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Friday, March 10, 2017

World War 3 watch 03 - The North Korean challenge

North Korea has suddenly become active. In the past few years, it has acquired missile technology, nuclear technology and is making and weaponizing missiles with nuclear warheads. Now Japan, a nation whose constitution calls for peaceful, non-aggression is developing first strike capabilities.

First, I doubt North Korea got this technology by itself. I suspect China behind the rise of North Korea. It could also be Pakistan at the prodding of China. I include Pakistan because of the noise in US circles for a radical reset with Pakistan. Ignore the reasons cited in that article for some time. It appears that Pakistan, China and Russia are forming closer ties. That is primary reason US may be considering a radical reset. In 2004, China applied for MTCR membership, but its membership was rejected due to concerns that Chinese entities continued to provide sensitive technologies to countries developing ballistic missiles, such as North Korea.

Second, China may be using North Korea as a strategic piece to test US and its intent in the Pacific, particularly closer to China. China may have used the Philippines for this purpose earlier this year. China is sea-locked and is intent on breaking that stranglehold. 

Third, it is possible US knows this. The THAAD deployment cannot be simply for North Korea. South Korea already has enough arsenal to obliterate North Korea two times over. Further, South Korean forces already possess Patriot systems for point defense and Aegis destroyers capable of stopping ballistic missiles. Huffington Post highlights a possible reason why China is wary of THAAD deployment
The United States and South Korea have repeatedly asserted that the deployment will be “focused solely on North Korean nuclear and ballistic threats” — not Chinese missiles. The possibility remains, however, for THAAD’s radar to be covertly switched into a longer range mode that feeds into the broader U.S. missile defense — giving Washington earlier notice of Chinese launches.
In countering the THAAD deployment to South Korea, two existing Chinese missile programs are likely contenders for accelerated development — hypersonic glide vehicles and multiple, independently targetable re-entry vehicles known as MIRVs. 

Fourth, Japan, a nation protected by the US with a full defence presence needs no protection. Yet, Japan last year inducted a pseudo-aircraft carrier, raised constitutional limitations on its armed forces and now is developing first strike capability.

This is a power game between China and US. The strategic pieces used are North Korea, South Korea and Japan. In south China sea, the US did not have a piece in play after Philipines sided with China. These developments do not bode well. But these are the facts.

From the perspective of India and Singapore, China remains a threat. Singapore has conclusively sided with the US and it is the best possible backstop against Chinese domination. India however, has not been decisively in favour of US. It may help to side with the US clearly and unequivocally. At the same time, given the US policy, both Singapore and India need to ramp up their self-defence protocols. Indian missile defence shield is completely inadequate in comparison with what Chinese are capable of. It is time to ramp up the Missile Defences.

Smart Manufacturing 01 - A Policymaker's guide to Smart Manufacturing by Stephen Ezell

Manufacturing policy intrigues me. The reasons are quite varied. The nations across the world are trying to push manufacturing. I was trying to go through different approaches to manufacturing policy by various nations. I came across this report by Stephen Ezell titled A Policymaker's guide to Smart Manufacturing. I was not quite impressed by the policy recommendations put out in the summary. However, the report is excellent otherwise and a must-read for everyone who loves policy or manufacturing or both.

