+1(978)310-4246 credencewriters@gmail.com
  

Description

Write a summary for the article attached. It should be at least half a page to a page long. double spaced.

The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/0960-0035.htm
IJPDLM
47,1
2
INVITED PERSPECTIVE
Supply chain 2.0 revisited: a
framework for managing volatilityinduced risk in the supply chain
Martin Christopher
Received 3 September 2016
Revised 25 October 2016
1 November 2016
Accepted 2 November 2016
School of Management, Cranfield University, Bedford, UK, and
Matthias Holweg
Said Business School, University of Oxford, Oxford, UK
Abstract
Purpose – The purpose of this paper is to provide an update to the Supply Chain Volatility Index (SCVI), and
expand on prior work by presenting a conceptual framework illustrating how firms can deal with persistent
volatility, the ensuing risk and mitigate the cost implications for their supply chain operations.
Design/methodology/approach – The authors use long-term time series of secondary data to assemble a
“basket” of key indicators that are relevant to the business context within which global supply chains operate.
The authors also report on five years of feedback gained from presentations of the SCVI to scholars
and practitioners.
Findings – Volatility has reduced from record levels experienced during the global financial crises, yet
remains at levels considerably higher than prior to the crisis, with no sign of a return to the more stable
conditions that prevailed when many current supply chain networks were designed.
Research limitations/implications – The authors reaffirm that new mental models are needed which
embrace volatility as a factor in supply chain design, rather than seek to eradicate it in supply chain
operations. Traditional static “network optimisation” based on a simple definition of low unit cost seems no
longer appropriate under conditions of persistent volatility.
Practical implications – The authors provide a conceptual link of volatility, risk and cost in the supply
chain, and outline how firms can develop a supply chain strategy by managing their exposure to volatility.
Originality/value – The authors challenge the common assumption that volatility invariably leads to risk
and higher cost in the supply chain. Instead the authors argue that the supply chain structure can mitigate the
exposure to supply chain risk. The authors introduce the concepts of recovery and resilience cost within a
framework designed to help firms manage volatility-induced risk by minimising the adverse cost implications
of volatility in their supply chains.
Keywords Uncertainty, Risk, Flexibility, Volatility, Supply chain design
Paper type Research paper
International Journal of Physical
Distribution & Logistics
Management
Vol. 47 No. 1, 2017
pp. 2-17
© Emerald Publishing Limited
0960-0035
DOI 10.1108/IJPDLM-09-2016-0245
Volatility: the “new normal” in supply chain management?
Practitioners and scholars alike readily subscribe to the notion that volatility in the
business environment has dramatically increased, and global events over the past decade
provide ample evidence to support this view. Volatility represents a major risk to supply
chains which needs to be understood and managed. However, much of the discussion of
volatility and its effects on the supply chain has been largely anecdotal, or refers to single
cases of supply chain failure. For this reason we embarked on a journey to measure and
analyse the different facets and patterns of volatility that supply chains operate in and to
seek evidence that volatility has increased in recent years. In our 2011 paper we
demonstrated this increase in volatility empirically (Christopher and Holweg, 2011).
We presented our work in the wake of the global recession, and following significant rises
in energy prices. Five years on, we still feel the tremors of the global financial crisis yet
whilst some key indicators have fallen drastically in absolute terms, overall levels of
volatility remain high. The volatility of the price of crude oil over the past decade, for
example, makes a case in point.
As we have noted, the notion that the world of business has become more “volatile”,
“turbulent” or “complex” is widely accepted. Even a cursory scan of the business press
suggests there is little doubt that in recent years the commercial environment has
become more turbulent and hence less predictable. Whereas in the past it was standard
practice to plan ahead – with a time horizon of months, if not years – now the challenge is to
find ways to become much more responsive to events as they happen. Traditionally,
businesses have relied on a forecast-driven approach when deciding on their supply
chain strategy, i.e. using of projections of future demand and costs, often based on historical
data, as basis for planning. Such an approach works well when firms operate in a relatively
stable environment; clearly it is less effective in the volatile conditions that organisations
face today.
Many firms today are dependent on supply chain networks that were designed at a time
when the business environment was more certain, and under the assumption that the future
would be much like the past. Now that those organisations are confronted with significantly
changed circumstances, it may be the case that conventional supply chain structures and
practices are no longer fit for purpose.
There has been much debate in the literature on supply chain risk and the need to build
resilience into supply chains (see e.g. Christopher and Peck, 2004; Sheffi, 2005; Tang, 2006;
Ponomarov and Holcomb, 2009; Pettit et al., 2010). However often these contributions are
based on the simplistic assumptions that: uncertainty leads to risk and ultimately cost, and
that resilience to counter these risks can be provided at no extra cost. We challenge both
these notions and develop a conceptual framework that links turbulence to risk and cost in
the supply chain. In fact we argue that many of the old business operations models are no
longer appropriate for today’s conditions. Most of those early models were developed
against a backdrop of reasonable stability in the business environment. This context has
changed. In the past the emphasis was on “network optimisation” in terms of operating
costs. Under current conditions, the focus needs to be upon developing the
right “bandwidth” of flexibility to cope with volatility in the business environment.
We build on this idea and argue that supply chain design is the key mechanism by which
firms can address the risks that arise from volatility. This in turn allows them to minimise
supply chain cost, in terms of internal and external failure, as well as the hedging cost that
may be involved.
In this paper we will present an update of the Supply Chain Volatility Index (SCVI), and
respond to suggestions concerning various possibilities for improving the index following
its first publication in 2011. We outline the implications of persistent volatility in
present-day supply chains, and develop a framework suggesting how firms can manage
their supply chains by embracing volatility, managing their exposure to ensuing risks and
mitigate the adverse cost implications of volatility.
