Flexibility and Manufacturing Productiveness: Half 1 – Rigid Automation and its Penalties


Veo Robotics LogoOver the past couple of months I’ve been banging our drum concerning the significance of flexibility in manufacturing, the worth of flexibility in manufacturing, and why human-machine collaboration is a technique to supply this flexibility.

All of it comes all the way down to the truth that manufacturing as at present practiced — with poor human-machine collaboration — could be suboptimal and never sufficiently aware of the long-term developments of shorter product life cycles and growing product range. In different phrases, the inflexibility of producing right now is dear.

The present incremental mannequin of producing productiveness enchancment (for instance, kaizen1) will not be sufficient to generate worth and long-term development. Productiveness in manufacturing has hit a wall and leaping over this wall would require novel approaches2 to introducing flexibility in factories and workshops.

Virtually, we might count on some proof of this inflexibility to point out up within the numbers — within the macro, as proxied by productiveness statistics, and within the micro, with poor factory-level economics. Within the dialogue that follows we discover some high-level findings on how productiveness development has stalled throughout the economic system—significantly in manufacturing—earlier than we transfer on to a dialogue on the micro degree in a subsequent publish.

Productiveness Progress: A Basic Macro View
VEO Robotics SideMeasurement of productiveness and productiveness development depends on the classical economist’s “development accounting” toolbox, first developed by Robert Solow within the 1950s. In its easiest formulation, GDP development (or development in a part of GDP) can come from:

Will increase within the amount of labor;
Will increase within the amount of capital;
A rise in development not defined by will increase within the portions of labor or capital, referred to as “complete issue productiveness,” or TFP. This “measure of our ignorance” is a metric that captures the interplay between the opposite elements of labor productiveness in a roundabout way measured or measurable.

This TFP (also referred to as Multifactor Productiveness, MFP) describes the effectivity beneficial properties (or losses) related to development (or decline) in output that’s not a results of adjustments in measured inputs. It relates the change in an business’s actual output to adjustments within the mixed inputs utilized in producing that output. Will increase in TFP may result from enhancements in know-how (e.g., higher software program or simpler automation), enhancements in managerial practices, a extra educated workforce, reallocation of sources from sectors which can be much less productive to those who are extra productive, and different unmeasured (or unmeasurable) components.

TFP and Productiveness
Northwestern College professor Robert Gordon, in his monumental 2016 ebook “The Rise and Fall of American Progress: The US Normal of Dwelling Because the Civil Struggle,”three postulates that TFP is the important thing to the puzzle of low productiveness development.four The opposite drivers of development are exogenous and can fluctuate with hours labored, funding charges, and enterprise cycles. Solely TFP development is a transparent indicator that labor productiveness is rising completely and independently of its drivers.

US total factor productivity growth over different time periods.

Determine 1: US complete issue productiveness development over totally different time intervals. Supply: Determine 17-2 of Robert Gordon’s “The Rise and Fall of American Progress: The US Normal of Dwelling Because the Civil Struggle.”

Gordon asserts that the surges of TFP between 1920–1970 and 1994–2004 are anomalous and that TFP ranges between 2004–2014 are a greater indication of the long run (Determine 1). The 1920–1970 numbers could be attributed to the Second Industrial Revolution’s basic objective applied sciences (GPTs): electrical energy and the inner combustion engine,5 and the opposite bump in 1994–2004 was pushed by “an unprecedented and never-repeated fee of decline within the worth of pc velocity and reminiscence and a never-since-matched surge within the share of GDP dedicated to funding in data and communication know-how (ICT).”6

It’s true that these two intervals skilled drastic technological adjustments which can be largely answerable for spikes in TFP. However, counter to Professor Gordon’s stance, the truth that these developments had been unprecedented doesn’t preclude one thing comparable occurring sooner or later. It’s fully doable that we are going to expertise one other technological renaissance that produces a spike in TFP. Take into account, for instance, Thomas J. Watson’s and even Invoice Gates’ pronouncements on how massive the pc market may very well be.

Professor Gordon doesn’t dispute the frenetic tempo of innovation in digital know-how, robots, automation, and AI. Nonetheless, he notes that the quick tempo of innovation doesn’t essentially imply these improvements are having an affect on the expansion charges of labor productiveness and TFP. If we’re to belief Professor Gordon, productiveness development within the general economic system has stalled, it has hit a wall. How does that search for manufacturing?

