supply chain design in the era of increased...

SUPPLY CHAIN DESIGN IN THE ERA OF INCREASED VOLATILITY AND THE 2nd MACHINE

Dr. Javad Feizabadi

Dec-2017

MISI | Corporate guidelines | July 2016 | Copyright © 2016 MISI. All rights reserved. By TMI

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Supply chain design in the era of increased turbulence and

second machine

Javad FeizabadiMIT Research Associate

Associate Professor @ Malaysia Institute for Supply Chain Innovation (MISI)

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6 CentersofExcellence10+ EducationalPrograms80+ Researchers&Faculty150+ CorporatePartnerships117+ CurrentStudents1000+ AlumniWorldwide

1GlobalNetworkMISIisRanked#1

worldwideforsecondconsecutiveyearinMasterofSciencein

SCM

3 3

Agenda

• The 2nd machine

• Entering the age of volatility and complexity

• Developing structural flexibility

• 2nd machine technologies as one of the enablers of structural flexibility

• A framework for an adaptable supply chain

4

3-Dimensional Concurrent Design *c MIT2000clockspeed.com

IMPLEMENTATION OF SUPPY CHAIN DESIGN:EMBED IT IN 3-D CONCURRENT ENGINEERING

PRODUCTPROCESS

SUPPLY CHAIN

Recipe, Unit Process

Details,Strategy

PerformanceSpecifications

Product Architecture, Make/Buy components Time, Space, Availability

Technology, &Process Planning

Manufacturing System, Make/Buy processes

5 5

Disruptive process innovation in Autos vs. disruptive product innovation in Electronics

Disruptive Process Innovation in Autos vs. Disruptive Product Innovation in Electronics

Perfo

rman

ce

Lean Production

Mass Production Process Innovators --Ford --Dell --Wal-mart Craft Production --Southwest Air --Toyota --Li & Fung

Time

6 6

The 1st Industrial Revolution From cottage industry to the factory, 1760 to 1840

Picture source:http://3.bp.blogspot.com/-o1Ou3tt6PaU/TpirrlAc2yI/AAAAAAAAC90/Iv-cfbrXLIQ/s1600/weavers+cottage.jpg

The 2nd Industrial RevolutionThe Assembly Line, late 19th

century to early 20th century

The 3rd Industrial RevolutionComputer revolution, began in the 1960s

7

The 4th Industrial Revolution, from 2000

7

• PHYSICAL MEGATRENDS

• Autonomous vehicles

• 3D printing

• Advanced robotics

• New materials

• DIGITAL MEGATRENDS

• Internet of things

• Artificial intelligence

• Cloud computing

• Big data and advanced analytics

• Blockchain

• Physical internet

• BIOLOGICAL MEGATRENDS

• Gene sequencing

• Synthetic biology

Schwab K. (2016), “Welcome to the fourth industrial revolution”, Rotman Management, Fall

8 8

The 4th Industrial Revolution

5

utilities—whose traditional business model has been upended by renewable (and increasingly customer-generated) energy sources and more sophisticated consumers. The conglomerate’s improvement target: within four years, cut delivery lead times by more than half, defend and increase market share, and raise profit margins by about 30 percent.

Complexity management. In utilities, as in much of today’s business world, decades of acquisitions have left many companies managing

to generate insights about potential cost and value improvements. For example, computer-aided design tools linked to vast pools of procurement data, social-media activity, and cost and complexity benchmarks can allow a company to quickly identify designs that maximize profitability while minimizing wasted time and effort.

Such breakthroughs are not just for the consumer sector. One of the world’s largest industrial conglomerates brings these ideas to life with products meant not for individuals but for

Exhibit 2

CDP 2017Roadmap for digitizingExhibit 2 of 5

In the fourth industrial revolution, digital analytics enables a new level of operational productivity.

Source: Forbes; World Economic Forum

1st 2nd 3rd 4th

Mechanization, water power, steam power

Maturation of new cyber physicaltechnologies(artificialintelligence, 3-D printing, robotics)

Data analytics driving efficacy and effectiveness and new business models

Pervasive sensingandactuation

Ubiquitous connectivity throughout the supply chain

Unprecedented levels of data and increased computing powers

Mass production, assembly line,

electricity

Computer andautomation

Cyber physicalsystem

9 9

Volatility Index

Our argument here is that it does not matter whether there is an increased level of volatility inthe oil price, the exchange rate, or the Bank of England base rate. What does matter is whenseveral of these indicators move together[2], as this changes the general business climate inwhich firms operate.

