hours manually collating data across many servers and applications to get insight into their product performance. Today, software engineers have data infrastructure that enables them to immediately see a server is down or that an application is underperforming – and this is often some of the first tooling new organizations put into place.
While software engineering has embraced and operationalized data, manufacturing is still trying to harness the massive amount of data produced in building new products, while struggling to put it to use. A recent Instrumental survey found that 63 percent of engineers are still relying on manually compiled reports from factory partners to know what’s going on in their factories and that 2 in 3 aren’t satisfied with the access they have to their manufacturing data. There’s a clear demand and need for better data tools that give access to critical data.
The Science of Production
Getting to this state of control was an iterative process - each time something was fixed, more data was collected, revealing new causes that the previous issues had masked. Each iteration proceeded the same way - plot the data on a control chart, look for patterns, locate the issue and make any necessary process adjustments.
Construction, once again, is a world that pushes production optimization difficulties to 11. All the things that make science hard to do in a manufacturing environment are even harder in construction. For one thing, construction has a much higher rate of process changes - every new project means new workers, new environmental conditions, new materials, new construction details, etc. Not only does this introduce new causes to the process, but it changes (if only slightly) what the basic process is. As we’ve seen with learning curves, it only takes very small disruptions to ‘reset’ what workers know about a process, and these disruptions occur much more frequently in construction.
Price optimization notebook for apparel retail using Google Vertex AI
One of the key requirements of a price optimization system is an accurate forecasting model to quickly simulate demand response to price changes. Historically, developing a Machine Learning forecast model required a long timeline with heavy involvement from skilled specialists in data engineering, data science, and MLOps. The teams needed to perform a variety of tasks in feature engineering, model architecture selection, hyperparameter optimization, and then manage and monitor deployed models.
Vertex AI Forecast provides advanced AutoML workflow for time series forecasting which helps dramatically reduce the engineering and research effort required to develop accurate forecasting models. The service easily scales up to large datasets with over 100 million rows and 1000 columns, covering years of data for thousands of products with hundreds of possible demand drivers. Most importantly it produces highly accurate forecasts. The model scored in the top 2.5% of submissions in M5, the most recent global forecasting competition which used data from Walmart.
Virtual Factory Tour―Automobile Production Plant
Where is 'The Edge' and why does it matter?
The Edge is not a place – It is an optimization problem. Edge computing is about doing the right things in the right places. As with all optimization problems, getting to the “right” answer requires considering a number of tradeoffs that are specific to your situation and then applying the right technology to maximize the benefits for the cost you are willing to pay.
Part of what makes Edge confusing is that definitions of “The Edge” tend to focus on technologies rather than on use cases. Since use cases span a very wide range of requirements and the boundaries between those use cases don’t map directly to technologies, definitions in terms of technology can be difficult to use.
DeFi: The Next-Gen Revolution in Supply Chain Financing
For blockchain-based supply chain financing, the principle is to leverage distributed accounting to allow more ecosystem nodes to participate and contribute the data to achieve more point-to-point interaction. Because all data on the blockchain is verified and traceable, it’s becoming an important asset in capital circulation.
Looking to the not-so-distant future, DeFi systems, if enabled with NFT (non-fungible token) based currency, can not only confirm the ownership, but also facilitate tracking. Additionally, the transaction of various NFT assets can be the basis for subdivided financial market markets accelerating the leveling of the competitive landscape.
For example, the consumers of a product can invest in a token backed up by a portfolio of the Consumer Goods company’s invoices and inventory. They can increase their investment further by using a bond token as collateral in DeFi lending protocols to obtain more liquidity. Additionally, the Consumer Goods company’s suppliers will have more efficient ways to obtain funding and optimize cash flow.
Surge Demand
The time has come for electric vehicle companies to start producing cars. Industrial venture arms call for innovation including Toyota Ventures seeking Industry 4 technologies, the Amcor Lift-Off initiative supporting sustainable packaging solutions, and AmerisourceBergen funding emerging healthcare companies.