When rigid automation collides with manufacturing developments requiring extra flexibility, productiveness on the macro combination suffers.

Manufacturing Productiveness Progress—Hitting a Wall
TFP development within the US manufacturing sector has been challenged over the previous few many years. Determine 2 from the US Bureau of Labor Statistics (BLS) reveals YoY adjustments in TFP, with a noticeable slowdown within the post-2004 interval.7 TFP within the manufacturing sector grew by a mean of two.zero% per yr from 1992 to 2004, with producers growing their manufacturing of products with comparatively fewer inputs. From 2004 by means of 2016, nonetheless, manufacturing TFP declined by a mean of zero.three% per yr. The explanations for this slowdown in TFP development have been extensively studied and have been attributed by many superb economists (together with Professor Gordon above) to the “one off” semiconductor and IT revolution pushed by (now-waning) Moore’s Legislation.eight

US total factor productivity YoY percent change 1993-2016.

Determine 2: US complete issue productiveness YoY p.c change 1993-2016.

In a unique lower, the crew on the BLS disaggregate the elements of producing TFP on the business subsector degree. They conclude that, since 2004, semiconductors, digital elements, and pc and peripheral tools manufacturing have been the largest contributors9 to TFP slowdown, per the “finish of Moore’s Legislation” thesis.

Nonetheless, trying on the common TFPs for a choose variety of manufacturing subsectors, particularly, sturdy items resembling vehicles, white items, and different capital tools (Determine three), we are able to see that there was a generalized productiveness slowdown comparatively impartial of that in computer systems and semiconductors. Clearly, the decline in manufacturing TFP will not be pushed solely by pc and semiconductor manufacturing TFP, so we should dig a bit deeper.

Average TFP 1992-2004 and 2004-2016 for US durable goods manufacturing.

Determine three: Common TFP 1992-2004 and 2004-2016 for US sturdy items manufacturing.

Luckily, the BLS analysis crew thought the identical. They carried out an extra piece of useful evaluation trying on the financial components of manufacturing utilized in manufacturing—capital, labor, supplies, bought providers, and power.

Elements of Prodcution
Beneath the speculation that interplay among the many components of manufacturing impacts TFP, they examine the shares of the components of manufacturing between 1992–2004 and 2004–2016 (Determine four). And what they discover is that the share of capital grew from 21% to 24%, whereas the share of labor dropped from 32% to 25%, which is per elevated (however not essentially extra environment friendly) automation. Moreover, the share of supplies and bought providers mixed grew from 45% to 49%, which is per elevated outsourcing and exterior buying of supplies and providers.

Based on the BLS crew, these adjustments in issue shares weren’t pushed by only some massive industries. Of the 86 manufacturing business teams analyzed, solely 14 industries reported development within the common share of labor in manufacturing between the 2 intervals. Extra importantly, adjustments in issue shares seem like associated to adjustments in TFP development. The common annual fee of 2004–2016 TFP development was decrease than throughout 1992–2004 in 62 out of the 86 manufacturing industries. Of those 62 industries, 53 had decreases within the common factor-share weight of labor, which is probably going a results of greater automation and outsourcing.10

Factor of production shares 1992-2004 and 2004-2016.

Determine four: Issue of manufacturing shares 1992-2004 and 2004-2016.

So we are able to infer that the alternative of capital for labor in manufacturing (i.e., decrease labor depth in manufacturing) is a wrongdoer within the TFP slowdown of the latest many years. The BLS crew factors out that if TFP development is a results of innovation, and if extra of the manufacturing course of occurs with out the enter of employees, the result’s much less manufacturing innovation.11

However this shift towards extra capital and fewer labor can be indicative of upper automation ranges — and, as we wish to repeat, an excessive amount of automation, or the mistaken form of automation, can be rigid. Solely when this extra automation combines with labor to generate greater productiveness (that’s, greater TFP) will automation be a web achieve.12 When rigid automation collides with manufacturing developments requiring extra flexibility, productiveness on the macro combination suffers.

In Half 2 of this text, I’ll revisit a few of the micro proof now we have beforehand offered and study it by means of the lens of productiveness.