The “SCVI”: critique and update
In order to illustrate the degree to which overall turbulence in our business environment
is changing overtime, we have developed a “SCVI”. The index was formally proposed in
Christopher and Holweg (2011), and depicts the coefficient of variation (CoV) of eight
key business parameters: key currency exchange rates, the Bank of England base rate, the cost
of raw materials (such as oil, gold and copper), an index of stock market volatility, and
the cost of shipping[1]. For each of these we measure the band of annual volatility, recognising
that not all the indices move at the same time. We are aware of the possibility of
multicollinearity, as many pairs of factors are correlated, but not all react to events or
global shifts in the same way. So what we highlight is a “band of volatility” across all the
indicators. To illustrate how the overall business environment is shifting, we have added an
aggregate or meta-index of variation to the chart. This is the mean CoV across all eight indices.
Supply chain
2.0 revisited
3
IJPDLM
47,1
4
Our argument here is that it does not matter whether there is an increased level of volatility in
the oil price, the exchange rate, or the Bank of England base rate. What does matter is when
several of these indicators move together[2], as this changes the general business climate in
which firms operate.
A critique of the methodology
Our approach gives rise to two questions: whether the right variables have been chosen, and
second, whether the computation of a “mean coefficient of variation” is a meaningful and
valid 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. Arguably
any other combination would have been equally representative, or better. In Figure 1 we
present our logical justification for our choice of indicators in terms of their direct and
indirect impact on the business climate. We challenge readers to review, revise and expand
on our selection of variables. Equally there is a case for suggesting that industry-specific, or
even company-specific indices, might be constructed to reflect the particular environment in
which a particular business operates.
With regard to the computation of the index, the CoV is an appropriate, scale-free metric
that highlights the degree to which a series of data oscillates. It does not, however, allow any
judgement as to whether the variation measured is unusual, or problematic. The results thus
still need to be assessed qualitatively. In comparison, it is worth considering how the stock
market Volatility Index (VIX) and the Baltic Dry index (BDI) are computed. VIX is a
weighted 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 VIX
is the square root of the risk neutral expectation of the S&P 500 variance over the next
30 calendar days, and is quoted as an annualised standard deviation. The BDI, on the other
hand, is a combination of quoted prices. Every working day, a panel of international
shipbrokers submits their view of current freight cost on various routes to the
Baltic Exchange. The routes are meant to be representative, i.e. large enough in volume to
matter for the overall market. These rate assessments are then weighted together to create
both the overall BDI and the other specific indices. As can be seen, like the SCVI, both VIX
and BDI also make arbitrary judgements as to what to include, and revert back to simple
statistical metrics of variance and deviation.
Other potential weaknesses of the SCVI include multicollinearity between variables, and
autocorrelation in the time series. The former is a well-known problem with macroeconomic
variables used in statistical analyses. As, however, we are not proposing any inference
Volatility in the business environment
Demand
Commodity
prices
Cost of
energy
Exchange Copper and
Baltic Dry
rates
Crude oil price
Index
Figure 1.
Linking aspects of
supply chain volatility
to the index variables
Disruptive
Innovation
Political
unrest
(Access to)
finance
Gold Bullion
price
VIX
Bank base
rate
Supply Chain Volatility Index
Direct relationship
Indirect relationship
based on the SCVI data, there is no over-specification of any variable. Second,
autocorrelation in time series data is an equally well-known problem. The best predictor of
the next value of a macroeconomic time series data point at t is its value at t−1.
One of the methodological shortcomings that we have experienced was a lack of
sensitivity in the CoV to changes when parameters exhibit relatively low absolute mean
values, and especially when presented in the form of annualised data. We explored both
annual CoV, based on monthly data, and a revised methodology based on 12-month rolling
CoV. Whilst both qualitatively provide the same results, the latter methodology better
accounts for the greater volatility that is observed with the annual data.
A comment on shipping cost indices
One comment in response to our initial presentation of the SCVI concerned the suitability of
the BDI as a generic proxy for assessing volatility in the cargo freight rates. Whereas the BDI
is the standard reference for dry bulk, it may not be as useful for container-based freight rates.
According to the Baltic Exchange, dry cargo in general accounts for approximately two-thirds
of seaborne trade volumes. Container traffic, which is not accounted for by the BDI, is just
over 10 per cent by weight of dry cargo, however, much greater in terms of value. It is
therefore instructive to compare the BDI with the Shanghai Containerized Freight Index
(SCFI), a more recent standard reference for container-based transportation cost.
In maritime shipping, the BDI represents a unified and broker-independent index for dry
bulk raw material such as coal, iron ore and grain. The BDI is a composite of sub-indices
covering four different sizes of dry bulk vessel: Capesize, Panamax, Supramax and
Handysize. It is calculated by taking the time charter components (on a per ton and daily
hire basis) from 20+ individual shipping routes of leading shipbrokers that have been
reported daily since 1985.
In general, the BDI is considered a leading economic indicator and barometer for global
commerce because it tracks the cost of shipping pre-production (raw) materials, which
typically are subject to low levels of speculation. Changes in the BDI can provide insight on
global supply and demand trends for raw materials in real time. In particular, the index can
react quickly to larger global demand changes as the supply of ships is considered to
be inelastic, due to the long lead times for delivery of new vessels on the one hand, and the
difficulty and time required for decommission on the other.
For containerised freight, which has been one of the fastest growing segments of the
global freight market, there is no equivalent unified index to the BDI. With container
shipping there are two types of price referencing available: charter rate indices compiled by
shipbrokers, or freight rate indices that established by maritime advisory firms or
government organisations. Charter rate indexes include the Harpex, Maersk Broker
Container Index, Howe Robinson Container Index or ConTex. Major freight rate indices
include: China Containerized Freight Index, SCFI, and Drewry Container Freight Rate Index.
We focus on the SCFI as it targets the spot rates of the container transport market, which is
more sensitive to changes in demand, and because the freight information is reported by a
cross section of panelists, including major liner companies, shippers and freight forwarding
agents. The SCFI reflects rates of 15 worldwide shipping routes, and was set up in October
2009 with an index basis of 1,000 points.
