Friday, February 20, 2015

Climate change and manufacturing

Or, when you’re in a hole … stop digging 

The subtitle to this posting, the so-called “first law of holes,” is attributed to various sources (earliest going back to 1911 in the Washington Post) and is usually interpreted as “if you find yourself in an untenable position, you should stop and change, rather than carry on exacerbating it.” (from Wikipedia)

China became the world’s biggest greenhouse gas emitter in 2006 overtaking the US due primarily to electricity generation and industrial processes. However the per capita carbon footprint of a Chinese person is still much lower than the average US person. This is not good. Increasing industrialization and the slippery slope to more consumption.

So, what’s the hole and how do we stop digging?!

Michael Oppenheimer, a Princeton University climate scientist, recently commented on a public radio talk program broadcast on KQED in the San Francisco Bay Area (Michael Krasny’s  Forum program) about the recent release at the end of last year by the United Nations of one of its bluntest and bleakest reports to date on the dangers of global warming. The UN study, prepared by he Intergovernmental Panel on Climate Change (IPCC) warns that the world must cut nearly all greenhouse gas emissions by 2100 in order to head off the worst effects of climate change. U.N. Secretary General Ban Ki-moon urged world leaders to act, saying that “the science is unambiguous”.

The IPCC study now argues that humans are affecting the climate with 95% certainty (this is the same degree of certainty with which the medical community links smoking to lung cancer … there is always a possibility that it is not the case but the preponderance of evidence indicates there is a cause-effect relationship).

By way of background, the aims of the IPCC are to assess scientific information relevant to:

    1    Human-induced climate change,
    2    The impacts of human-induced climate change,
    3    Options for adaptation and mitigation.

One of the first items in the IPCC “Summary for Policymakers” is the following clear and concise statement:

“Human influence on the climate system is clear, and recent anthropogenic emissions of greenhouse gases are the highest in history. Recent climate changes have had widespread impacts on human and natural systems.” 

This view (and statements like the one above) has come under fire … specially in the US … where for some odd reason there is a movement against education, logical scientific thought and reason, in favor of the opinions that might be politely characterized as “less informed.” It’s not clear why that is. Everyone has a right to their own opinion, of course, but consideration should be given to facts and reality in coming to it, or one would think.  Maybe the truth is hard to face, harder to accept and plan for and harder still to accept that there are others that may know more about a subject than we do. Education used to help with this. But, many of the same anti-science (or at least anti-this science) folks also are not big supporters of education. So it goes. 

Two things it would be good to remind ourselves of … 1) there is value in scientific expertise, properly and transparently carried out and 2) humans will always want to improve their status/affluence.

People say manufacturing productivity will save the day but labor productivity misses the point. We really need to think in terms of resource productivity. You may recall that we had this discussion in a posting in July 2013 as part of a discussion about the effective utilization of resources and how resource productivity, rather than labor productivity, might play into the argument ( This discussion included again a reference to the IPAT equation (and the attention given to the “T” part - impact per unit of GDP or the so-called technology term) and need to increase by factor of 10 this “productivity” to offset the growth of population and affluence.

How so?

Take a look at how affluence (and the pursuit of it) as measured by GDP/capita impacts energy demand/capita. The figure below, from McKinsey Global Institute’s (MGI) report in 2013 on Resource Revolution: Tracking global commodity markets shows the link between

the consumption of energy and the growth in standard of living (as represented by GDP/capita). As countries become more affluent their energy use grows. This is due to a couple of contributing elements - for example, acquisition and use of more and more products that use energy and other resources (think refrigerators, automobiles, televisions, plumbing, etc.), transition from agricultural based economies to industrially based economies and the generation of electricity to power all of this.

And, as it turns out, with the increasing levels of green house gases in the atmosphere from all this unbridled development the atmosphere gets warmer, polar ice starts melting and weather becomes more extreme. And for the unfortunate individuals living in low areas along major bodies of water (think Bangladesh) land is flooded and agricultural activities either are moved elsewhere or, more likely, abandoned. So, another driver then is the increased urgency of industrializing these economies to reduce the dependence on flood or weather challenged agricultural production - further aggravating the problem

That’s digging the hole faster.

How do we stop digging or, at least, slow down the rate of digging until we can stop?  One solution lies in the slope and amplitude of the above curve (meaning how fast it is rising and to what level does it eventually get?). We can see from the above figure that the US is already there, and higher, than any of our competitors. But others, developing (or emerging countries) are determined to climb the affluence path. And we cannot really stop them. 

But, is there any reason that the link between a certain level of affluence (as represented by GDP/capita) and the energy consumption to get to that level is fixed?  It is if we think in the same way of resourcing, making, distributing and disposing of products. If every country that is working to increase the affluence of its inhabitants follows the lead of those that have gone before we are stuck (still in the hole).

But why should they? The real question is how do we serve this inherent need but more effectively and not at the expense of the world - how do we stop digging?

If we can find a way to resource, make, distribute and, instead of disposing of products, extend, rebuild or re-use products wouldn’t that be better? And, if we can do this while maintaining (or increasing) the value of the product in the eyes of the market or the consumer wouldn’t that be one way to address the “T” in the IPAT? Meaning, driving the impact per unit of value (or GDP) lower.

From the manufacturing side, the things that can be adjusted to accomplish this include using cleaner (or renewable) sources of energy (same product value but at lower energy, hence, greenhouse gas, creation), using better manufacturing technology (same or better product value but at lower energy cost of production) to convert our materials into products, and better materials (reduced impact from extraction and processing of materials, less material, recovery of materials, etc.)

But there’s more! How can we implement these novel manufacturing impact reducing ideas not just in the countries that are already way up the curve (or “developed”) but those that are climbing? We should be able to implement production technology in emerging economies as well so they don’t have to create all the challenges we have first. This will be the topic of a future posting since, obviously, the technology must match the situation.