A critique of the methodologyOur approach gives rise to two questions: whether the right variables have been chosen, andsecond, whether the computation of a “mean coefficient of variation” is a meaningful andvalid representation of overall supply chain turbulence. With regard to the first point,we admit to having made a normative choice of variables to include in the index. Arguablyany other combination would have been equally representative, or better. In Figure 1 wepresent our logical justification for our choice of indicators in terms of their direct andindirect impact on the business climate. We challenge readers to review, revise and expandon our selection of variables. Equally there is a case for suggesting that industry-specific, oreven company-specific indices, might be constructed to reflect the particular environment inwhich a particular business operates.

With regard to the computation of the index, the CoV is an appropriate, scale-free metricthat highlights the degree to which a series of data oscillates. It does not, however, allow anyjudgement as to whether the variation measured is unusual, or problematic. The results thusstill need to be assessed qualitatively. In comparison, it is worth considering how the stockmarket Volatility Index (VIX) and the Baltic Dry index (BDI) are computed. VIX is aweighted average for a selected number of options on the S&P 500 index. More specifically,VIX is calculated as the square root of the par variance swap rate for a 30-day term. The VIXis the square root of the risk neutral expectation of the S&P 500 variance over the next30 calendar days, and is quoted as an annualised standard deviation. The BDI, on the otherhand, is a combination of quoted prices. Every working day, a panel of internationalshipbrokers submits their view of current freight cost on various routes to theBaltic Exchange. The routes are meant to be representative, i.e. large enough in volume tomatter for the overall market. These rate assessments are then weighted together to createboth the overall BDI and the other specific indices. As can be seen, like the SCVI, both VIXand BDI also make arbitrary judgements as to what to include, and revert back to simplestatistical metrics of variance and deviation.

Other potential weaknesses of the SCVI include multicollinearity between variables, andautocorrelation in the time series. The former is a well-known problem with macroeconomicvariables used in statistical analyses. As, however, we are not proposing any inference

Volatility in the business environment

Demand Commodityprices

Cost ofenergy

DisruptiveInnovation

Politicalunrest

(Access to)finance

Exchangerates

Copper andCrude oil price

Baltic DryIndex

Gold Bullionprice

VIX Bank baserate

Supply Chain Volatility Index

Direct relationship

Indirect relationship

Figure 1.Linking aspects ofsupply chain volatilityto the index variables

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Christopher M and Holweg M. (2017); “Supply chain 2.0 revisited: a framework for managing volatility- induced risk in the supply chain” ; International Journal of Physical Distribution & Logistics Management, Vol. 47 No. 1, pp. 2-17

10 10

Volatility Index

exacerbated as rare earths have come under scrutiny due to the environmental and socialconditions under which they are mined, which may further increase the risk of stable supply(Alonso et al., 2012).

We have tested the levels of volatility for the prices of rare earth metals empirically, andindeed have found extreme levels of volatility for rare earth elements’ spot prices between2010 and 2012, and continued high levels of volatility since that time. Compared to the SCVI,average volatility for rare earth metal spot prices peaked at 60 per cent in 2010 and again in2011, which marks a 50 per cent increase over the SCVI peak in 2009. This extreme volatilitythat technology-related industries have been facing since 2010 has exposed theirvulnerability and dependence on a few global sources. Latterly, technology manufacturershave been increasing their focus on the redesign of products and processes to gain greaterflexibility and independence in the event of supply problems, yet their persistent reliance onthese elements upholds both prices and volatility in this market, and thus poses a strategicrisk to firms that are exposed to this market.

The SCVI 1970-2015We have updated the SCVI with data up to mid-2016 to assess whether the mean variationacross all indicators as well as the band of variation has returned to greater stability afterthe global financial crisis. Figure 2 shows the CoV as our measure for the SCVI for completeyears 1970-2015.

