260 • Supply Chain Risk Management: An Emerging Discipline
EMERGING SUPPLY CHAIN RISK METRICS
We are witnessing a combination of existing measures as well as the devel-
opment of entirely new measures being applied to the risk management
arena. e following describes some of these risk- related measures.
Value at Risk
A metric that will increasingly have corporate- level visibility as a risk met-
ric is value at risk (VaR), a metric that is used extensively by the nancial
community to evaluate nancial investments. From a nancial perspec-
tive, VaR represents the largest loss likely to be suered on a portfolio
position over a holding period (usually 1–10days) with a given prob-
ability (condence level). VaR is a measure of market risk and includes
three components—a time period, a condence level or percentage, and a
loss amount or percentage attached to a risk.
7
is concept is now being
applied to supply chain scenarios. Chapter5 provided a detailed illustra-
tion of this metric and its use in supply chain risk management.
Time- to- Recovery
e time- to- recovery (T- t-R) from adverse events, a risk resiliency mea-
sure used extensively in the information technology arena, is generating
signicant interest as an evolving supply chain risk measure. An example
of a T- t-R measure involves the time to recover from a natural disaster,
such as a ood. Virtually any supply chain disruption can have a T- t-R
measure attached to it.
T- t-R measures, like most measures, have an objective attached to them.
Some sources refer to this objective as the recovery time objective (RTO),
which is the time in which a system, a facility, or equipment must be
restored aer a disruption to avoid an unacceptable break in business con-
tinuity and the incurrence of signicant losses. T- t-R measures how long
it takes an entity in a supply chain to reach full volume or full operating
status aer a major disruption. is does not have to mean full recovery
at a specic facility. If the full volume for a part from a supplier can be
provided within two weeks by working overtime at another facility, then
two weeks is the T- t-R, even if the aected facility takes longer to recover.
Risk Measurement • 261
Risk Exposure Index
An extension of the T- t-R index is the Risk Exposure Index, developed by
David Simchi- Levi at MIT. is index attempts to provide a better way for
companies to quantify their supply chain risks compared with the tradi-
tional 2 × 2 matrix that places the likelihood of an event occurring (high
or low) against its nancial impact (low or high). With this traditional
approach, potential risk events are plotted into one of four quadrants with
those with the highest likelihood of occurring and the largest impact
receiving priority for attention. While this approach serves a worthwhile
purpose, it is does not represent cutting- edge risk measurement.
e Risk Exposure Index assigns a cost that would occur from a poten-
tial disruption across each level and node in a given supply chain, based
on the T- t-R for each level/ node and the resulting nancial impact (FI),
including market share losses. ose individual risk components are then
totaled to produce a full FI for the entire supply chain.
8
is methodology also addresses unpredictable risks, including natu-
ral disasters and res at critical suppliers. While these types of risks are
nearly impossible to predict, the chances that such a disruption will occur
over a period of time is quite likely, which is built into the model. e key
to this analysis is the calculation of the nancial damage that such a dis-
ruption would likely cause.
Supply Chain Key Performance Indicators
We expect to see various key performance indicators (KPIs) emerge that
are viewed by executive management as supporting a company’s risk man-
agement eorts. e following presents six measures, some of which have
been in existence for a while but not necessarily visible at higher executive
levels. ese are not predictive measures—they do not provide warning
about pending or specic risk events. Rather, they provide insight into
how well some important business processes are operating. We will prob-
ably all agree that when important processes are operating well, a business
faces lower risk exposure.
Forecast Accuracy. As mentioned in Chapter12, best- practice com-
panies track and assign clear accountability for forecasting success to
an executive or executive steering committee. Best- practice companies
also regularly measure forecast accuracy across their dierent products.
262 • Supply Chain Risk Management: An Emerging Discipline
One survey revealed that fully two- thirds of respondents reported fore-
cast accuracy between 50% and 80%, a range that indicates room for
improvement. Forecast accuracy should be computed regularly and com-
pared against preestablished benchmarks. Many techniques are available
for assessing forecast accuracy, including mean forecast error, bias, mean
absolute deviation, mean absolute percent error and tracking signals. All
of these techniques compare, in some manner, actual demand against
forecasted demand with the dierence between these two gures consid-
ered error.
Concept- to- Customer Cycle Time. Product development leaders rely
on an important time- based metric called concept- to- customer (C- to- C)
cycle time. is metric reects the importance of being aware of the time
it takes to develop new products as well as acting as a target that no sin-
gle functional group can unilaterally attain. Surprisingly, in our experi-
ence most companies do not measure an overall cycle time, making the
development of a C- to- C cycle time measure especially attractive. Holding
functional groups mutually accountable for this measure sends a powerful
message about the importance of collaborating during product develop-
ment eorts. And as Chapter4 pointed out, the linkage between strategic
risk exposure and new product development success (or failure) is strong.