The SCFI is thus a very recent creation, and although it may serve well as a good
predictor for future container-based shipping rates, long-term comparisons of spot price
volatility cannot be drawn, as retrospective data pre-2009 is not available. With the data
available, we could not determine a significant correlation between the BDI and SCFI
(pairwise correlation coefficient: 0.22; p W0.10; linear prediction R2: 0.028).
A general observation is that freight rates have become increasingly volatile since early
2000s due to macroeconomic factors, such as economic and political conditions, but also
Supply chain
2.0 revisited
5
IJPDLM
47,1
6
endogenous factors in the shipping industry regarding fleet expansion and shipping routes.
At present, the shipping industry (bulk and container) is plagued by overcapacity and spot
freight rates are far below break-even levels. With the integration of China into the world
economy, trade volumes of containerised and bulk goods increased dramatically and
shipping capacity struggled to keep up with demand. Due to the worldwide financial and
economic crisis freight demand was severely curtailed and the BDI fell from 11,000 points to
700 within six months by the end of 2008. With the greatly expanded global container
capacity, shipping lines engaged in a price war that drove freight rates down below breakeven levels. The risks and uncertainty linked to the severe volatility in shipping rates are
likely to cause prolonged turbulence as shipping clients increasingly negotiate alternative
contractual arrangements. Contracts are now often index linked or renegotiated on a
quarterly basis, so that the spot rates have a much greater impact on actual price setting.
This in turn increases price volatility in the market once more.
In summary, both BDI and the SCFI are important indicators of supply chain volatility
and, as we have shown, are two indicators that do not move in sync. In our index we have
opted for the BDI as a metric of shipping cost volatility, largely due to the fact that the BDI
allows for a longer time-series comparison. We do admit, however, that for specific
questions or circumstances firms and scholars may want to replace the BDI with the SCFI
when calculating the SCVI.
A comment on rare earth elements
A further suggestion that has been made to us is that because of the growing use of
“rare earth metals” in a range of products, fluctuations in the price of these materials
should be included in the SCVI. This comment was made frequently by executives from
technology firms, who stated to have experienced serious volatility when sourcing rare
earth materials.
Contrary to their name, “rare” earths are in fact relatively abundant in the earth’s crust,
but discovered and accessible concentrations are significantly less common than for most
ordinary base and precious metal ores (Haxel et al., 2002). The vast majority of the world’s
rare earths supply comes from a small number of sources, and thus the primary concern
with rare earth availability is not their geophysical abundance, but rather whether rare
earth supply in general can expand at a sufficient pace to meet future demand (Alonso et al.,
2012). The rare earths are a group of 17 elements comprising the 15 lanthanides, scandium
and yttrium. These rare earth elements find application in a diverse variety of industrial
areas due to their nuclear, metallurgical, chemical, catalytic, electrical, magnetic and optical
properties. For example, rechargeable lanthanum-nickel-hydride (La-Ni-H) batteries are
gradually replacing Ni-Cd (cadmium) batteries and could eventually replace lead-acid
batteries in automobiles (Haxel et al., 2002). Every Toyota Prius has over 25 pounds of
lanthanum in its nickel-hydride battery (The Economist, 2010).
Table I lists the rare earths elements in their order of abundance in earth’s crust, and
provides an overview of their primary commercial uses.
High technology and environmental applications of rare earths have grown considerably
in diversity and importance over the past four decades. Many of their applications are
highly specific in that substitutes are inferior or unknown. With the expected rise of global
demand for conventional, hybrid and electrical automobiles as well as for portable mobile
devices and renewable energy equipment, the demand of rare earths is also likely to
increase. Many industries therefore depend on a secure and stable supply of rare earths that
are critical for their technologies. Although the worldwide availability of rare earth
resources is greater than the additional materials needed to meet expected future world
consumption, current annual demand exceeds world production. Strong growth is
particularly expected through future growth in magnet production (e.g. for wind turbines)
More abundant
rare earths
Major application
Less
abundant
rare earths
Scandium
Samarium
Aluminium alloys, lasers, camera lamps,
tracing agent, catalyst
Yttrium
Fluorescent lamps, ceramics, metal
alloy agent
Lanthanum
Batteries for electric motors (hybrid
vehicles), metal alloys, fluid-cracking and
auto catalyst
Cerium
Auto catalyst, petroleum refining, metal
alloys, glass polishing, lenses and displays
Praseodymium Audio equipment, magnets
Neodymium
Auto catalyst, petroleum refining, laptop
hard drives, audio equipment, magnets
for electric motors
Supply chain
2.0 revisited
Major application
Audio equipment, magnets for
electric motors, alloys
Gadolinium Magnetic refrigeration
7
Dysprosium Permanent magnets, audio
equipment, magnets for electric
motors (hybrid engines)
Erbium
Phosphors
Ytterbium Lasers, steel alloys
Europium
Colour for TV and computer screens
Terbium
Phosphors, permanent magnets
Holmium
Glass colouring, lasers
Thulium
Medical x-ray units
Lutetium
Catalysts in petroleum refining
Promethium No applications (least abundant)
Sources: Humphries (2012), Massari and Ruberti (2013), Chakhmouradian and Wall (2012), Jackson and
Christiansen (1993)
and for rechargeable NiMH batteries (for hybrid vehicles). Demand for cerium and
neodymium for use in automotive catalytic converters and catalysts for petroleum refining
will trend with refinery and automotive production.