The main interest of the index is both the absolute level of volatility, as well as thechanges in those levels. We use CoV to normalise and compare volatility in key indicators.We have borrowed a tool from stock market analysis and have established “BollingerBands” (Bollinger, 2002) to provide a guide for determining whether changes in the indexare significant. Bollinger Bands use a 20 months moving average plus or minus twostandard deviations to set the level of the bands; if in any one period the index breaksthrough the band, then this might be considered to be an indication of an emerging out-of-the-ordinary situation (see Figures 3 and 4).

Since the recent financial and economic crisis, large and lasting volatility can beobserved in raw material prices (from 2008 to 2013), which was identified as the mostprevalent factor for businesses in general through our interaction with industry leaderswhen presenting the results of our research. Greatest variation, as suggested by our data,can however be observed in stock market indices and with the cost of shipping.Supply chain operations can be strongly affected by changes in shipping costs, andin turn stock markets can react erratically to local and global supply chain issues or

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

Max Min Mean CoV Linear (Mean CoV)

Figure 2.Supply ChainVolatility Index1970-2015, withmin-max interval

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SCMtermwascoined

Today’ssupplychainsweredesigned

11 11

Yesterday’s Model•• Stable Conditions•• Inventory Based•• Low Cost Production

Market Driven

Supplier Driven

Mass Customisation/Economies of Scope

Mass Production/

Tomorrow’s Model• Turbulence & Uncertainty• Information Based• Customer Value Oriented

Economies of Scale

Thesupplychainofthefuture


12

Conventional supply chain design

12

Picture source: http://www.ticsales.com.au/what_we_do.asp

• Based on conditions of relative stability

• Designed to optimize production flows

• Often based on ‘lean’ thinking

• Network optimization based on cost rather than responsiveness

1313

Structural vs Dynamic FlexibilityDynamic flexibility is a reflection of the agility of the supply chain, particularly its ability to respond rapidly to variations in volume and mix.

Structural flexibility is the ability of the supply chain to adapt to fundamental change, e.g. if the ‘centre of gravity’of the supply chain changes, can the system change?


1414

Supply Chain Centre Of Gravity

indicating that we are entering a renewed period of high volatility. Furthermore, thecontinued wide band of variation for individual constituents of the index post-2008 suggeststhat we are still in an “era of turbulence”.

The need for a new mental modelThe notion of shifting centres of gravityThe changes in the context or landscape in which supply chains operate is an observationthat is shared by SCM scholars (see e.g. Bowersox et al., 2000; Sweeney, 2013; Stevens andJohnson, 2016; Spekman and Davis, 2016), and practitioners alike. Since 2010 we havepresented the supply chain volatility data at academic seminars, to executive audiences,MBA classes at Oxford, Cambridge, Cranfield and elsewhere. In these presentations we haveasked participants for their views on the key variables that cause turbulence in theirrespective firms’ supply chains, and have followed this with a discussion about what kind ofeffects the various turbulence factors had on their supply chains. By far the most prevalentfactor causing supply chain volatility was related to “materials and components”.Here, quality levels, availability of materials and components, lead-time of globalsuppliers and the limited flexibility of global suppliers were mentioned. Also, specific rawmaterials with significant volatility were seen as being: steel, copper, aluminium and rareearth metals (see above).

The second most important category mentioned was political factors, such as regulation(e.g. related to emissions and labour), import/export taxes, corruption, labour cost and theprocess for granting licenses or regulatory approval.

Combined, turbulence related to materials, supply and political issues were mentionedtwice as often as all the other factors: this is important to note, as despite their newscoverage, neither natural disasters (such as Tsunamis, earthquakes or ash clouds) norenergy/transportation cost (either directly as oil or fuel price, or indirectly as airfreight orcontainer shipment cost) were mentioned anywhere nearly as often as we had expected.

Additional factors mentioned include the “economy” in general terms, referring tocustomer demand as well as macroeconomic uncertainty in national economies. Alsomentioned, but to a lesser extent, were the cost of energy, the cost of transportation (especiallythe cost of airfreight), and technology (in terms of disruptive technologies, the quality of ITsystems and data), and lastly, access to finance (for both customers and suppliers).