Inventory Accuracy. Recall from Chapter12 that measuring inventory
accuracy is essential for managing various kinds of supply chain risk.
Inventory accuracy exists when the physical inventory on hand for an
item equals the computerized or electronic record on hand (POH = ROH),
regardless of the quantity of inventory. Supply chain managers should
become almost evangelical in their quest for perfect record integrity,
including the integrity of records and data at suppliers and distributors.
Order- to- Cash Cycle Time. An important part of any supply chain is
the customer order fulllment process. e order- to- cash cycle involves
the steps from acquisition of a customer’s order to receiving payment from
a customer. When viewed narrowly, order fulllment focuses mainly on
the acts of distribution and logistics. When viewed broadly, order fulll-
ment includes all the steps and activities from the sales inquiry to delivery,
and perhaps even the return of the nal product or service. is involves
order preparation, transmission, entry, order lling (which may include
production and purchasing), billing, shipping, tracking, and returns.
Companies that take a broader view of order fulllment extend their
perspective to include the management of accounts receivable, making
Risk Measurement • 263
order- to- cash cycle time a key performance indicator. is stresses the
nancial aspects of the fulllment process by not viewing the process as
complete until customer payment is received. Ineective management of
the order- to- cash cycle time has clear nancial risk implications.
Perfect Order Rate. e perfect order metric is a mathematic composite
of multiple factors. e perfect order is one that is delivered on time, com-
plete (all ordered items are in the shipment), damage free, accurate (correct
items and quantities), with proper documentation. Perfect orders not only
drive customer loyalty for the product and producer, but they also lead to
greater supply chain eciency and reduced investments in inventory.
9
To date the primary users of the perfect order metric have been con-
sumer packaged goods companies. Virtually any company, however, can
use a perfect order measure if they can overcome the humbling nature of
this metric. As variables with a less- than- perfect value are combined, the
resulting metric becomes lower and lower. is is clear from Figure13.2. To
some, it simply looks better to report each line item separately and forego
the cumulative measure. Let’s not mess up some good numbers with facts.
Return on Assets. If there is one higher- level measure that tells how
well a supply chain is performing, return on assets is that measure. While
multiple versions of this metric exist, they all include a numerator that
includes income and a denominator that looks at assets. Regardless of the
specic formula used, one thing we know for certain is that any supply
chain problems, including the consequences of any risk events, will show
up in the numerator and/ or denominator of this metric. Chapter3 illus-
trated how a company uses return on assets as its primary way to measure
the performance of its business units.
• “Perfect orders”:
• Orders delivered on time
• Orders delivered complete
• Orders delivered damage free
• Orders lled accurately
• Orders billed accurately
97%
98%
96%
97%
99%
A perfect order is an order that is delivered complete, on time, in perfect
condition, and with accurate and complete documentation.
Perfect Order Rate = 0.97 × 0.98 × 0.99 × 0.96 × 0.97 = 87.6%
FIGURE 13.2
Perfect order measure.
264 • Supply Chain Risk Management: An Emerging Discipline
CONCLUDING THOUGHTS
Supply chain managers must take an unbiased view of their performance
measurement systems. e objective here should be to take poor measure-
ment systems and make them better while transforming good systems
into excellent ones. One course of action is to assemble internally a team to
compare the current state of supply chain risk measurement areas against
an ideal future state. Any gaps that exist between the current and future
state require a clear plan to get to a preferred state.
While measuring various dimensions of supply chain risk is a worthy
pursuit, the reality is that risk measurement is simply an activity. Activity
means nothing unless it leads to lower supply chain risk compared with
what would likely occur without the measurement system in place.
Measurement activity must lead to accomplishment.
Summary of Key Points
• A central question when thinking about supply chain risk measure-
ment is whether a measure, model, or index is valid and reliable.
• Most rms have some sort of supplier performance measurement
system in place. Fortunately, a set of best- practice guidelines exists
for assessing whether a supplier performance measurement system is
likely to satisfy its intended use.
• One type of measure scores risk events or suppliers using derived
algorithms that model risk. Risk indexes are quantitative models that
consider multiple factors to arrive at a single risk indicator or score.
• A body of research is emerging that counters the notion that complex
algorithms and models are automatically more eective than simple
rules of thumb or guidelines when making organizational decisions.
• At the ERM level, country risk index ratings are particularly valu-
able when thinking about making foreign direct investments. At the
SCRM level, this information can inuence logistics, sourcing, and
selling behavior.
• Total cost of ownership is a topic companies cannot ignore as they
search for new and better ways to manage supply chain risk. We
expect to see at least three major supply chain cost models in use:
total landed cost models, supplier performance cost models, and life-
cycle cost models.
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