Manufacturing Optimization Systems for the Postmodern COVID-19 Era

Ford and GE Healthcare will manufacture 50,000 ventilators in less than 100 days as GM and Ventec produce 10,000/month and Dyson supplies 15,000 in just 10 days. Meanwhile, shoemaker New Balance is making face masks, distilleries in the United States and England are producing hand sanitizer, and 3D printing company Stratasys has 150+ customers additively manufacturing Personal Protective Equipment (PPE). If anything is clear in the postmodern COVID-19 era, it is that the new critical path for manufacturing decision makers comes down to resilience, flexibility, and agility.

Business Intelligence (BI) systems collect, transform, and present information. An optimization system needs to do all those things in a specific way in addition to closing feedback loops. Together, these features represent a new solution category that ABI Research refers to as Manufacturing Optimization Systems (MOS).

A MOS is a collection of software components that work together to do four things:

1. Wholly Collect Data from Key Sources in the Manufacturing Process: “Wholly” means for a given source, 100% of data is collected without sampling. This is a required foundation to enable the other steps to work.
2. Intelligently Transform Data Using Active Algorithms: Active algorithms constantly crawl datasets to identify shifts, trends, anomalies, and other important data events.
3. Contextually Present Data in Ways That Are Relevant, Timely, and Actionable: The presentation must be widely available and accessible given the likelihood of multivariate stakeholders.
4. Close Loops in the Manufacturing Process: A MOS not only pulls data (for input) but also enables individual optimization to be pushed out to a single process, the entire supply chain, and everything in between.

A MOS is essentially a continuous version of what global consulting firms like Bain and BCG would do on a one-time basis for their clients.

Companies doing this today include Tulip and Drishti for “human process” optimization and Instrumental for “product data” optimization. Sight Machine and Arch Systems are providing implementations of #1-3 but don’t yet do #4.

At full implementation and realization, a MOS supersedes an MES.

As result of COVID-19, there has been a seismic shift in the buying needs of manufacturers. Capabilities like remote monitoring, predictive maintenance, and virtual support are now key requirements. So too is the ability to pull data throughout the supply chain to optimize the manufacturing process. But none of this is possible without cloud. Consequently, ABI Research expects the use of cloud for production applications will accelerate dramatically as manufacturers are further pressed to improve yield, reduce returns, and better utilize human resources. Next is flexibility, agility, and speed.

A good rule of thumb is that faster supply chains mean less product in channel and, therefore, a faster cash conversion cycle[1]. A faster cash conversion cycle typically translates to more cash on hand and the ability to reallocate funds toward other investments. The drawback is supply chain volatility, since it implies operating on a Just-in-Time (JIT) basis. CE manufacturers like Apple and Samsung as well as automotive manufacturers like Ford and GM are some of the exemplars for fast cash conversion cycle management (in fact, Apple runs a negative cash conversion cycle).

The flexibility and agility afforded by a cloud-first, MOS-friendly, fast-cash strategy will pay dividends at all intervals. Today, it is in the form of better remote visibility and less travel to facilities; mid-term, it is an accelerated NPI process; long-term, it is a leading market position built on key technologies and strong business fundamentals. MOS, like cloud, is about to cross the chasm.

[1] The cash conversion cycle (or “cash-to-cash cycle”) is the time between paying suppliers and receiving payment from customers.