Wouldn’t this be better than just betting on productivity, traditionally measured, to save the day? Isn’t betting on productivity sort of like saying the solution is digging faster?

In fact, our friends at McKinsey are already worried about this. In a recent, January 2015 MGI report they ask the question with respect to global growth - can productivity save the day in an aging world? The manufacturing strategies mentioned above to reduce impact per unit of GDP will not work for service industries - a large part of the McKinsey study and many economies - but a more enlightened view of productivity, meaning resource productivity, will help drive these technologies for both emerging and developed economies alike.

So, not only will we stop digging … we’ll throw away the shovel!

Monday, January 5, 2015

Material flow, the butterfly effect and consumer influence

What is “your butterfly” up to?!

Past postings have included discussions of the Ricoh Comet Circle and the circular economy as reasonable representations of several (perhaps oddly) connected elements in a more holistic view of sustainable systems as they influence green manufacturing. Connections in the context of those discussions have been about “material” interactions and movement … what comes from where, what goes where, where is “away” when something is thrown away at its end of life?

Over the new year break a number of discussions came to light about the other side of the “circular economy” coin … influence and effects beyond processes, materials, transportation, energy, water and so on.
This needs some explanation. What is referred to here is the influence decisions make on other decisions, people, experiences, quality of life, etc. Behind the smooth interconnectedness of our global economy sits a complex infrastructure of people, places, things. Sometimes these are simply referred to as “the supply chain.” But it’s not that simple. More importantly, if it was that simple we’d not be addressing core values of the many actors in the supply chain.

Data, specially that collected at different speeds and representing different “views” of the enterprise from top to bottom as discussed in the last posting, is currently focused on these material interactions and movements.  As referred to in the previous blog posting on the digital revolution, communication speeds, computational capability and speed and the hardware spitting out the data from machines and systems are more common, less expensive and more reliable. But, do they tell us the whole story? Or, more significantly, what would be need (or how would we analyze this data and use it) to tell the rest of the story.

This definitely needs some elaboration! Let’s rely on an old analogy … the “butterfly effect.”  One can find lots about this on the web but, basically, according to WiseGEEK “the butterfly effect is a term used in chaos theory to describe how small changes to a seemingly unrelated thing or condition (also known as an initial condition) can affect large, complex systems. The term comes from the suggestion that the flapping of a butterfly's wings in South America could affect the weather in Texas, meaning that the tiniest influence on one part of a system can have a huge effect on another part. Taken more broadly, the butterfly effect is a way of describing how, unless all factors can be accounted for, large systems like the weather remain impossible to predict with total accuracy because there are too many unknown variables to track.” The site goes on to comment on the fact that there are skeptics that this really works (due, for example, to things in large systems that tend to dampen, or attenuate, effects) but they do argue that is is applicable to complex systems beyond the weather.

So, assuming here that the economy (circular or otherwise) or at least the manufacturing enterprise is less than or on the same order of complexity as the weather … and that we believe that small things or changes can affect large, complex systems, can we apply this to our discussion of green and sustainable systems? More specifically, can we refer to any such changes in terms of our interest in influences and effects?

If you do a “search and replace” for “flapping of a butterfly's wings” with “human effects or behavior” and as well “weather in Texas” with “sustainability”, respectively in the above definition of butterfly effect … it reads:

“ … the suggestion that the human effects or behavior [as part of a supply chain] in South America could affect sustainability, meaning that the tiniest influence on one part of a system can have a huge effect on another part. Taken more broadly, the butterfly effect is a way of describing how, unless all factors can be accounted for, large systems like sustainability [of a supply chain] remain impossible to predict with total accuracy because there are too many unknown variables to track.”


OK … let’s go with this for a bit to see where it takes us!  What are some of the butterflies that are at play here? How could human effects or behavior in, say South America, or China, or India, eventually have a huge effect on another part? The most obvious answers to this in the context of sustainability start with terms like “conflict minerals” and, then, slavery. Meaning, the "human behavior" affected by slavery, in this case undesired.

One of the major human effects (butterflies) that is increasingly brought up is the derivation of work related to supply, handling and production of materials, products or components, specially within the supply chain, from persons not paid, or lowly paid, or otherwise captive by a system that exploits them for labor and other services. According to Free the Slaves there are tens of millions of people in slavery today. They put estimates at from 21 to 36 million people worldwide. These people are forced to work without pay, under threat of violence, and they’re unable to walk away. They can be  found  in brothels, factories, mines, farm fields, restaurants, construction sites and private homes. Slavery is illegal everywhere, but it happens nearly everywhere according to Free the Slaves. Lisa Kristine, a photographer, has an excellent website dramatically documenting this. This is real.

Ones first reaction is “certainly I am not benefiting from or engaged in consumption that relies on slavery.” The links to slavery behind the products we buy and use is complex ... but there is a link. The story is told well at websites like Slavery Footprint which will calculate, based on the composition and origin of products one purchases and  uses - including many of the most reputable brands in the markets how many slaves work for you. For example, what about the cotton in the t-shirt, or exotic materials in the smart phone or coffee beans in our cup of espresso? or, closer to green manufacturing, the alloying elements in the solder used in electronics production? 

Slavery Footprint makes a bold statement in answer to this - “It’s the supply chain, stupid. And it’s a supply chain that enslaves more people than at any time in human history.” You can take a survey on the website that starts with where you live, what kind of place you dwell in (rooms, autos, etc.), your eating habits, your consumed products in the home, jewelry, what you wear, what leisure activities you engage in, and, specially, the type of electronic gadgets you own and use and ends up with an estimate of “how many slaves work for you.”

My number was 27. I have no idea how accurate this is … but it’s not likely to be zero … so this is not good.