Increasing demand, coupled with few global supply sources, have been key drivers of
price volatility for the various rare earth elements. Currently, China supplies about
97 per cent of the world’s demand for rare earths, with smaller amounts mined in the USA,
Australia, India, Malaysia and Brazil (Alonso et al., 2012). In recent years, this
quasi-monopoly has caused much volatility in the supply and prices of rare earths. Since
July 2010, China has set a production quota for rare earth oxides, citing domestic
requirements and environmental concerns, and restricting supply through a 40 per cent
reduction in export quotas and increase of taxes (Hatch, 2012). China’s near-complete control
over the world’s supply drove up prices between for rare earth oxides by 460-2,900 per cent
on average between 2009 and 2011. Prices for neodymium (US$241 per kilogram) and
dysprosium (US$1,380 per kilogram), used in permanent magnets, increased by 1,550 and
1,200 per cent, respectively, in 2011 compared with those in 2009. In 2012, Chinese exports
plummeted 71 per cent from 2011 amid WTO trade suits, and collaborative global attempts
to develop alternative rare earth supply sources and a break in worldwide demand. Since
2011 to mid-2013, rare earths prices have collapsed again by as much as 91 per cent in the
case of cerium oxide, and Chinese exports have recovered (a move likely to defend its
95 per cent global market share).
In the long term, it is expected that rare-earths-dependent industries will increasingly reduce
their reliance on China as the only source for many of the rare earths and a large number (400+)
of exploration and development projects outside of China are underway in 37 countries.
The recycling of rare earth metals from their end products is also expected to play an important
role in the future. Companies such as Toyota Motors and Hitachi already recycle rare earth
metals (Bloomberg, 2013). However, the currently small amount of in-use stock implies that this
would probably not be common practice for several years to come. Nevertheless, despite these
diversification efforts the Chinese quasi-monopoly is expected to last for at least another two
decades (Bradshaw et al., 2013). Thus, this setting exhibits a continuous and significant source
of supply chain volatility in regards to quantities and pricing. This volatility may be further
Table I.
Major commercial
uses of rare earth
elements
IJPDLM
47,1
8
exacerbated as rare earths have come under scrutiny due to the environmental and social
conditions 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, and
indeed have found extreme levels of volatility for rare earth elements’ spot prices between
2010 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 in
2011, which marks a 50 per cent increase over the SCVI peak in 2009. This extreme volatility
that technology-related industries have been facing since 2010 has exposed their
vulnerability and dependence on a few global sources. Latterly, technology manufacturers
have been increasing their focus on the redesign of products and processes to gain greater
flexibility and independence in the event of supply problems, yet their persistent reliance on
these elements upholds both prices and volatility in this market, and thus poses a strategic
risk to firms that are exposed to this market.
The SCVI 1970-2015
We have updated the SCVI with data up to mid-2016 to assess whether the mean variation
across all indicators as well as the band of variation has returned to greater stability after
the global financial crisis. Figure 2 shows the CoV as our measure for the SCVI for complete
years 1970-2015.
The main interest of the index is both the absolute level of volatility, as well as the
changes 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 “Bollinger
Bands” (Bollinger, 2002) to provide a guide for determining whether changes in the index
are significant. Bollinger Bands use a 20 months moving average plus or minus two
standard deviations to set the level of the bands; if in any one period the index breaks
through the band, then this might be considered to be an indication of an emerging out-ofthe-ordinary situation (see Figures 3 and 4).
Since the recent financial and economic crisis, large and lasting volatility can be
observed in raw material prices (from 2008 to 2013), which was identified as the most
prevalent factor for businesses in general through our interaction with industry leaders
when 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, and
in turn stock markets can react erratically to local and global supply chain issues or
100%
90%
80%
70%
60%
50%
40%
30%
Figure 2.
Supply Chain
Volatility Index
1970-2015, with
min-max interval
20%
10%
0%
1970
1975
1980
1985
Max
Min
1990
1995
Mean CoV
2000
2005
2010
Linear (Mean CoV)
2015
Supply chain
2.0 revisited
40%
35%
30%
25%
20%
9
15%
10%
5%
0%
1970
1975
1980
1985
1990
1995
2000
2005
2010
Mean CoV
20-period MA
20-period MA – 2*sigma
20-period MA + 2*sigma
2015
Figure 3.
Supply Chain
Volatility Index
1970-2015, with
Bollinger Bands
20%
15%
10%
5%
0%
2000
2005
2010
Mean CoV
20-period MA
20-period MA – 2*sigma
20-period MA + 2*sigma
2015
Linear (Mean CoV)
disruptions (Hendricks and Singhal, 2003; Hendricks et al., 2009). In contrast, exchange
rates and other monetary indicators, on average, remained rather stable in comparison
over this period.
So, what pattern does the update of the SCVI reveal? First and foremost, the data up to
first half of 2016 suggest an initial return to greater stability after prolonged levels of
volatility during the crisis years 2008-2009, which saw the sharpest decline in worldwide
real GDP growth on record (a negative growth of 0.7 per cent for 2009). However despite
there being seemingly more stability across all selected parameters we can observe a much
greater bandwidth in terms of turbulence over the last decade compared to any of the other
four decades on record before. In particular, shipping costs that had been fairly stable from
the 1980s (the beginning of our records) until 2003 have exhibited dramatically increased
volatility since then. The index also exhibited an uncommon prolonged level of turbulence
throughout the Global Financial and Economic Crisis from 2008 to 2010, with large and
lasting amplitudes in the CoV, and continuing volatility especially in stock markets,
shipping prices and raw materials to date.
Most recently, as shown in Figure 4, the index has been rising above the “crisis level” of
10 per cent which over the past decades has only been associated with major economic or
geopolitical unrest. Also, as of 2016 the index has reached the upper Bollinger Band,
Figure 4.
Supply Chain
Volatility Index
2000-2015, with
Bollinger Bands
IJPDLM
47,1
10
Figure 5.
Factors causing shifts
in the centre of gravity
in a supply chain
indicating that we are entering a renewed period of high volatility. Furthermore, the
continued wide band of variation for individual constituents of the index post-2008 suggests
that we are still in an “era of turbulence”.