Whilst individual firms were always affected by idiosyncratic factors, overall it is worthnoting that all groups presented a uniform picture of factors. Obviously we recognise thatdata collected in this way are anecdotal, but it is indicative of a global shift that can occur intoday’s supply chains. As global forces on both the “supply side” and the “demand side”continue to oscillate, so too does the “centre of gravity” of the supply chain. For example, afirm solely serving customers in one geographical region, say Europe, will experience a strongdemand side pull towards Europe. Equally, a firm operating across all regions may find itscentre of gravity is being pulled towards low-labour cost regions on the supply side. Figure 5outlines key factors on the demand and supply sides that “pull” the centre of gravity.

Supply Side Vectors Demand Side Vectors

Centreof

Gravity

Labour Costs

Materials andResource Availability

Skills

Transport Costs

Changing Demographics

Changing CustomerPreferences

Disposable Income

Industry Development

Figure 5.Factors causing shiftsin the centre of gravityin a supply chain

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Moving from dynamic to structural flexibility

EfficientSupplyChain

AdaptableSupplyChain

TraditionalSupplyChain

DynamicFlexibility

StructuralFlexibility

Low

Low

HighHigh

1616

Efficient versus adaptable supply chains

to a stable supply chain that managed seamless flows across tiers in the network. Thisis the key tenet of what the SCM literature has discussed ever since 1982. Very fewfirms, however, have learned how to build structural flexibility into their supplychains. Two well-published cases are Dell and Zara (Ferdows et al., 2004; Fugate andMentzer, 2004; Kapuscinski et al., 2004), which are amongst the few firms that not onlymanage endogenous turbulence, but have also attempted to extend their strategiesinto managing demand-driven exogenous turbulence. Dell manages the demand for itscomponents by adjusting prices. Zara has developed a “rapid-fire” supply chain thatis able to respond very quickly to changes in fashion and demand by drawing uponwhat can be best described as a set of “modular” small factories in Northern Spain.However, such competitive advantage can be short-lived, as the case of Crocs vividlyillustrates (Marks et al., 2007). The reason is that the ideas and practices of SCM havelargely emerged over a period of relative stability – as demonstrated by the VolatilityIndex – they have not been tested until recently in more turbulent conditions.

We need a new mental model for how to deal with turbulence in the supply chain,by shifting away from a single-minded quest for efficiency to a balanced view on howto create adaptable supply chain structures (Table III). In many ways, the departurefrom the traditional “efficient” supply chain, to one that is able to cope with dynamicdistortions (using tools such as CPFR, VMI, and information sharing), to a supply chainthat is able to adapt structurally is a natural transition (Figure 2). However, it doesrequire a fundamentally different perception of what a “good” supply chain designshould look like. Let us define in more detail what is meant by structural flexibility.

What exactly is structural flexibility?Structural flexibility refers to the ability of the supply chain to adapt to fundamentalchanges in the business environment. Here, we first and foremost consider the centresof gravity in a firms’ supply chain system. We can broadly define “centre of gravity” inthis context as the nexus between supply and demand. Using a mechanical analogy,if each customer has a string to pull products from your factory (the more items, thestronger the pull) and major raw material and component suppliers hold strings thatpull the location of the manufacturing plants towards them: on balance, your centre ofgravity would be where all forces even each other out. And there might well be severalcentres that emerge, in many cases by product category, or by market region.

Why does this matter? The centre of gravity minimises the distance to yourcustomers, so this would be the best “local for local” solution. A firm might also have

Efficient supply chain Adaptable supply chain

Focus Establish control to reducevariability and thus cost to compete

Embrace volatility and developsuperior ability to adapt

Decision time horizon Short-term, quarterly results Long-term viability, whilemaintaining positive cash flow

View on turbulence Bad, as it causes instability and cost Inevitable, hence the need to pre-empt it by creating adaptablestructures

Approach to dealing withturbulence

Use SIX SIGMA and other tools toeradicate it where possible

Use tools to increase flexibility“bandwidth” to cope

Table III.Efficient versusadaptable supply chain

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Gaining structural flexibility• Corporate culture and mindset to embrace turbulence• Visibility and information sharing• Exploitative and explorative learning and absorptive

capacity • Investigate ‘local-for-local’ alternative to global sourcing

and centralised manufacturing

• Focus on the ‘economies of scope’ rather than the ‘economies of scale’