So, what about butterflies and their effect? If one knows that slaves are contributing to the products they purchase and use due to, say materials (tantalum in a smart phone, cotton in underwear, constituents in food consumed or products (think cosmetics) used - and knowing the source of the materials and any slavery associated - for example from United Nations Labor Organization and its Global Estimate of Forced Labor), and then one finds out what are the brands that are the most prevalent in the use of these materials, and then stops buying and, more importantly, works with others to get more people to become aware how their purchasing behavior supports slavery, and then companies see the publicity or reduction of sales due to this awareness … and … finally, the companies change their purchasing behavior to source ethically … isn’t that a butterfly effect?!

It could happen! 

Here is one butterfly example - focusing on conflict minerals in the Congo - called “Raise Hope for Congo." Among other helpful information it has a simple description of how the 3Ts—tin, tantalum, tungsten—and gold move from the mines of eastern Congo all the way to your cell phone. These minerals form the basis of some of our most popular  technological advances in devices that most people use every day -  game consoles, laptop computers, and mobile phones. Further, besides going into tin cans, tin is an essential ingredient of solder for electronic circuit boards. Tungsten has many uses in traditional manufacturing including drill bits and gold is commonly used in electronic components because of its conductivity and lack of corrosion.

And, just as evidence that this is a real butterfly … even McKinsey has an essay on the birth of a consumer movement in their “Socially Conscious Consumer” posting! That means this is real! With information (data!) consumers will naturally move from products whose provenance they cannot confirm or for which it is known that slavery is involved in the supply chain AND move to products that can prove they are ethical.

But, the problem still exists. So, to make the full effect requires more butterflies!

If you are a company with a large consumer base - think Walmart or Marks and Spencer or Proctor and Gamble or your favorite tech products company - this will cause change (or at least concern and then change!) And, if your product relies on some of these minerals or other materials for its functionality it is a strong encouragement to look for both responsible sources or alternative materials. In an earlier posting reference was made to a BCG-MIT Sloan Business School study about business cases for sustainability. The top motivation indicated in a business survey was improved brand reputation … next was increased competitive advantage. Butterflies work!

These butterflies could be helped a lot by data. Transparency, linking back to the social impacts, labor practices/slavery, the influences mentioned at the start of this posting, is the key. Big data may help. But, the data most needed is that which is in this case most hard to come by. 

So, we come back to the question posed at the start of this post … what is “your butterfly” up to? Or, perhaps more correctly stated, can you get some butterflies “fired up”?! There are lots of opportunities. These influences will need to be included in our view of “circular economies.” We will need more and better data to tell us the whole story. Or, if the data is available, a better sense of how we need to analyze this data and use it to tell the rest of the story.

Maybe this should be one of our top New Year’s Resolutions.

Tuesday, November 11, 2014

Data Schmata

Show me some numbers!

At the end of the last posting the statement was made that it is necessary to address green manufacturing and the role of digital enterprises in the context of informing the customers each enterprise serves as well as those to whom the enterprise appears as the customer. Importantly, this has to be done both external to the organization as well as inside. What does that mean? With apologies to again referring to past postings … this starts with the Google earth view of manufacturing. This was first introduced in a posting way back in 2009. It bears refreshing everyone’s memory! The idea, shown in the image below, envisions one starting at the enterprise level and then zooming in to increasingly detailed parts of the manufacturing
enterprise spanning

 "Google-earth" view of manufacturing

the facility, line or system in the facility, the machines in the line, the tooling on the machines and finally the process on the machine. This image is updated from the first one shown back in 2009. Along the left side of the image are characteristics of data flow and operations. For example, data rates and response rates of the elements at the different levels range from weeks and months at the highest level reflecting long term planning to minutes and hours in the line for organization of production to seconds and minutes in terms of the machine functions operation as in “macro planning” and then milliseconds and microseconds at the tooling process levels. These data rates reflect the speed of changes occurring in some aspect of the element that has importance to the overall functioning of that element and, necessarily, the consumption of resources.  The illustration below (from Vijayaraghavan, A. and Dornfeld, D., “Automated Energy Monitoring of Machine Tools,” CIRP Annals, 59, 1, 2010, pp. 21-24) shows this temporal aspect of decisions and impact more clearly.

Required data rates at different levels of the manufacturing enterprise

To affect process control at the process level (here for a metal cutting operation - hence the reference to chips and cutting tool) one needs to have data and response at the micro/millisecond rate. As one moves higher in the structure the timing scale slows down proportionally. When reaching the enterprise level where supply chain management and asset management is of concern the decision and response time is longer. This is not, however, to imply that there are not decisions in supply chain management that do not occur more rapidly in some cases. In fact, the interesting thing about this type of representation is to look for the dependencies at lower levels on decisions and responses at higher levels. For example, a catastrophic tool failure at the process level that causes substantial down time and loss of availability of the machine and line could ripple up to the production planning and scheduling level if the disruption is substantial.

Each interface between the different levels in the manufacturing enterprise, and their accompanying data rates, decision rates and response rates offers an opportunity to add noise to the system (meaning reducing the reliability of the data or, at least, increasing variability in the data) and must be accounted for.

OK … but the Google earth view above has another side to it on the right. In this figure the potential range of influence and impact of the social effects (or dimensions) of the manufacturing enterprise are illustrated. These are challenging to represent in a simple drawing like this but will affect, at the process level in the facility, mostly the workers and support staff (for example, working conditions, safety, training, pay, etc.). The potential range of impact expands as one moves up the levels culminating with the supply chain which can have a national, regional effect or, within that country, a specific community (for example, air quality, water quality, healthcare and education, etc.). The “data rates” for acquiring impacts for these impacts (assuming one can quantify them sufficiently) will be similar as on the “hardware” side although may not be at the fastest level. Monitoring working conditions for exposure to chemicals or other contaminants for the worker at the machine may need to be done on a second or faster rate. Data on health care or educational levels of workers in the supply chain will be less frequent.