The need for a new mental model
The notion of shifting centres of gravity
The changes in the context or landscape in which supply chains operate is an observation
that is shared by SCM scholars (see e.g. Bowersox et al., 2000; Sweeney, 2013; Stevens and
Johnson, 2016; Spekman and Davis, 2016), and practitioners alike. Since 2010 we have
presented the supply chain volatility data at academic seminars, to executive audiences,
MBA classes at Oxford, Cambridge, Cranfield and elsewhere. In these presentations we have
asked participants for their views on the key variables that cause turbulence in their
respective firms’ supply chains, and have followed this with a discussion about what kind of
effects the various turbulence factors had on their supply chains. By far the most prevalent
factor causing supply chain volatility was related to “materials and components”.
Here, quality levels, availability of materials and components, lead-time of global
suppliers and the limited flexibility of global suppliers were mentioned. Also, specific raw
materials with significant volatility were seen as being: steel, copper, aluminium and rare
earth 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 the
process for granting licenses or regulatory approval.
Combined, turbulence related to materials, supply and political issues were mentioned
twice as often as all the other factors: this is important to note, as despite their news
coverage, neither natural disasters (such as Tsunamis, earthquakes or ash clouds) nor
energy/transportation cost (either directly as oil or fuel price, or indirectly as airfreight or
container shipment cost) were mentioned anywhere nearly as often as we had expected.
Additional factors mentioned include the “economy” in general terms, referring to
customer demand as well as macroeconomic uncertainty in national economies. Also
mentioned, but to a lesser extent, were the cost of energy, the cost of transportation (especially
the cost of airfreight), and technology (in terms of disruptive technologies, the quality of IT
systems and data), and lastly, access to finance (for both customers and suppliers).
Whilst individual firms were always affected by idiosyncratic factors, overall it is worth
noting that all groups presented a uniform picture of factors. Obviously we recognise that
data collected in this way are anecdotal, but it is indicative of a global shift that can occur in
today’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, a
firm solely serving customers in one geographical region, say Europe, will experience a strong
demand side pull towards Europe. Equally, a firm operating across all regions may find its
centre of gravity is being pulled towards low-labour cost regions on the supply side. Figure 5
outlines key factors on the demand and supply sides that “pull” the centre of gravity.
Supply Side Vectors
Demand Side Vectors
Labour Costs
Changing Demographics
Materials and
Resource Availability
Skills
Transport Costs
Centre
of
Gravity
Changing Customer
Preferences
Disposable Income
Industry Development
All supply chains have a centre of gravity which is determined by the combined effects of
the “pull” of various forces on the demand and the supply sides of the firm. The resultant
centre of gravity impacts decisions on where factories should be located, where materials
should be sourced and where strategic inventories should be positioned. A number of
important issues need to be weighed in the balance when supply chain design decisions are
taken. On the demand side the forces or vectors that will impact the centre of gravity include
changing demographics, hereby population growth dynamics and changing age profiles
mean that some markets globally are growing more rapidly whilst others are shrinking.
For example, Unilever now reports that over half its revenues come from developing
countries. Also, differences in disposable income are an important factor. A major change is
taking place regarding the relative growth in spending power in different countries.
Traditional markets in the west which once dominated global spending are now being
overtaken by the emerging economies in terms of expenditure. Also, as populations
transition from being predominately rural towards increasingly urban and as their
disposable income rises, so too does the pattern of consumption change. The massive
growth in the demand for cars in China and India provides a good example of this as does
the changes in diet now occurring in many emerging economies with a consequent rise in
the demand for dairy and meat products.
Similarly on the supply side a number of factors will act as countervailing forces
impacting the centre of gravity. These include relative labour costs and the availability of
resources and capabilities. The major shift in industrial production in recent years away
from western economies to low-cost countries has had a major impact on trade flows and the
level of demand for raw materials. Serving these fast growing markets whilst still needing to
maintain a presence in static or declining markets is a challenge many companies face
today. Many sourcing decisions in recent decades have been motivated by the desire to take
advantage of lower labour costs. So-called “low cost country sourcing” has been based on
the desire to improve competitiveness by manufacturing or sourcing in locations, where
labour costs are a fraction of more traditional locations. However, what were once
significant differentials in labour costs have often been eroded through wage inflation.
Likewise new potential contenders for the description of low-cost countries have emerged.
Inevitably the availability and cost of key input materials and resources such as metals,
energy, chemicals and other commodities are a major influence on location decisions.
With rising demand and, in some cases, declining supply the availability and prices of these
critical input factors can be dramatically affected. There is a growing realisation amongst
some established manufacturing companies that they will have to re-assess their current
supply chain arrangements, as the production economics that prevailed in the past may no
longer apply. Consider Amazon UK, for example, who specifically state that the location
decision for their latest UK fulfilment centre in Manchester was made considering the
company’s future centre of gravity within the UK market, and that it is not optimal
considering their current situation. The decision was made as Manchester is the most likely
centre of gravity in the medium term.
As industries continue to become more knowledge intensive and dependent upon specific
skills and capabilities, access to them becomes ever-more critical. Even in times of high
unemployment companies in many sectors find that they face skills shortages, for example
information technology specialists, software designers and engineers. Whereas once it was
the western world that pre-dominated in the supply of these skills, this is rapidly changing
as the levels of education and training in the newly emerging economies accelerate
(Huo et al., 2015).
Because the likelihood is that the centre of gravity of a supply chain is going to change
more frequently in the future, given the volatility of the business environment, the need for
flexibility in the supply/demand network increases. Many companies find themselves in a
Supply chain
2.0 revisited
11
IJPDLM
47,1
12
situation where they have invested in specific supply chain solutions which are often fixed
for a period of time, e.g. factories, distribution centres, supply arrangements, etc. As a result
they may find it difficult to re-configure the network as conditions change. Hence, the
likelihood is that the network is no longer “optimal” for current conditions. Indeed it can
be argued that because today’s highly inter-connected global supply/demand networks are
akin to complex systems they can never actually be “optimized”. All that supply chain
decision makers can hope to do is to create solutions that are flexible enough to provide
“satisfactory” solutions in an ever-changing environment. We refer to this ability to quickly
change the actual shape of a supply/demand network as structural flexibility.
