4 min read

Digital Manufacturing and Labor

By Mike Kinder on Dec 15, 2017 5:41:00 PM


The large-scale redistribution of operational intelligence is an underrated consequence of digital manufacturing and other IoT based operational capabilities.  We previously explored how to think about Digital Manufacturing as a specific category of use cases and this installment will pick up specifically on the labor implications of such use cases.


Any emerging digital manufacturing solution is going to require significant investment and implementation costs.  While the costs will vary dramatically, the cash outlay will always precede time to value.  In fact, many businesses will be challenged to meet 1 or 2-year payback hurdles and thus required to maintain a longer-term manufacturing strategy vision.  Therefore, getting the business case right is critical for all manufacturing companies.

While most operations leaders grasp the digital manufacturing value creation potential immediately, quantifying the business case can be a challenge.  Because new IoT based capabilities impact all manufacturing metrics and most value drivers, business cases must be able to monetize the benefit of higher absorption, improved quality, better safety compliance, increased throughput, better inventory visibility, and increased labor productivity – that is, the value creation potential in the business case has to be the sum of these incremental operational improvements.  In the eyes of many senior executives and finance types, this is a tougher sell than a direct cost out project with a more straightforward benefit calculation.


While all five manufacturing metrics – productivity, inventory, service, quality, and safety – stand to improve drastically through digital manufacturing, the direct impact on the P&L is trickier to quantify.  At the end of the day, the direct P&L impact is going to lie with asset productivity, cost of quality, and labor productivity.  The first two are inherently linked into the digital manufacturing ecosystem: capabilities like machine-to-machine communication and predictive maintenance are directly embedded in the solutions that are coming to market now like GE Predix and Rockwell Automation’s Connected Enterprise.

However, the labor productivity aspect is more of an indirect benefit and viewed as a byproduct of the overall productivity enhancements.  Realizing the labor productivity benefit is going to be harder than most companies realize; this a real problem since it is commonly the highest addressable cost bucket and the biggest driver of the business case.


Let’s start with a simple thought experiment by considering how labor productivity is realized under the following digital manufacturing scenarios:

a.  New machine capabilities increase equipment uptime and throughput

b.  Plant wide material flow visibility anticipates where bottlenecks are about to form in the plant

c.  Machine learning capabilities automatically level load the production schedule in real time

d.  RIF based track and trace solutions monitor the movements of employees and material

e.  Smart cameras identify value add versus non-value add work

f.  Digital work instructions, wearables, and operator alerts show workers what to do in real time

These example use cases all point to some level of improved labor productivity but the quantified benefit is less than obvious.  For example, higher machine throughput will improve absorption and potentially decrease labor cost per unit and digital work instructions should add an element of operator enablement, and thus individual productivity, as it relates to facilitating process steps and identifying the right material and tools to use.  The key takeaway here though is that, under the current thinking, labor productivity is a lagging benefit – one that comes to fruition as a byproduct of these use cases rather than the primary focus area.


Without addressing the primary constraint with today’s labor paradigm, a digital manufacturing solution will always be sub-optimized.  There are two primary weaknesses:

1.  The imbalance between asset flexibility and labor flexibility

2. The lack of direct labor benefit in the digital manufacturing business case

The flexibility imbalance is really where the system falls apart.  In the ecosystem of the future where production planning, customer service, and plant equipment can all respond and adapt in real time, labor becomes a prohibitive bottleneck.  Labor has to respond with a complimentary level of flexibility and agility to enable the system to achieve its full potential.  Couple that with the lack of direct labor benefit in the business case and we have an obvious obstacle.

At Veryable, we believe that the answer lies with harnessing the power of the redistribution of operational intelligence to enable a fully variable and on-demand labor model.  A piece-work based on demand labor capability is the key that fully unlocks the digital manufacturing explosion within the next 5-10 years.  With an on-demand labor model, companies can build in productivity from Day 1 and establish essentially an infinite amount of flexible capacity to match the new capabilities embedded in the rest of the solution architecture.

Rather than implementing digital work instructions and trying to figure out to derive a labor benefit, start with the labor benefit available from an on-demand model and figure out how to enable an untrained resource to be fully productive using only these instructions.  Rather than using cameras to identify non-value added time, use recorded information to identify quality issues in real time or illustrate process steps in front of on-demand resources.  In short, harness the new intellectual property and tailor it to enable anyone to be productive.

We believe this to be the number one opportunity for digital manufacturing adoption and success, as well as the manufacturing sector at large.  Can you imagine being able to flex on a day’s notice to double or triple your labor capacity?  Can you imagine an economy where workers’ job security is not tied to one specific job?  Can you imagine an economy where every worker is relevant, and a prospective resource, for every manufacturing or distribution company?  We can.

Mike Kinder

Written by Mike Kinder

Mike brings over 15 years of manufacturing and supply chain experience within operations consulting and industry. With experience at PwC and GE in manufacturing strategy, operational transformation, and digital manufacturing, he is an expert in Lean and Six Sigma and digital manufacturing.