Never-the-less, is one desires to apply the digital enterprise concepts to tracking the sustainability of enterprise operations from energy and resources to social concerns the data challenges will be impressive.

We need to get more specific. What will this data look like? To illustrate, three examples are presented her derived from recent research at the Laboratory for Manufacturing and Sustainability (LMAS) at Berkeley. They address a facility level view, a line level view and a machine level view. They all concern manufacturing that centers on production of machinery using a range of processes but with a strong component of machining and metals fabrication. Energy consumption is a common metric here as it is readily measurable.

Facility level - The first example is drawn from the research of Dr. Nancy Diaz (N. Diaz, “Development of Energy Models for Production Processes and Systems to Inform Environmentally-Benign Decision-Making,” Ph.D. Thesis, University of California, Mechanical Engineering, 2013) and focuses on the comparison of electrical energy intensity (kWh/meter squared/year) for four production facilities of a major Japanese machine tool manufacturer. The data reflects the consumption of energy by the machinery in the plant, the heating, ventilation and air conditioning (HVAC) and lighting. The four plants address different parts of machine tool manufacturing from ballscrew production, the most precise (and hence requiring most exacting control of the environment - temperature and humidity) to less demanding machining and assembly. The figure below, from Diaz thesis, illustrates the dramatic range of consumption of energy per unit of floor area for the different factory functions and energy uses. The ball screw production facility has the highest HVAC energy intensity since these are exceptionally precise components that determine the quality and eventual performance of the machine tool and must be produced under the most stringent environmental conditions. Ball screws are turned by the motors on each linear axis of the machine and cause the table on which the workpiece is mounted during machining to move under the control of the computer program. They define the precision and accuracy of the machine movement (to a great extent).


Facility level production energy intensity for machine manufacturing

What do we learn from this? First of all, at this level, it is clear what the “relative cost” of different manufacturing processes are in terms of energy (and likely other resources) … precision is the highest due to the requirements of the facility, quality of the consumables, etc. Think of the semiconductor industry as at the high end. Then, it also shows where the greatest potential for improvement is in the process efficiency to reduce this intensity. But, it is also necessary to determine the total impact , meaning, the measured intensity (energy / unit area) times the total area involved in this type of production. If this is a small part of the total production then it might not be the first priority. If it is a major component then it could offer big improvement.

Systems/line level - The second example is drawn from the research of Dr. Stefanie Robinson (S. Robinson, "An Environmental and Economic Trade-Off Analysis of Manufacturing Process Chains to Inform Decision Making for Sustainability," Ph.D. Thesis, University of California, Mechanical Engineering, 2013) and focuses on the energy and resource consumption in a process chain with the objective of establishing a basis for trading off the potential for upgrading specific operations in the line. This was based on research conducted with a major heavy equipment manufacturer in the US. The figure below shows a schematic of a multiple operation process chain and a detail of one of the process operations with typical input and output of energy and other consumables along with waste and emissions.

Process chain and detail of individual process input/output

With a representation of a process chain, and the individual operations, one then needs to determine the consumption and rate of outflow of the major consumables and waste streams. One can appreciate that it is necessary to do a rather detailed analysis of the inputs and outputs (wasted and worn tooling, scrap from production, leakages, etc.) to be accurate. With this data, the actual resource consumption and associated economic and environmental cost can be determined. Then, the impact of changes in any of the production steps can be evaluated both in terms of productivity and quality as well as environmental (energy, global warming, water) effects and associated costs as shown below.

                        System consumption metrics and environmental and economic "cost"

Process level - This third and last example bores in more finely on the process level detail for a machining operation. Data from this level of analysis would feed into the systems/line level just described. This example also draws on the work of Dr. Nancy Diaz in the above cited thesis. This work developed a  generic method for calculating energy consumption during a realistic machining operation on a precision milling machine based on constant and variable contributions of the material removal rate (MRR). The MRR is a driver of productivity in a machining operation and is based on real time data of the feed rate of the cutting tool, the cutting speed and the depth or width of engagement in the case of milling studied here. This data is now available in real time from the machine controller thanks to standardized interfaces and data formats such as MTConnect and associated software. It is also available from the numerical control program driving the machining operation (that is the path the cutting tool takes in the machining operation) but that is often inaccurate due to actual the performance of the machine in operation. The curve below shows the specific energy (Joules/cubic millimeter, J/mm3) as a function of MRR.

Specific energy consumption for different material removal rates in milling

The significance of this data is that the designer or production engineer can determine the energy to create a part feature from knowledge (either estimated from the tool path or measured in process). And, this was determined for a variety of machining conditions with different tooling - so it has some breadth of application. For other materials, however, the curve would likely shift up (if a more challenging material to machine - more energy per unit of material removed) or down (if easier to machine).

So this is all driven by data! Lot’s of it collected at different speeds and representing different “views” of the enterprise. It is encouraging but humbling. Fortunately, as referred to in the previous blog posting on the digital revolution, communication speeds, computational capability and speed and the hardware spitting out the data from machines and systems are more common, less expensive and more reliable. The expression “drinking from a firehose” comes to mind! 

But, the good news is that no one will be thirsty! Some of the tools for using this data in productive and green operations will be covered in the future.

Monday, October 27, 2014

The Digital Revolution

Dejå vu all over again?!

The last posting started the buildup to using data (from where ever) to drive innovation, clarity and transparency, business model/economics, institutionalization, and benchmarking for industry in general and, ideally, for green manufacturing as well. This data should include information on what any specific process or system is doing, what it is consuming or emitting, what the impact per unit process output is, what is the efficiency of conversion of resources into product, what it the efficiency of the cycle, how does one system or process compare to another doing the same thing, and how does the overall performance match up with competitors in the same market, company, division, or factory, and so on.

The upshot of the discussion is that it should be possible to close gaps between what is needed to understand the items above and what is available. Further, the idea is to leverage the capabilities of big data and the digital views of an enterprise to help close this gap.