What are the key enablers of structural flexibility?
Structural flexibility is the term we use to describe the ability of a firm to re-configure its supply/
demand network in response to changes in the business environment, not unlike Prahalad and
Hamel’s concept of a “strategic architecture” (Prahalad and Hamel, 1990). Like other SCM
scholars (e.g. Stevens and Johnson, 2016; Spekman and Davis, 2016), our observations have led
us to the conclusion that many conventional supply chain lack this flexibility and, as a result,
may not be operating efficiently or effectively in today’s circumstances. In other words,
a network that was designed to be “optimal” for the conditions prevailing in the past may be
distinctly suboptimal in today’s greatly different world. Given the challenge, how can
organisations seek to re-engineer their supply chains to avoid these problems in the future?
Perhaps the most critical enabler, but the one most difficult to achieve, is a corporate
culture and “mindset” that is open to change and is comfortable with frequent changes to
processes and working practices. Also, because some of the enablers of structural
flexibility – discussed below – involve much higher levels of collaborative working across
organisational boundaries, there needs to be a willingness to actively create “win-win”
partnerships across the supply chain, analogous to the “extended enterprise” concept
proposed by Spekman and Davis (2016).
Given that this co-operative approach to working across the extended enterprise can be
achieved, there are a number of critical factors that underpin structural flexibility. First and
foremost, visibility and information sharing is a crucial enabler. The ability to see from one
end of the pipeline to another is essential. It is important to be able to see the changes that are
on the horizon both upstream and downstream. Information sharing provides a powerful
platform on which to build collaborative working relationships across the supply chain.
A second issue is access to capacity. An important facilitator of flexible supply chain
management is the ability to access additional capacity when required. Capacity here refers
not only to manufacturing capacity but also in transport and warehousing. Furthermore,
that capacity may not be owned by the firm in question, it could come from partners across
the network, third party providers or even competitors.
Third, a more general enabler is access to knowledge and talent (Sweeney, 2013). Given
the rapid rate of change in both markets and technologies, a major challenge to
organisations today is ensuring that they have access to knowledge in terms of the potential
for product and process innovation. Equally critical is access to people who are capable of
exploiting that knowledge. “Open innovation” and technology sharing agreements are ideas
that are rapidly gaining ground. Once again, companies are increasingly turning to external
sources of knowledge and talent to provide adaptive capabilities.
Inter-operability of processes and information systems, we argue, would also be very
helpful. In an ideal world organisations would be able to alter the architecture of their
physical supply chains in short time frames with minimal cost or disruption involved.
Equally, those same companies require the ability to manage multiple supply chains serving
specific market segments. To enable this re-configuration it greatly helps if the nodes and
links of the supply chain are “inter-operable”. In other words they can be plugged together in
a variety of ways to enable specific supply chain solutions to be easily constructed.
Standard processes and information systems help greatly in creating inter-operability.
Network orchestration: because the achievement of higher levels of adaptability
generally requires inputs from a variety of other entities in the wider supply/demand
network, the need for co-ordination across the network arises. As supply chains become
more “virtual” than “vertical” there is a growing requirement for orchestration. Whether
that orchestration task is performed by the firm itself or by a specialist external logistics
service provider or “4PL”, the ability to structure appropriate networks and to synchronise
activities across the nodes and links of those networks is paramount.
Conceptually, there is little doubt that visibility and information sharing, access to capacity,
access to knowledge and talent, inter-operability and network orchestration combined will
increase structural flexibility in the supply chain, and will enable firms to effectively respond to
shifting centres of gravity. However, as we suggested earlier, the provision of flexibility does
come at a cost. Specifically, investing in flexibility incurs cost today against an uncertain event
in the future, and hence this additional cost needs to be balanced against the benefits of greater
structural flexibility. In the following section we present a conceptual framework indicating
how supply chain structures can evolve in the light of increased volatility.
Coping with volatility: a framework
Given that volatility is likely to be a pervasive backdrop to the supply chain landscape for
some time to come, what are the basic principles that underpin good practice in these
conditions? Much can be learned from the extensive research that has been conducted in the
area of supply chain risk and resilience (see Chopra and Sodhi, 2004; Christopher and
Lee, 2004; Sheffi, 2005; Kleindorfer and Saad, 2005; Chang et al., 2015). It is recognised that,
partly as a result of turbulence and volatility, supply chains are more vulnerable to
disruption and unexpected shocks than has been the case in the past. As a result it is often
proposed that supply chains should be designed with greater levels of redundancy and
slack to provide the “headroom” to cope with the unexpected (Sheffi and Rice, 2005). Usually
this redundancy comes in the form of additional inventory held as buffers at different stages
in the supply chain. Alternatively, or as well, resilience can be enhanced through greater
supply chain flexibility, i.e. by being able to change the network rapidly to respond to
changed conditions. Either way it should be recognised that resilience comes at a cost and
thus the question arises as to the cost/benefit of an investment in improved resilience.
From a purely financial point of view, carrying excess inventory or building additional
capacity that may rarely be required is not an attractive proposition. Yet much of the extant
literature on supply chain resilience implicitly suggests that more resilience is better.
However, the cost of achieving that resilience is not always recognised. The challenge is to
be able to put a figure on the costs of things going wrong. If this can be done it will provide
some indication of how much it might be worth investing to avoid that risk. One approach
that has been proposed is that advanced by Simchi-Levi et al. (2014), whereby the impact of
a disruption at each node in the supply chain cash flow is calculated. The time-to-recovery
following the disruption is estimated and the impact on total supply chain performance is
estimated. Decisions on investments in supply chain resilience should be influenced both by
the actual exposure to risk and the likelihood and impact of that risk. A key point to make is
that volatility does not automatically lead to supply chain risk and cost – the latter depends
entirely on the exposure to the risk caused by volatility.