• Create ‘bandwidth’ through asset sharing, e.g. capacity and inventory, design for SC, dual sourcing, late configuration, rapid manufacturing, flexible labour arrangement, outsourcing, segmentation

• Adopt a ‘real options’ approach tosupply chain decision making

• Network orchestration• The impact of industry 4.0 technologies

18

Gartner’s Hype Cycle 2016

19

Gartner’s Hype Cycle 2017

20

Digital initiative investment’s returns

McKinsey Quarterly, February 2017, “The case for digital reinvention”

21

Products are more digitized while SCs are less so …

McKinsey Quarterly, February 2017, “The case for digital reinvention”

22

Explosion of Data in Supply Chains

Source:Rozados,I.andB.Tjahjono,(2014),“BigDataAnalyticsinSupplyChainManagement”ComplexityisdrivenbyVolume,Velocity,Variety,andVeracity!

23

Big Data Analytics Applications Across the SC

SourceSandersN.R.,(2016),“Howtousebigdatatodriveyoursupplychain”

of these applications the knowledge and insights provided are deep and the focushyper-specialized.

Marketing Applications

Marketing analytics applications are customer oriented and are on the sell sideof the supply chain. The nature of marketing has driven the development of big dataapplications that focus on capturing customer demand, enablingmicro-segmentation,and predicting consumer behavior. In fact, micro-segmentation has become a highlyimportant application of big data analytics. Although market segmentation has longbeen a marketing capability, the coupling of big data with sophisticated analytic toolshas enabled micro-segmentation at increasingly granular levels.27 Companies cannow use technology to gather and track data on the behavior of individual customers,and then combine these with traditional market research tools to gain greaterinsight. The collected data is increasingly tracked in real time, enabling companiesto quickly readjust their customer strategies. This is seenwith retailers such as NeimanMarcus where behavioral segmentation is matched with a multi-tier membershiprewards program.28 The company uses sophisticated analytics to identify key custom-ers and then creates targeted purchase incentives resulting in higher margin pur-chases from the company’s higher-margin customers.

EXHIBIT 1. Analytics Applications Across the Supply Chain

SOURCE MAKE MOVE SELL

Location-Based Marketing

In-Store Behavior Analysis

Customer Micro-Segmentation

Multichannel Marketing

Assortment Optimization

Distribution & Logistics Optimization

Transportation Alternatives

Routing

Scheduling

Vehicle Maintenance

Supplier Risk

Product Characteristics

Sourcing Channel Options

Supplier Integration Level

Supplier Negotiation

Inventory Optimization

Capacity Constraints

Facility Location

Facility Layout

Workforce Analytics

How to Use Big Data to Drive Your Supply Chain

30 UNIVERSITY OF CALIFORNIA, BERKELEY VOL. 58, NO. 3 SPRING 2016 CMR.BERKELEY.EDU

24

Data Analytics Maturity

SCN: So, it’s the typical “old dog, new tricks” problem?

DSL: No, I think it goes beyond simply being stubborn. Many of today’s executives

have risen through the ranks without the benefit of sophisticated data analytics, so a

large part of their decision-making process is simply based on “gut” instinct. So, when

the analytics output differs from their instinct, it is understandably difficult for them to

accept the notion that “raw” numbers and a “black box” can accurately reflect the

intricacies of their business better than their intuition.

SCN: Any final thoughts?

DSL: Data analytics is still such a new area and there is no one standard approach, so it

is hard for companies to know where to start. But, not doing anything is not a choice.

For example, if you think about pricing in retail or high tech, the first thing you want to

do is look at your data and determine how to develop an effective predictive model. If

you can do that, you are ready to move to the price optimization level. Most companies

get stuck trying to figure out how to do everything in one big bang approach that may

Copyright © 2016 Avnet, Inc. | All rights reserved.8 | SUPPLY CHAIN NAVIGATOR

Winter 2016

25

Blockchain: Foundational technology adoption trajectory

entered in one copy, all the other copies are simultane-ously updated. So as transactions occur, records of the value and assets exchanged are permanently entered in all ledgers. There is no need for third-party inter-mediaries to verify or transfer ownership. If a stock transaction took place on a blockchain-based system, it would be settled within seconds, securely and ver-ifiably. (The infamous hacks that have hit bitcoin ex-changes exposed weaknesses not in the blockchain itself but in separate systems linked to parties using the blockchain.)