First, it is helpful to try to understand what big data and digital enterprises actually mean and then how they relate to our conversation here. A web search for the term “digital enterprise” turns up a lot of product pitches and some useful definitions. For example, WhatIs?TechTarget has a posting which offers the following definition - “A digital enterprise is an organization that uses technology as a competitive advantage in its internal and external operations.”  The “technology” is referring to information technology. This is rather broad. 

Let’s take a look backwards to see what can be learned. In November 1994 Fortune magazine published an article by Gene Belinsky titled “The Digital Factory.” This turned up the other day when I was rummaging through some old files in my office. The article predicts a range of impacts that the digital factory will have from customization of products “literally in quantities of one while churning them out at mass production speeds” to allowing supply chain integration, micro factories and breathing new life into the “beleaguered U.S.  machine-tool industry.” Referring to it as “soft manufacturing” it even proposed that the most astounding effect could be on employment where it “could stabilize or even increase the number of production-worker jobs in the U.S.” The article gives a number of excellent examples where this is being employed circa 1994 and the improvements realized and highlighting that “software is becoming more important than hardware - more important than machine tools - in American factories. And smart humans are back, replacing dumb robots.” It even predicted the importance of 3-D printing for prototyping.

Well, the first part is right … software became more important than machine tools - in the U.S. at least. Since this report the U.S. machine tool industry has essentially collapsed and in its place are builders from Japan and Germany with machines of incredible sophistication. They figured out that software AND the machine tool was a powerful combination. We didn’t. The second part is not. Robots staged a comeback, thanks to capable planning software, better cheaper sensors and efficient motors and controllers combined with a better understanding of where they work best and with what tooling systems. Pick up any trade magazine or “Google” robots and you’ll see an amazing array of co-worker robots doing sophisticated tasks. And productivity in the U.S. is up (see the post in July 2013) but manufacturing employment has not keep up with growth.

Is this current buzz about digital manufacturing just another Fortune 1994 article that, 20 years later, will look equally out of touch? Not likely. The downfall of the last prediction was that as software was growing in importance and capability, the “network” (or what was then connecting things) was infantile compared to what we now enjoy. Ditto for data flow from machines and systems. Sensors on production machinery (not just the position feedback data for machine tool axes or robot arm positions) were expensive, intrusive, slow to respond and often measured things that did not truly reflect the process or element being observed. One could go on at length about this but we’d be way off the path.

Not so now. The examples cited in an earlier posting on the internet of things showed systems communicating an impressive volume of data at high rates - enabling essentially real time behavior and response. The companies that make machinery that link into these systems (the folks that picked up where most U.S. machine builders stopped) learned how to employ the better cheaper sensors and efficient motors and controllers combined with a better understanding of where they work best and with what tooling systems that made robots more pervasive.

So, where does that leave us with respect to greening manufacturing and sustainable production?

Maybe to focus this a bit, let’s go back to the deep well of knowledge provided by McKinsey! In a May 2014 article titled “The Seven Traits of Effective Digital Enterprises,” authored by T. Olanrewaju, he and his colleagues go over examples of “transformational” traits of the successful digital enterprise. These are, in usual McKinsey style, rather high level and inspirational rather than execution oriented. But there are some nuggets of practical ideas - where practical here means “what can we do on the shop floor?”

One is “measure digital value not digital interactions.” This is the digital interactions are “digital-washing” equivalent to "green washing." Results or progress against a benchmark or a fiducial is what counts. But first you need the yardstick.

Another is “don’t accept historical norms; question the status quo; create a plan covering every function, product, business unit and location.” This is none other than the “Google earth view” of manufacturing identifying the elements, interfaces and value at all levels - leave no stone unturned. And, more over, it is looking for effectiveness and not only efficiency. Doing the suboptimal or wrong thing well is not a substitute for doing the correct thing and doing it well (some of this was covered in earlier postings). The last one to note (and please do read the whole article at the link above to get the full story) is “follow the money.” This addresses the “reduced impact for higher value” that has been trumpeted in this blog for some time. But, too often, follow the money is replaced by “follow the energy” and ignores other important consumables - materials, time, water, even labor. Energy is important for sure but many other econsumables are important too - maybe more important in some situations!

The report states “Many organizations focus their digital investments on customer-facing solutions. But they can extract just as much value, if not more, from investing in back-office functions that drive operational efficiencies. A digital transformation is more than just finding new revenue streams; it’s also about creating value by reducing the costs of doing business.”  Wow. We can use that. Do a “search and replace” here for digital investment, back office functions and finding new revenue streams replace them with sustainability initiatives, manufacturing processes and systems and reducing utility bills, respectively. And insert “environmental” between “costs” and “of” in the last line.

Or … following all the substitutions it looks like this. 

Many organizations focus their sustainability initiatives on customer-facing solutions. But they can extract just as much value, if not more, from investing in manufacturing processes and systems that drive operational efficiencies. A digital transformation is more than just reducing utility bills; it’s also about creating value by reducing the costs of doing business. And, digital enterprises should be able to do this better and insure measurable progress.

But there is also a “customer-facing” aspect to this of course. That’s where the circular economy comes in. Every enterprise has customers and is a customer to some other enterprise. That’s what’s shown in the Ricoh Comet Circle and, more abstractly, in the circular economy.

It is necessary to explore this with a circular economy in mind … starting with how to inform the customers our enterprise serves as well as those to whom we appear as the customer. And this has to be done both external to the organization as well as inside. We’ll continue with this in the next posting.

Friday, September 5, 2014

Moving to the next level

Or ... Mind the (data) gap

The interest and focus on sustainability is now more evident than ever before. Advertisements in increasing numbers trumpet some aspect of a product or a service as “sustainable”. Usually it is not but that doesn’t stop the attractiveness of using it to sell. Business has also noticed and there is increasing attention to “business plans for sustainability” and evidence that customers, if not investors, reward some businesses for trying to be sustainable. But, as mentioned last time, to is not clear if we are making any real progress.