The rising challenge for supply chain managers is to accurately assess the degree of
exposure to risk at every node and link of the supply/demand network. First, volatility leads
to supply chain risk, but it is the exposure to this risk that determines the cost that this
volatility incurs. When managing this risk, supply chain managers need to trade off
combined post hoc internal and external recovery cost, with a priori resilience cost.
Supply chain
2.0 revisited
13
IJPDLM
47,1
14
The combined recovery and resilience cost determine the actual cost that volatility incurs in
the supply chain. Once it has been determined whether there is exposure to the ensuing risks
of volatility, there are three types of cost that are relevant:
(1) The internal recovery cost of a likely supply chain failure, which includes the cost of
buffer inventory, potential obsolescence, “fire-fighting” costs, as well as the cost of
unbalanced capacity and labour costs, such as overtime.
(2) The external recovery cost of a supply chain failure, which includes lost sales, stockouts, incentives needed to clear excess inventory, as well as contractual penalties
and potential expedited shipments, such as emergency airfreight.
(3) The resilience cost, which includes all expenses made a priori to mitigate possible
effects of the supply chain failure. These include hedging and insurance, but also,
the increased transaction cost due to diversified manufacturing and sourcing
footprints, as well any redundancy in footprint.
Combined, these three cost elements determine the cost implications of volatility on the supply
chain: firms need to quantify each cost element, and determine the likelihood of their occurrence.
This enables them to define the trade-offs they deem appropriate between recovery
and resilience cost, or in other words, what level of investment in flexibility is seen as
appropriate. Figure 6 summarises the approach we propose, with volatility as the starting point.
Volatility in the business
environment poses risk of
supply chain failure
Supply chain structure
mitigates exposure to ensuing risk of:
1. Internal recovery cost
• Excess inventory
• Obsolescence cost
• Fire-fighting cost
• Unbalanced/idle capacity
• Overtime payments
2. External recovery cost
• Lost sales
• Stock-outs
• Sales incentives
• Contractual penalties
• Cost of expedited shipments
Trade-offs
3. Resilience cost
Figure 6.
A framework for
managing volatilityinduced risk in the
supply chain
• Cost of time, capacity and inventory buffers
• Hedging and insurance cost
• Cost of access to surge capacity, contract manufacturing and
shared services
• Increased transaction cost due to diversification and redundancy in
footprint
Supply chain
cost
As volatility hits the supply chain, for example in the form of shifting centres of gravity,
cost will invariably be incurred. The supply chain structure hereby acts as a mediator by
mitigating the exposure to the ensuing risk. If, for example, supply side factors change the
centre of gravity, a diversified manufacturing footprint can mitigate the adverse cost
implications of this shift.
Overall the supply chain structure should in fact co-evolve in line with the costs incurred
by volatility in the business environment: as in any complex adaptive system, the outcome
is co-produced by the interactions of the various subsystems that constitute the supply
chain. It is therefore not sensible to advocate a normative or prescriptive approach to
dealing with volatility; instead it should be seen as a journey in which the supply chain
structure adapts to mitigate the exposure to the volatility the firm faces, hopefully
minimising the recovery and resilience costs caused by volatility.
Outlook
Evidence is mounting that when the concept of supply chain management first emerged in
1982 (Oliver and Webber, 1982) the world was a different place (Bowersox et al., 2000;
Christopher and Holweg, 2011; Sweeney, 2013; Stevens and Johnson, 2016). The conclusion
that we draw from our updated analysis is that high levels of volatility in the wider business
environment are likely continue to impact our supply chains for the foreseeable future.
Whilst the original underlying principles of supply chain management still hold today, the
idea that networks can be “optimized” in terms of cost has to give way to a design
philosophy that is grounded on the premise that the best decisions in conditions of
uncertainty are those decisions that are flexible and resilient enough to respond to events as
they happen. We need to move our mental models away from a static view, towards one that
embraces the volatility that surrounds us in all facets of business. Whereas in the past the
key question for supply chain managers was: “does my supply chain operate at minimal
unit cost?”, the new question now has to be: “does my supply chain feature the appropriate
flexibility bandwidth, to minimise total cost in the light of volatility?” Transitioning from
previous mental models into this “brave new world” will take time, as cultures, procedures
and performance metrics will need to be changed radically. Building structural flexibility
will not be free, yet a conscious decision about what level of flexibility to provide will enable
firms to navigate the uncertain waters that are yet to come.
Notes
1. Specifically we consider: EUR/GBP (WMR&DS) exchange rate; USD/GBP (WMR&DS) exchange
rate; Crude Oil-Brent FOB, U$/BBL; Gold Bullion LBM U$/Troy Ounce; LME-Copper, Grade
A 3 Month £/MT; UK Clearing Banks Base Rate – middle rate; VIX from 1986; Baltic Dry index
from 1985; yearly median coefficients. Sources of data: Datastream; EIA (for crude oil data up to
08/2008), Chicago Board Options Exchange (for VIX data).
2. Several of these indices are correlated, as one might expect, nonetheless we have chosen these
indicators having taken the view that they are logically independent.
References
Alonso, E., Sherman, A.M., Wallington, T.J., Everson, M.P., Field, F.R., Roth, R. and Kirchain, R.E.
(2012), “Evaluating rare earth element availability: a case with revolutionary demand from clean
technologies”, Environmental Science and Technology, Vol. 46 No. 6, pp. 3406-3414.
Bloomberg (2013), “China sets first rare earth output quota for 2013 at 46,900 Tons”, Bloomberg News,
7 January.
Bollinger, J. (2002), Bollinger on Bollinger Bands, McGraw Hill, New York, NY.