A FRAMEWORK FOR BLOCKCHAIN ADOPTIONIf bitcoin is like early e-mail, is blockchain decades from reaching its full potential? In our view the an-swer is a qualified yes. We can’t predict exactly how many years the transformation will take, but we can guess which kinds of applications will gain traction first and how blockchain’s broad acceptance will eventually come about.

In our analysis, history suggests that two dimen-sions affect how a foundational technology and its business use cases evolve. The first is novelty—the degree to which an application is new to the world. The more novel it is, the more effort will be required to ensure that users understand what problems it solves. The second dimension is complexity, represented by the level of ecosystem coordination involved—the number and diversity of parties that need to work to-gether to produce value with the technology. For exam-ple, a social network with just one member is of little use; a social network is worthwhile only when many of your own connections have signed on to it. Other users of the application must be brought on board to gener-ate value for all participants. The same will be true for many blockchain applications. And, as the scale and impact of those applications increase, their adoption will require significant institutional change.

We’ve developed a framework that maps innova-tions against these two contextual dimensions, di-viding them into quadrants. (See the exhibit “How Foundational Technologies Take Hold.”) Each quad-rant represents a stage of technology development. Identifying which one a blockchain innovation falls into will help executives understand the types of chal-lenges it presents, the level of collaboration and con-sensus it needs, and the legislative and regulatory ef-forts it will require. The map will also suggest what kind of processes and infrastructure must be established to facilitate the innovation’s adoption. Managers can use it to assess the state of blockchain development in any industry, as well as to evaluate strategic investments in their own blockchain capabilities.

Single use. In the first quadrant are low-novelty and low-coordination applications that create better, less costly, highly focused solutions. E-mail, a cheap alternative to phone calls, faxes, and snail mail, was a

single-use application for TCP/IP (even though its value rose with the number of users). Bitcoin, too, falls into this quadrant. Even in its early days, bitcoin offered im-mediate value to the few people who used it simply as an alternative payment method. (You can think of it as a complex e-mail that transfers not just information but also actual value.) At the end of 2016 the value of bit-coin transactions was expected to hit $92 billion. That’s still a rounding error compared with the $411 trillion in total global payments, but bitcoin is growing fast and increasingly important in contexts such as instant pay-ments and foreign currency and asset trading, where the present financial system has limitations.

Localization. The second quadrant comprises inno-vations that are relatively high in novelty but need only a limited number of users to create immediate value, so it’s still relatively easy to promote their adoption.

HOW FOUNDATIONAL TECHNOLOGIES TAKE HOLDThe adoption of foundational technologies typically happens in four phases. Each phase is defined by the novelty of the applications and the complexity of the coordination efforts needed to make them workable. Applications low in novelty and complexity gain acceptance first. Applications high in novelty and complexity take decades to evolve but can transform the economy. TCP/IP technology, introduced on ARPAnet in 1972, has already reached the transformation phase, but blockchain applications (in red) are in their early days.

DEGREE OF NOVELTY

AMOU

NT OF

COMP

LEXITY

AND C

OORD

INATIO

N

SUBSTITUTIONRETAILER GIFT CARDS

BASED ON BITCOIN

AMAZON ONLINE BOOKSTORE

TRANSFORMATIONSELF-EXECUTING

SMART CONTRACTS

SKYPE

SINGLE USEBITCOIN PAYMENTS

E-MAIL ON ARPANET

LOCALIZATIONPRIVATE ONLINE

LEDGERS FOR PROCESSING FINANCIAL

TRANSACTIONSINTERNAL CORPORATE

E-MAIL NETWORKS

LOW HIGH

HIGH

JANUARY–FEBRUARY 2017 HARVARD BUSINESS REVIEW 123

Iansiti M. and Lakhani K. R., (2017), The truth about the blockchain”, Harvard Business Review, January-February

26

Internet of Things

bmiresearch.com 4

What Is The IoT? The coming decades will see the emergence of the ‘Internet of Everything’

The IoT is generic label for the trend of connecting 'things' - usually electronic devices - that can passively or actively monitor, collect and exchange data over a wireless communication network. Two-way connectivity means that these 'things' can interact with or intervene on their environment. Thanks to timely interventions, diminished need for manpower and greater accuracy the cost benefits and efficiency gains brought by the IoT will appeal to almost every business and social sector.