History of manufacturing
It describes the evolution of the smart manufacturing, rather manufacturing itself. Here are some quotes: [Emphasis and comments mine]
For most of human history, “manufacturing” entailed artisanal fabrication (i.e., individually skilled bronze or iron workers), a paradigm that prevailed through to the Middle Ages (approximately 700 AD), when it gave way to a craft-guild production system [these were the trade body equivalent - RD] that was still specialized in its trade, but now evolved from the individual- to the guild-production level. This system was usurped by the so-called First Industrial Revolution, beginning at the end of the 18th century in Great Britain, which saw the introduction of water- and steam-powered mechanical production facilities (e.g., the cotton gin and textile loom) and the increased use of iron-based products.  
Almost a century later, in the late 19th century, the introduction of electricity-powered mass production based on the division of labor, increased use of steel-based products and machines, and assembly-line concepts (i.e., Henry Ford and mass automobile production) heralded the so-called Second Industrial Revolution 
A third transformation occurred in the postwar era, when discrete goods manufacturers began to introduce automation technologies (a term coined in 1945 to describe single-purpose machines designed to produce one specific part or conduct one specific process) as well as automated, continuous-flow systems. However, these systems were anything but flexible and, as they matured, the work could often be performed by lower-skilled labor, far from final consumer markets. At the same time, the emergence of science-based industries, including electronics and chemicals, meant that an increasing number of products became more sophisticated. It is important to note, however, that most commentators ignore this third transformation, lumping it together with the transformation at the end of the 1890s. As a result, many refer to today’s transformation as the “fourth industrial revolution” or “Industry 4.0.” (This report will refer to today’s transformation as the fifth wave.) [RD: There was also development of transducer technology that aided automation] 
The fourth transformation occurred in the 1980s and 1990s, when the first digital- electronic systems were developed, empowering ICT-enabled systems (e.g., computer-aided design and manufacturing, robotics, etc.) to further automate manufacturing, adding some flexibility but mostly enabling the better coordination of dispersed supply chains, thereby enabling the global distribution of many kinds of production.  
Emerging today is a fifth wave—an era of “smart manufacturing” that integrates advanced- digital technologies more completely into production systems. These technologies include wireless communication technologies, the Internet of Things, cloud computing, easily (re)programmable robots, machine intelligence, and other next-generation digital technologies to create a direct, real-time interface between the virtual and physical world. 
Here are the sources for the above from the references cited in above article:
  • Rebecca Taylor, “Do It in Digital: Virtualization and Tomorrow’s Manufacturing” (presentation, The National Center for Manufacturing Sciences, Ann Arbor, MI, July 2015). 
  • Dr. Henning Kagermann, Dr. Wolfgang Wahlster, and Dr. Johannes Helbig, “Recommendations for Implementing the Strategic Initiative INDUSTRIE 4.0” (German Federal Ministry of Research and Education, April 2013), uer_Sonderseiten/Industrie_4.0/Final_report__Industrie_4.0_accessible.pdf. 
  • For a description of the history of manufacturing technology waves, see Robert D. Atkinson, The Past and Future of America’s Economy: Long Waves of Innovation That Power Cycles of Growth, (Cheltenham, UK: Edward Elgar, 2005). 
  • Stephen J. Ezell, “Industry 4.0 Holds the Key to Modern Manufacturing,” Bridges 42, December 2014, 
Defining smart manufacturing
There is a lot of confusion as to what kind of manufacturing is being promoted by different governments. One thing, though, is certain. The governments are promoting new manufacturing and not what already exists. This new manufacturing goes by different names across different countries - "advanced manufacturing", "smart manufacturing", industrie 4.0, etc. I use the term "smart manufacturing for this. Stephen also collates the definitions of Advanced Manufacturing or smart manufacturing:

OECD Definition of advanced manufacturing: Advanced manufacturing technology is defined as computer-controlled or micro-electronics-based equipment used in the design, manufacture or handling of a product. 
The President’s Council of Advisors on Science and Technology (PCAST) defines advanced manufacturing as “A family of activities that (a) depend on the use and coordination of information, automation, computation, software, sensing, and networking, and/or (b) make use of cutting edge materials and emerging capabilities enabled by the physical and biological sciences; for example, nanotechnology, chemistry, and biology. It involves both new ways to manufacture existing products, and the manufacture of new products emerging from new advanced technologies.”  
The definition given by legislation introduced in the 114th Congress proposed a useful formal definition of smart manufacturing as part of the North American Energy Security and Infrastructure Act of 2016 (S. 2012).9 The legislation defines smart manufacturing as: 
“Advanced technologies in information, automation, monitoring, computation, sensing, modeling, and networking that (A) digitally (i) simulate manufacturing production lines; (ii) operate computer-controlled manufacturing equipment; (iii) monitor and communicate production line status; and (iv) manage and optimize energy productivity and cost throughout production; (B) model, simulate, and optimize the energy efficiency of a factory building; (C) monitor and optimize building energy performance; (D) model, simulate, and optimize the design of energy efficient and sustainable products, including the use of digital prototyping and additive manufacturing to enhance product design; (E) connect manufactured products in networks to monitor and optimize the performance of the networks, including automated network operations; and (F) digitally connect the supply chain network. 
How policymakers should view Smart Manufacturing?
Stephen cites Tim Shinbara:
Tim Shinbara, vice president for manufacturing technology at the Association for Manufacturing Technology (AMT), explains that policymakers should envision four levels, or layers, of smart manufacturing. 
At the first layer lie the intelligent machines themselves, the individual production equipment doing the work of forming, cutting, forging, and stamping products that integrate into the Industrial Internet of Things by being equipped with sensors that create information streams. 
At the second level, a “digital thread” consolidates information streams from those individual machines across the factory floor (and indeed across the entire enterprise-wide production system) by linking multiple “process chains” together. This represents a consolidated, ICT-enabled view of each of the individual process chains that constitute an enterprise’s holistic manufacturing production system. 
At the third level lies applying data analytics to this broad “manufacturing intelligence” to optimize processes and to iteratively design intelligent products. 
At the fourth level are smart CEOs, or smart C-suite executives, who are empowered to make optimized, real-time decisions on production levels, production location, production options, etc., based on the corporate intelligence created by the smart manufacturing enterprise. 