Some attribute this to the presence of a “green gap.” The opening statement in a report from 2011 by Ogilvy and Mather sums up the “green gap” as follows:

“While we have been relatively good at getting people to believe in the importance of more sustainable behaviors, practices, and purchases, we have been unable to convert this belief fully into action” (p. 13).

For reference, the “green gap” is defined in the report as the gap between consumers’ green intentions and green actions. One might argue that this applies to business, and manufacturing, as well.

This is the other end of the equation with respect to increasing value while reducing impact (as it defines what consumers are willing to count as “value” in the domain of sustainable products) and it provides animation for implementing the circular economy. If there is no motivation or perceived reward and the value is not recognized then circular concepts, unless masked in conventional marketing or product functionality/value, will not be successful.

There was frustration expressed at the meeting held as part of the process to generate this report that, while many people in leadership positions - the so-called “thought leaders” - keep hammering away on the issues and need for action, the mainstream consumer is not really responding. Or, if responding, not responding rapidly or massively enough.

The chart below illustrates this point. In a survey of behavior in the US and China (PRC) respondents were asked indicate the importance of certain activities in terms of their definition of living a green or sustainable lifestyle (called “importance”). The then were then asked with respect to these activities whether or not they usually do the activity (called “behavior”). The gap between the two responses shows the divide between belief and action ... the “green gap.” The activities asked about were:

- taking public transportation,
- walking or biking to work
- purchasing locally grown food
- using eco-friendly cleaning products
- recycling bottles/cans/paper.

These are not, granted, the whole set of behaviors defining a green lifestyle or preferences (nothing about water, or reusing products, etc.) but are a reasonable set of trade-offs. And, of course, they are life style preferences and not directly related to production of goods and services.

China and the US were selected due to the impressive contributions to global warming of their economies.

The report mentions the desire of major corporations to assume more leadership in developing practices and products that address climate change, for example, but also the realization that in many cases the biggest impact of the product is in the use phase and not in the production or manufacturing phase. This has been discussed extensively in the past in this blog.

The suggestion was that rather than trying to get every one motivated to follow a better course on might need to talk to a larger audience than just the “committed greens” who already get it. Oglivy called this larger audience the “massive Middle!” This is not to be confused with the ‘silent majority’ of the Nixon-era (Google it if you don’t follow!) Ogilvy focuses on consumer issues, and angst about contrasting desires with needs, but  also gives a set of 12 ways to close the gap. Some of these have great applicability to business in general and manufacturing in specific. The objective is to “make green mainstream.”  

The 12 ways Ogilvy lists (with my translation to manufacturing space) are :

1) make it normal; in industry speak, institutionalize green behavior and practices.

2) make it personal; Oglivy intended this to be linking products to individual behavior; for manufacturing this is getting everyone in the enterprise connected to the activity.

3) create better defaults; in manufacturing this would be to have a series of options so that the fall back is not business as usual but another green alternative.

4) eliminate the sustainability tax; Oglivy relates this to the usually higher cost of green product; the manufacturing analogy is to insure that the economics are sound and there is a solid business model for this way of operating.

5) bribe shamelessly; this is not your usual bribe to get around regulations or influence decisions! This is rewarding products and services for behavior; in the manufacturing world, this means recognizing leaders, and their products and technologies.

6) punish wisely; shaming people into good behavior has some benefits if applied carefully they argue; in manufacturing, this is likely best done by benchmarking and providing metrics so that the organization can see where it is and where it needs to go. The days of “the flogging will continue ’til moral improves” are over!

7) keep innovating; this is easily translatable to manufacturing; in fact, sustainability drives innovation in manufacturing.

8) lose the crunch; Ogilvy is referring here to the image of green as Birkenstock wearing granola munchers (hence the “crunch”!) … which is not really an issue in Berkeley! It really means making green more mainstream; For manufacturing that is what this blog is all about.

9) turn eco-friendly into male ego friendly; This is Oglivy’s way of making green less “girly green” (their words … not mine!); This does not really relate to manufacturing as, for example, green machine tools are not considered less manly than conventional ones.

10) make it tangible; convert the tangible benefits of sustainable to something that can be easily visualized; manufacturing is attempting to do this all the time - from a cost, performance, impact, efficiency or effectiveness aspect.

11) make it easy to navigate; truth and transparency … easy to follow the dots; this maps directly to manufacturing and can relate to "eco-roadmaps" and similar to be discussed more in future postings.

12) tap into hedonism over altruism; help consumers see the fun on the green side of life; not sure how we can relate this; manufacturing is to many of us “fun” already - so one can suppose that green manufacturing can be “more fun”?! Let’s not push this one too far.

So now you are probably asking, how do we connect this back to big data?!  If one follows the above discussion about the key steps to address this massive middle as it relates to manufacturing, one can look at the ways outlined above and see some common elements
needed to enable these for manufacturing - all dependent on information.

The innovation, clarity and transparency, business model/economics, institutionalization, benchmarking, etc. are all driven by data. Data on what your process or system is doing, what it is consuming or emitting, what the impact per unit process output is, what is the efficiency of conversion of resources into product, what it the efficiency of my cycle, how does one system or process compare to another doing the same thing, how does my performance match up to my competitors in the same market, company, division, or factory, and so on. All determined by data. And data flowing from machines, systems, facilities and enterprises.

So we can close this gap, at least as it relates to green manufacturing, by leveraging the tools and capabilities of big data and the a digital view of our enterprise. This is where the story picks up next time with more details and some examples.