Connected Devices Forecast. f = BMI forecast. Source: BMI

Connected Devices (mn), 2015-2050

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

2015 2020f 2025f 2030f 2035f 2040f 2045f 2050f

Smartphones M2M Mobile Computing Wearables

� Smartphones currently dominant but will gradually lose primacy ¾ They will remain the way

consumers connect to the IoT

� Machine-to-Machine (M2M) connections will be the dominant part of the connected-devices ecosystem.

� Tablets (most prominent mobile computing) will join PCs as favourite IoT connector for enterprises.

� Wearables will see a rapid take-up when technology and habits will allow it ¾ Wearable connected devices will

be most relevant for healthcare applications


bmiresearch.com 4

What Is The IoT? The coming decades will see the emergence of the ‘Internet of Everything’


Connected Devices Forecast. f = BMI forecast. Source: BMI

Connected Devices (mn), 2015-2050

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

2015 2020f 2025f 2030f 2035f 2040f 2045f 2050f

Smartphones M2M Mobile Computing Wearables

� Smartphones currently dominant but will gradually lose primacy ¾ They will remain the way

consumers connect to the IoT

� Machine-to-Machine (M2M) connections will be the dominant part of the connected-devices ecosystem.

� Tablets (most prominent mobile computing) will join PCs as favourite IoT connector for enterprises.

� Wearables will see a rapid take-up when technology and habits will allow it ¾ Wearable connected devices will

be most relevant for healthcare applications

27

Explorativeabsorptivecapacity

Exploitativeabsorptivecapacity

The 2nd machine technologies to design an adaptable supply chain

Datagathering

Dataprocessing

Datainterpreting

indicating that we are entering a renewed period of high volatility. Furthermore, thecontinued wide band of variation for individual constituents of the index post-2008 suggeststhat we are still in an “era of turbulence”.

The need for a new mental modelThe notion of shifting centres of gravityThe changes in the context or landscape in which supply chains operate is an observationthat is shared by SCM scholars (see e.g. Bowersox et al., 2000; Sweeney, 2013; Stevens andJohnson, 2016; Spekman and Davis, 2016), and practitioners alike. Since 2010 we havepresented the supply chain volatility data at academic seminars, to executive audiences,MBA classes at Oxford, Cambridge, Cranfield and elsewhere. In these presentations we haveasked participants for their views on the key variables that cause turbulence in theirrespective firms’ supply chains, and have followed this with a discussion about what kind ofeffects the various turbulence factors had on their supply chains. By far the most prevalentfactor causing supply chain volatility was related to “materials and components”.Here, quality levels, availability of materials and components, lead-time of globalsuppliers and the limited flexibility of global suppliers were mentioned. Also, specific rawmaterials with significant volatility were seen as being: steel, copper, aluminium and rareearth metals (see above).

The second most important category mentioned was political factors, such as regulation(e.g. related to emissions and labour), import/export taxes, corruption, labour cost and theprocess for granting licenses or regulatory approval.

Combined, turbulence related to materials, supply and political issues were mentionedtwice as often as all the other factors: this is important to note, as despite their newscoverage, neither natural disasters (such as Tsunamis, earthquakes or ash clouds) norenergy/transportation cost (either directly as oil or fuel price, or indirectly as airfreight orcontainer shipment cost) were mentioned anywhere nearly as often as we had expected.

Additional factors mentioned include the “economy” in general terms, referring tocustomer demand as well as macroeconomic uncertainty in national economies. Alsomentioned, but to a lesser extent, were the cost of energy, the cost of transportation (especiallythe cost of airfreight), and technology (in terms of disruptive technologies, the quality of ITsystems and data), and lastly, access to finance (for both customers and suppliers).