There could be the fifth level that protects the consumer’s data and privacy as a part of the structural design of advanced manufacturing itself. Thus it should be easy for the consumer to share her personal information to the manufacturing centre so as to enable better customisation while protecting the data shared by her. One way to do it is through protective laws at a national level. Alternatively, we can use something similar to Asimov's rules for robots for the protection of data in smart manufacturing.

Smart at each step of manufacturing
I get confused when we use a generic word "smart" to everything. Many times it does not clarify what exactly smart means. Using ICT in every step - yes. But what does it mean? Stephen dwells on it and constructs a detailed vision of how the use of ICT can be deployed in manufacturing. The report is full of examples from Xerox and GE to SMEs using these technologies.

He classifies the "smart" discussion into: [some important quotes highlighted for my use-RD]
  1. Digitally Enabled Product Design
  2. Additive Manufacturing (3D Printing)
    1. Heretofore, most manufacturing processes were subtractive, that is, they started with a block of sheet metal or aluminum and were milled or stamped into desired shapes; in contrast, additive manufacturing is built up layer-by-layer, enabling fundamentally new shapes and even mechanical linkages that simply can’t be achieved through traditional subtractive manufacturing techniques.
  3. Digitally Empowered Factory Operations
    1. Life sciences and ICT hardware manufacturers leverage smart manufacturing technologies to predict problems and cuts costs. Intel uses predictive modeling on data to anticipate failures, prioritize inspections, and cut monitoring costs at its chip-manufacturing plants.
    2. Finally, smart manufacturing techniques allow designers to rethink the traditional, location-fixed factory floor, and even to make the “factory floor” itself mobile. For instance, Pfizer is currently developing portable manufacturing platforms, allowing it to efficiently produce vaccines for children in countries where they are needed most.
  4. Digitally linked supply chain management:
    1. Here the report turns a bit shallow but
  5. Smart products beyond the factory:
    1. In another instance, in 2013, batteries in two Tesla Model S cars were punctured and caught fire after drivers struck metal objects in the roadway. Tesla realized the chassis on some of its vehicles was too close to the ground, and was able to send an over-the-air software update to all Model S vehicles that raised their suspension under certain conditions, significantly reducing the chances of further punctures.
    2. The data stream produced by engines and airplanes also changes the service and support offerings that jet-engine manufacturers can provide. Engines produce tremendous amounts of information. A single Boeing 737 engine produces 20 terabytes of data every hour in flight.81 Therefore, an eight-hour flight from New York to London on an aircraft with two engines can generate 320 terabytes of data.82 GE Aviation Engines tracks the exact conditions—temperature, humidity, altitude, particulates in the air (e.g., dust)—of each mile flown by its engines. Accordingly, when GE puts together its engine maintenance and service bid for airlines such as Emirates, Lufthansa, Southwest, or United its offer is based on knowledge of the historical use and experience of each engine in the contract.
In this part some things may have been missed:
  1. Distributed quality control is critical. 
    1. One of the reason for keeping the production in-house was control over quality of the product which impacted the brand.
    2. With advent of standardized quality control procedures (6-sigma, Kaizen, TPM, etc.) each factory was able to achieve high quality. Yet, adherence to quality remains a significant issue in modern quality. There has been substantial development in this area. Apple uses AI to match the components etc. 
    3. It will soon be possible to control the quality remotely.

But what about transfer of jobs and manufacturing to lower cost locations?
Stephen answers this with 
Indeed, smart manufacturing techniques will increasingly enable competitive manufacturing in high-cost environments. For example, professor Suzanne de Treville of the University of Lausanne has developed supply-chain analytics tools that help companies quantify and price the advantages they have in manufacturing locally, making it easier to show that the apparent cost reduction offered by a competitor in a low-wage country might not be as compelling as it seems. By applying quantitative finance tools to demand dynamics, Treville’s freely available Cost-Differential Frontier (CDF) price calculator allows manufacturers to price the increase in exposure to demand volatility that comes from increases in lead time.105 Many companies applying the tool find that the supply- chain mismatch costs arising from increased demand-volatility exposure are frequently greater than the cost reduction offered by an offshore supplier, and that going offshore is often not a bargain. Combining this analysis with quantifying the impact of possible demand peaks also allows companies to rethink cost allocations depending on time sensitivity. In total, the tool helps manufacturers to understand volatility in order to manufacture close to their markets profitably (and without requiring subsidies). In summary, there’s an increasing economic justification in modern manufacturing for co- locating idea generation, design, systems development, production, and supply-chain management.
I do not agree with the conclusion. I think manufacturing can remain more distributed than before. I don't see why Auto servicing centres cannot assemble the entire cars while maintaining quality controls. 