Friday, August 8, 2014

Circular economy, II

Progress and big data

At the end of the last posting the question was posed “can the “internet of things” be part of the circular economy? Can this connectedness push consumers to consider more sustainable behavior, or create products that provide increased value with lower impact, or allow effective recovery of resources at end of life?” The answer proposed was the very definitive “it depends”! Which is true - depends on reaction (or pro-action) of consumers, depends on reaction (or pro-action) of companies, and depends on reaction (or pro-action) of governments and non-governmental organizations (NGO’s).

In attempting to insure products and services in the manufacturing sector that reduce impact per unit of value created we’ll need to be careful in accounting for all the impacts generated and environmental return on  investment.

That means data … and metrics. Good data (not just streams of “information”) and real metrics that relate the engineering and manufacturing efforts with machine, system, enterprise and product performance.

This starts at how we measure growth in the economy. If we do not consider the impact of growth, broadly speaking, then the growth is pushed without consideration of the collateral issues - energy, water, materials consumption and the relevant impacts as well as the important social impacts across the supply chain.

A recent article posted on the Aljazeera America site, written by Sean McElwee, makes the point well. The article, titled “Gross Domestic Problem” (after GDP more traditionally meaning gross domestic product), states that “GDP is a fine measure of the goods and services produced within a country’s borders. However, it does not tell us how sustainable that growth is or at what cost it comes.” The sustainable part here refers to the business sense … will it keep going. It is the last bit “… at what cost it comes” that should interest us. They author refers to a paper in the January issue of Nature by a group of social scientists who argue that “If a business used GDP-style accounting, it would aim to maximize gross revenue — even at the expense of profitability, efficiency, sustainability or flexibility.” And this time they mean the real sustainability!

The Nature article states that the gross domestic product is a misleading measure of national success! They cite Robert F. Kennedy’s observation that the country’s GDP “measures “everything except that which makes life worthwhile.”  And the result is … while world GDP has made impressive gains since it was introduced around 1950 “progress” defined broadly may not be so impressive.

The problem is seen in the graph below, showing growth in GDP over the last 5 decades and the comparable growth in GPI (so-called genuine progress indicator). The Nature article explains that the the GPI is calculated by “starting with personal consumption expenditures, a measure of all spending by individuals and a major component of GDP, and making more than 20 additions and subtractions to account for factors such as the value of volunteer work and the costs of divorce, crime and pollution.” Meaning, it can include the impacts of “progress” such as destroying wetlands or depleted water resources.)

Figure: GPI and GDP over time (source: Kubiszewski, I. et al. Ecol. Econ. 93, 57–68 (2013).)

If you believe the GPI metric - one can see that sustainable progress is not progressing!

And corporations that follow the piper of growth without accounting for well-being because it is what the accounting systems and shareholders expect are just doing good business.

And countries that try to increase GDP/capita because that is associated with affluence (recall the IPAT equation?) and everyone wants to be more affluent.

That’s why Patagonia founder Yvon Chouinard cited in the last posting was so upset with corporations and “business as usual” … no one wants to restrict growth.

The Aljazeera article sums it up nicely. “GDP doesn’t even include the price of everything. For instance, the International Monetary Fund found that our failure to price the effects of carbon dioxide amounts to a $1 trillion annual subsidy for fossil fuel corporations. Conversely, the Clean Air Act produced $22 trillion in economic benefits from 1970 to 1990, according to an EPA retrospective study — much more than the estimated $523 billion it cost. In each case, GDP ignores crucial public benefits and the externalities of economic growth.”

As a result, Aljazeera notes from a landmark study titled “Mismeasuring our Lives”, “what we measure affects what we do, and if our measurements are flawed, decisions may be distorted.” So, making a choice between promoting growth as measured by GDP and protecting the environment may be false choices if the environmental degradation can be calculated and appropriately included in measurements of economic performance. 

I hope Mr. Chouinard read this! Actually, he would hope more corporate CEO’s read this!

But, this posting is not just ranting about the inequities created by a GDP centric view of the economy … what’s the solution and how does manufacturing fit in .. or, at least, big data, help.

Interestingly, this problem (coming up with the right way to measure things so the true impact or benefit or cost can be determined) is something engineers, specially manufacturing engineers, have been dealing with for a long time. In the field of manufacturing we often quote Lord Kelvin (aka Sir William Thomson) who stated “To measure is to know" and following on “If you can not measure it, you can not improve it." In lay terms, if you can’t measure what you made you don’t know whether or not you made it! Seems time to extend to this to measuring real growth on the way to sustainable development.

And that is where big data (or any data!) comes in! The circular economy, using resources effectively and efficiently - let’s say productively - will rely on the linking of a host of consumers all along the supply chain (from material sources to converters to manufacturers to distributers to consumers and back - recall the Ricoh comet circle).

According to McKinsey (the “Wikipedia of the Fortune 500 set”), in manufacturing big data can be useful for identifying ways to increase yield in a number of processing operations by identifying “insights” into production that were overlooked due to complexity of the process, large numbers of variables, many differing process stages, etc. If this same ability to uncover “insights” could extend to the broader impacts of manufacturing across the supply chain that would give both a sound technical basis as well as a reliable economic measure of the value of improvements.

This gets us back to the one element of the IPAT equation we can actually influence - Impact/GDP. That is, increasing the value created to the consumer or the market with reduced impact measured however you think is important (recall we indicated this would have to be on the order of 10X reduction to offset increased population and demand).

Maybe the real ratio we should be concerned with is GDI/GDP! Genuine progress per unit of GDP growth. If we have (or can get) the data for our manufacturing enterprises to determine with sufficient accuracy the impact on GDI of our production and our products this might give us a basis for making decisions.

Then, we could move beyond eco-charts plotting reduced environmental damage vs economic benefit where we assume a 1:1 relationship is sufficient to a plot of GDI vs GDP (on a local scale of course) to evaluate our decisions from design to production.

This will be challenging. It will have to be “circular” as we cannot gain the resource productivity that drives this any other way.