Supply chain
2.0 revisited
15
IJPDLM
47,1
16
Bowersox, D.J., Closs, D.J. and Stank, T.P. (2000), “Ten mega-trends that will revolutionize supply chain
logistics”, Journal of Business Logistics, Vol. 21 No. 2, pp. 1-16.
Bradshaw, A.M., Reuter, B. and Hamacher, T. (2013), “The potential scarcity of rare elements for the
energiewende”, Green, Vol. 3 No. 2, pp. 93-111.
Chakhmouradian, A.R. and Wall, F. (2012), “Rare earth elements; minerals, mines, magnets (and more)”,
Elements, Vol. 8 No. 5, pp. 333-340.
Chang, W., Ellinger, A.E. and Blackhurst, J. (2015), “A contextual approach to supply chain risk
mitigation”, International Journal of Logistics Management, Vol. 26 No. 3, pp. 642-656.
Chopra, S. and Sodhi, M.S. (2004), “Managing risk to avoid supply-chain breakdown”, MIT Sloan
Management Review, Vol. 46 No. 1, pp. 53-61.
Christopher, M. and Holweg, M. (2011), “ ‘Supply chain 2.0’: managing supply chains in the era of
turbulence”, International Journal of Physical Distribution & Logistics Management, Vol. 41 No. 1,
pp. 63-82.
Christopher, M. and Lee, H. (2004), “Mitigating supply chain risk through improved confidence”,
International Journal of Physical Distribution & Logistics Management, Vol. 34 No. 5, pp. 388-396.
Christopher, M. and Peck, H. (2004), “Building the resilient supply chain”, International Journal of
Logistics Management, Vol. 15 No. 2, pp. 1-14.
(The) Economist (2010), “The difference engine: more precious than gold”, (The) Economist,
17 September.
Hatch, G.P. (2012), “Dynamics in the global market for rare earths”, Elements, Vol. 8 No. 5, pp. 341-346.
Haxel, G.B., Hedrick, J.B. and Orris, G.J. (2002), “Rare earth elements – critical resources for high
technology”, US Geological survey Fact Sheet 087-02.
Hendricks, K.B. and Singhal, V.R. (2003), “The effect of supply chain glitches on shareholder wealth”,
Journal of Operations Management, Vol. 21 No. 5, pp. 501-522.
Hendricks, K.B., Singhal, V.R. and Zhang, R. (2009), “The effect of operational slack, diversification, and
vertical relatedness on the stock market reaction to supply chain disruptions”, Journal of
Operations Management, Vol. 27 No. 3, pp. 233-246.
Humphries, M. (2012), “Rare earth elements: the global supply chain”, Congressional Research Service,
7-5700 (R41347), Washington, DC, 8 June.
Huo, B., Han, Z., Chen, H. and Zhao, X. (2015), “The effect of high-involvement human resource
management practices on supply chain integration”, International Journal of Physical
Distribution & Logistics Management, Vol. 45 No. 8, pp. 716-746.
Jackson, W.D. and Christiansen, G. (1993), “International strategic minerals inventory: summary reportrare-earth oxides”, US Geological Survey Circular N-930, Washington, DC.
Kleindorfer, P.R. and Saad, G.H. (2005), “Managing disruption risks in supply chains”, Production and
Operations Management, Vol. 14 No. 1, pp. 53-68.
Massari, S. and Ruberti, M. (2013), “Rare earth elements as critical raw materials: focus on international
markets and future strategies”, Resources Policy, Vol. 38 No. 1, pp. 36-43.
Oliver, R.K. and Webber, M.D. (1982), Supply‐Chain Management: Logistics Catches Up With Strategy,
Outlook. Booz, Allen and Hamilton, New York, NY.
Pettit, T.J., Fiksel, J. and Croxton, K.L. (2010), “Ensuring supply chain resilience: development of a
conceptual framework”, Journal of Business Logistics, Vol. 31 No. 1, pp. 1-21.
Ponomarov, S.Y. and Holcomb, M.C. (2009), “Understanding the concept of supply chain resilience”,
International Journal of Logistics Management, Vol. 20 No. 1, pp. 124-143.
Prahalad, C.K. and Hamel, G. (1990), “The core competence of the corporation”, Harvard Business
Review, Vol. 68 No. 3, pp. 79-91.
Sheffi, Y. (2005), “Building a resilient supply chain”, Harvard Business Review, Vol. 83 No. 10, pp. 1-4.
Sheffi, Y. and Rice, J.B. Jr (2005), “A supply chain view of the resilient enterprise”, MIT Sloan
Management Review, Vol. 47 No. 1, pp. 41-48.
Simchi-Levi, D., Schmidt, W. and Wei, Y. (2014), “From superstorms to factory fires”, Harvard Business
Review, Vol. 92 Nos 1/2, pp. 96-101.
Spekman, R. and Davis, E.W. (2016), “The extended enterprise: a decade later”, International Journal of
Physical Distribution & Logistics Management, Vol. 46 No. 1, pp. 43-61.
Stevens, G.C. and Johnson, M. (2016), “Integrating the supply chain … 25 years on”, International
Journal of Physical Distribution & Logistics Management, Vol. 46 No. 1, pp. 19-42.
Sweeney, E. (2013), “Supply chain ‘mega-trends’: current status and future trends”, Journal of the
Chartered Institute of Logistics and Transport in Ireland, Spring 2013, pp. 31-34.
Tang, C.S. (2006), “Robust strategies for mitigating supply chain disruptions”, International Journal of
Logistics: Research and Applications, Vol. 9 No. 1, pp. 33-45.
Corresponding author
Martin Christopher can be contacted at: m.g.christopher@cranfield.ac.uk
For instructions on how to order reprints of this article, please visit our website:
www.emeraldgrouppublishing.com/licensing/reprints.htm
Or contact us for further details: permissions@emeraldinsight.com
Supply chain
2.0 revisited
17

Purchase answer to see full
attachment

  
error: Content is protected !!