Comparative policy analysis
Stephen thereafter compares the policies used by different nations to develop smart manufacturing. Here is the comparative snapshot:
He has described the policies in short. But these policies still remain too generic. The idea is to spend the money and hope for the best. Most of these countries, though, do have a basic idea of what can be achieved.

Comments on policies
First, there is no focus on what kind of talent will be required for the Smart Manufacturing initiatives. 

There is also no comprehension as to what effects it will have on jobs. 

In this aspect Germany seems to be doing better. Firstly, it has developed some use cases which hopefully means that German entrepreneurs and manufacturers get some exposure of what is possible with new tech. Secondly, it means the adoption of basic level of Smart Manufacturing will be more widespread than other countries estimate.

Singapore has done well in this area too. Singapore government has engaged with different industry sectors to identify the skill requirements of these sectors and their future.

Wednesday, March 01, 2017

Demonetisation - Why did it NOT affect GDP at all?

India released its GDP growth number for Q3 of the Financial Year 2017 (April 2016-Mar 2017) and it clocked in at 7%. This performance was particularly noteworthy (pardon the pun) because 86% of notes in circulation were withdrawn on November 8. The problem eased only by mid-January. Thus, two out of three months in the quarter, India faced a massive disruption. Anecdotally, during the period since November 8, most of the country was busy depositing cash, swapping notes etc. Consumption activity was reduced, there was stalling of transportation services (trucks) etc. Experts commented that Indian GDP growth would reduce - by 1.5-3% from ~7.5%. In effect, markets were expecting GDP to clock in at 6.1%. Notwithstanding that, Indian GDP came in at a strong 7%. Economists were spooked and people have started blaming the CSO for the quality of data.

Naturally, today's papers are filled with sceptical articles.

Mint Manas Chakravarty concludes that this is not entirely because of base effects. In fact, some sectors have had base effect advantages but others do not. Overall, the trend in activity seems to be smooth.

Another Mint article indicates a possibility that Strategic timing of demonetization and fiscal stimulus seems to have helped in retaining growth despite demonetisation. Here the author suggests few reasons. First, the informal sector mainly affected by demonetization is not appropriately captured in GDP statistics. It also attributes the muted impact to strategic timing after the festive season demand has played out. The Third reason could be that the government has been using some sort of a counter-cyclical fiscal policy to stimulate investment activity. Government Final Consumption Expenditure (GFCE) grew at a massive 19.9% in year-on-year terms in the previous quarter.

The Economic Times questions the credibility of the data. In another article ET cities various analysts /economists who find the data incredulous. Interestingly, ET also has an article that says Fiscal deficit for first 3 quarters has surpassed the annual target. This sort of corroborates with the Mint's suspicion of fiscal stimulus.

Financial Express article states that, some analysts believe that some said use of old notes for consumption might have contributed to the rise. It also highlights some discrepancies —
the difference between the supply and demand side of GDP — turned negative after a gap of four quarters (-Rs 6,767 crore) in the December quarter, compared with Rs 45,378 crore in the second quarter and Rs 30,645 crore in the first quarter. In the last quarter of 2015-16, discrepancies touched a massive Rs 1,43,210 crore, causing a flutter then and raising doubts about the credibility of the country’s data collection mechanism. When private final consumption expenditure, gross fixed capital formation, government final consumption expenditure, change in stocks, valuables, and net exports exceed the overall GDP (based on the supply side data), discrepancies turn negative.
So what happened? Was the pain felt during the demonetization unreal? Actually, no. The answer to the positive surprise in the GDP figures is in the details.

First, the informal economy is severely under-represented in the GDP numbers. Naturally, it does not count the upside nor the downside. Thus, GDP number remains disconnected from their reality. However, this may not fully explain the upside surprise.

The main reason why data surprised because of the way it is designed. Nothing wrong with the calculations but these are design flaws. Here is a table showing the components used to determine the growth in individual sectors.
GDP Advanced Estimates - Indicators used for estimating GDP
If you notice, the indicators are not sensitive to the demonetization effect. At least not so quickly. The effect of using these indicators is that you will get a smoothened GDP series when compared to changes on the ground. Most of these indicators were positively affected by demonetization. For example,

  1. More taxes (municipal, sales and excise, etc) were paid so as to use the existing cash. 
  2. Many smaller firms were used to push money into bank accounts by showing that as cash sales (taxed at 35% v/s penalty rate of 50%). 
  3. Agricultural sowing/production was not affected because of the timing of the move.
  4. The number of train bookings had gone up during the demonetization period. There could be a similar increase in freight bookings too.

All in all, I do not think it is any calculation or manipulation effect. The high GDP number is result of design of the indicator itself which leads to smoothened output. Naturally, we may see a dampening effect in subsequent quarters are the true distortion gets dissipated.