We’ll need to work on more details in future postings!

Friday, June 27, 2014

The internet of (green) things

Or … what would my refrigerator tell me if it could talk?

Recently I was at a conference on manufacturing where I was caught in in an avalanche of buzzwords … cloud, big data, internet of things, industrial internet, connectivity, connected revolution and so on. This feeding frenzy of connectivity and data is driven by a number of things, real and imagined. Businesses see opportunity for enhanced productivity and reduced time from design to production. Other businesses see opportunities for providing services and products to an informed customer - all at much greater speed. Others still see a chance to offer analysis capabilities to convert the “firehose stream of data” to a manageable set of results and metrics.

For example, in the manufacturing domain, General Electric is driving the creation of an “industrial internet” which GE expects will define how “industrial equipment with sophisticated sensors will be linked over a network that connects people to machines and machines to one another to boost efficiency.” They won’t be taking to refrigerators, at first, but jet engines to indicate potential maintenance requirement or, closer to the factory, failure potential and maintenance needs of sensor-enabled machinery on the factory floor.

In the manufacturing space. a leading commercial computer aided manufacturing (CAM) software provider, DP Technology/ESPRIT, has introduced a cloud-enabled tool path planning capability  - cloud-enabled CAM. The ESPRIT MachiningCloud Connection gives programmers (the smart folks that create the instructions and select the tooling to allow sophisticated numerically controlled machine tools to create the complex physical components that make up manufactured products) access to complete and up-to-date tooling product data, cutting hours of programming time by eliminating manual tool creation. This would have been done with physical paper catalogs of tools, configurations, cutting inserts, and other peripheral hardware to make it work (think of shopping at Sears or Target before websites, online catalogs or Amazon! If you are old enough to recall Sear’s paper catalog which was like a phone book for a large city, if you are old enough to remember phone books too, it is an interesting but long and manual process!). This capability simplifies the selection of cutting tools and, better yet,  offers a list of recommended cutting tools based on machining features and machining sequences that are planned. Finally, the programmer or manufacturing engineer can simulate the machine operation and behavior with accurate 3D models of tool components and assemblies.

In the design and collaboration space, Autodesk has introduced Autodesk 360 for design innovation and collaboration.  More than just two dimensional “drafting,” this cloud-based tool that provides free online data storage and a powerful, secure set of tools that improves the way engineers and others can design, visualize, and simulate anywhere and with “virtually infinite computing power”. This also simplifies collaboration among co-designers and customers, and streamlines workflows.

And then, of course, things that talk to you. Top of this list is probably the Nest thermostat recently acquired by Google, Inc. By use of training cycles, and observing your “behavior,” it learns what temperatures you like and builds a personalized schedule. Nest says if one teaches it efficient temperatures for a few days within a week, the thermostat will start setting temperature schedules on its own. And, with the Nest app you can connect to the thermostat from a smartphone and, if arriving home early (or later) change the temperature miles from home.

So what is the internet of things anyway? My favorite first “go to” source is a Google search which, usually, gets a Wikipedia hit right off … for “internet of things” Wikipedia defines the term (and acronym IoT) as referring to “uniquely identifiable objects and their virtual representations in an Internet-like structure.“  Apparently this term has been in discussion since the early ‘90s and was formally proposed by Kevin Ashton in 1999 (Ashton, Kevin, "That 'Internet of Things' Thing, in the real world things matter more than ideas," RFID Journal, 22 June 2009.) though the concept has been discussed since at least 1991. Wikipedia goes on to explain that In 1994, the Internet of Things was known as “control networks,” which Reza Raji discussed in an IEEE Spectrum article as “[moving] small packets of data to a large set of nodes, so as to integrate and automate everything from home appliances to entire factories.”

Home appliances to entire factories! All communicating with other devices, computers and, presumably, people.

But what might the effect of this integration and resulting actions be? Better productivity? Increased consumption? Smart consumption? Smart and sustainable consumption? It depends.

Capitalizing on this drive to connectivity Amazon has recently introduced their own “smart phone." The New York Times article characterizes this as “a device that tries to fulfill the retailer’s dream of being integrated into consumers’ lives at every possible waking moment — whether they are deciding where to eat, realizing they need more toilet paper or intrigued by a snatch of overheard music.” Meaning … sell, sell, sell!

So, are we making progress or not? A quote from Patagonia founder Yvon Chouinard in a conversation to an audience of hundreds of CSR officers and aspiring eco-preneurs may offer some insight. He said “If these Fortune 500 companies are now cleaning up their act, then why is the world still going to hell?” “The elephant in the room is growth: you make an energy-efficient refrigerator, so then you buy two of them. Not one public company will voluntarily restrict growth to save the planet.”  Well … that does not sound very encouraging.

So, back to the discussion about the circular economy.

Can the “internet of things” be part of the circular economy? Can this connectedness push consumers to consider more sustainable behavior, or create products that provide increased value with lower impact, or allow effective recovery of resources at end of life?

First reaction is, again,  … it depends!  If this pushes consumers to by more “impactful” products because they are or can be connected but don’t offer proportionally increased value … then this is not a good sign and Yvon Chouinard will cry foul! If the connectedness can drive a reduction in impact (due to better efficiency, or more effectiveness, consumption only when in use and at reduced rates of consumption, for example) then probably yes. Meaning, if it can affect consumer behavior in a positive direction then this is worth exploring.

This difference between consumer actions and consumer wishes for sustainable consumption is commonly referred to as the “green gap”. We’ll discuss this more in future postings.

So we will need to be careful about tracking the cost-benefit or, at least, providing some feedback to the consumer on the performance of the product or, sadly, this will simply be another round of technology driving increased consumption. And that’s not circular.

We’ll continue with the Circular Economy, Part 2 in the next blog - which will follow sooner than this one did to the last posting (!).