Editors Notice: Robotics Enterprise Overview want to thank Alberto Moel for permission to publish this piece (frivolously edited). The unique could be discovered HERE. All views, ideas, and opinions expressed therein belong solely to the writer.

Notes

1 Approaches, in fact, such because the Veo FreeMove system.

2 Professor Gordon’s ebook is extraordinarily well-articulated and supplies an impressive survey of know-how innovation during the last two centuries, however typically extrapolates historic incidents to future dynamics—which is to say, take its predictions with a grain of salt.

three And presumably rising inequality, however a dialogue for an additional day.

four At an annualized development fee of 1.89%, TFP rose 2.5x within the interval 1920–1970.

5 The Second Industrial Revolution created a surge in productiveness development that lasted for 50 years, whereas the arrival of widespread computing had a weaker and extra delayed impact, lasting solely about 10 years. This delay was well-noted by Robert Solow in 1987 along with his basic quip that “you may see the pc age in every single place however within the productiveness statistics.” It took some time, however they had been certainly mirrored.

6 Michael Brill, Brian Chansky, and Jennifer Kim, “Multifactor productiveness slowdown in U.S. manufacturing,” Month-to-month Labor Overview, U.S. Bureau of Labor Statistics, July 2018. The info and a few of the analyses on this part are taken from this paper.

7 See, for instance, David Byrne, Stephen Oliner, and Daniel Sichel, “Is the knowledge know-how revolution over?” Worldwide Productiveness Monitor, no. 25, Spring 2013, pp. 20–36.

eight The crew on the BLS additionally discovered that prescribed drugs, medicines, and petroleum and coal merchandise had been vital components within the TFP slowdown. Fortunately, they supplied a useful appendix displaying common TFP development charges for particular subsectors.

9 A crew from the Brookings Establishment additionally recognized outsourcing as a causal issue within the TFP slowdown: Susan Helper, Timothy Krueger, and Howard Wial, “Why does manufacturing matter? Which manufacturing issues? A coverage framework,” Brookings Institute, February 2012. A number of different researchers from Cornell College’s ILR Faculty credited producers’ outsourcing to staffing providers (now kind of full) as having contributed as a lot as or much more than IT to manufacturing labor productiveness development within the 1990s to 2006: Matthew Dey, Susan N. Houseman, and Anne E. Polivka, “Producers’ outsourcing to staffing providers,” ILR Overview, vol. 65, no. three, 2012, pp. 533–59.

10 There may be empirical proof for this. For instance, Baptist and Hepburn discover that greater labor enter (and decrease intermediate inputs resembling supplies and power) is positively (and presumably causally) associated to TFP development (Simon Baptist and Cameron Hepburn, “Intermediate inputs and financial productiveness,” Philosophical Transactions of the Royal Society, January 28, 2013). This argument can be made by David Mindell in his 2015 ebook “Our Robots, Ourselves,” which argues in opposition to “lights-out” factories—if no people are concerned within the manufacturing course of, there could be no manufacturing innovation and enhancements.

11 As that is the Veo Robotics weblog, we’d wish to remind our variety readers that the Veo FreeMove system is a type of capital funding that’s designed to make human-machine collaboration extra productive. In different phrases, to make higher use of capital and labor to extend TFP.

Alberto Moel is the VP of Partnerships and Strategy at Veo Robotics

Alberto Moel, VP of Partnerships and Technique, Veo Robotics

Alberto Moel is the VP of Partnerships and Technique at Veo Robotics, the place he’s answerable for business partnerships, mental property, market and aggressive evaluation, and firm technique. Previous to Veo Robotics, Moel was a Senior Analysis Analyst at Sanford C. Bernstein in Hong Kong, the place he lined Asian excessive know-how corporations within the automation, robotics, and manufacturing know-how sectors.

Beforehand as a principal within the Hong Kong workplace and co-leader of the Tokyo workplace of Monitor Group, Moel specialised in know-how technique and company finance for Asian high-tech corporations. As well as, he has been a Professor and Lecturer on the Hong Kong College of Science and Know-how and at Harvard Enterprise Faculty, co-founded a profitable Brazilian hedge fund, and labored at JP Morgan (New York and Mexico Metropolis), Toshiba Company in Japan, and IBM Company in New York, Cambridge, and Zurich. Moel has SB, SM, and ScD levels from MIT, and an MBA from Harvard Enterprise Faculty.

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