Whilst individual firms were always affected by idiosyncratic factors, overall it is worthnoting that all groups presented a uniform picture of factors. Obviously we recognise thatdata collected in this way are anecdotal, but it is indicative of a global shift that can occur intoday’s supply chains. As global forces on both the “supply side” and the “demand side”continue to oscillate, so too does the “centre of gravity” of the supply chain. For example, afirm solely serving customers in one geographical region, say Europe, will experience a strongdemand side pull towards Europe. Equally, a firm operating across all regions may find itscentre of gravity is being pulled towards low-labour cost regions on the supply side. Figure 5outlines key factors on the demand and supply sides that “pull” the centre of gravity.

Supply Side Vectors Demand Side Vectors

Centreof

Gravity

Labour Costs

Materials andResource Availability

Skills

Transport Costs

Changing Demographics

Changing CustomerPreferences

Disposable Income

Industry Development

Figure 5.Factors causing shiftsin the centre of gravityin a supply chain

10

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Performancemanagement

CollaborationOrderManagement

SCStrategyPlanning

PhysicalFlow

1st level:Supplychaindesigngoal2nd level:Requiredcapability3rd level:Industry4.0levers

28

A framework for embedding structural flexibility in supply chain design enabled by 2nd machine technologies

Adopted from Christopher M. and Holweg M. (2017), modified by presenter

Volatilityinthebusinessenvironmentposesriskofsupply

chainfailure

Ambidextrousandadaptablesupplychainstructure:Mitigateexposuretoensuingriskof:

SCAbsorptivecapacityof2ndmachinetechnologyinbothexplorativeandexploitative

manners

1.Internalrecoverycost 2.Externalrecoverycost

• Excessinventory• Obsolescencecosts• Fire-fightingcost• Unbalanced/idlecapacity• Overtimepayments

• Lostsales• Stock-outs• Salesincentives• Contractualpenalties• Costofexpeditedshipments

3.Resiliencecost

• Costoftime,capacityandinventorybuffers• Hedgingandinsurancecost• Costofaccesstosurgecapacity,contactsmanufacturingand

sharedservices• Increasedtransactioncostduetodiversificationandredundancy

infootprint

Trade-offsSupplychainscosts:physical,

transactionalandmarketability

2929

QUESTIONS? COMMENTS!

SUGGESTIIONS!

Javad [email protected]

30 30

Digital Tech-Enabled Supply Chain

31

Tipping Points Expected by 2025

31


TippingPoint Occurrenceprobability

10%ofthepeoplewillbewearingclothesconnectedtotheInternet 91.2%

90%ofpeoplewillhaveunlimitedandfree(advertising-supported)storage 91%

1trillionsensorswillbeconnectedtotheInternet 89.2%

Thefirstrobotic pharmacist 86.5%

10%ofreadingglasseswillbeconnectedtotheInternet 85.5%

80%0fpeoplewill haveadigitalpresenceontheInternet 84.4%

Thefirst3D-printed carwillbeinproduction 84.1%

ThefirstgovernmentwillreplaceitscensuswithBigDatasources 82.9%

Thefirstimplantablemobilephonewillbeavailablecommercially 81.7%

5%ofconsumerproductswillbeprintedin3D 81.1%

90%ofthepopulationwillbeusingsmartphones 80.7%

32

Tipping Points Expected by 2025

32


TippingPoint Occurrenceprobability

90%ofthepopulationwillhaveregularaccesstotheInternet 78.8%

Driverlesscarswillequal10%ofallcarsonU.S.roads 78.2%

Thefirsttransplantofa3D-printedliverwilloccur 76.4%

30%ofcorporateauditswillbeperformedbyAI 75.4%

Taxwillbe collectedforthefirsttimebyagovernmentviatheBlockchain 73.1%

Globally,moretrips/journeyswilloccurvia carsharingthaninprivatecars 67.2%

10%ofglobalgrossdomestic productwillbestoredonBlockchain technology 57.9%

ThefirstAImachinewillsitonaboardofdirectors 45.2%

supply chain design in the era of increased...

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