Risk Measurement • 255
Assume a supplier delivers $1 million worth of parts to a company in the
third quarter of a year. e supplier also commits three infractions that quar-
ter—a late delivery, missing documentation, and a parts shortage. Further
assume the buyer assigns $30,000 in total nonconformance charges to these
infractions. e supplier’s SPI for the third quarter is 1.03, or (($1,000,000 +
$30,000)/$1,000,000). e SPI of 1.03 means the total cost of doing business
with this supplier is 3% higher than the unit price. If the unit price of a sup-
plier’s good is $127, then the estimated total cost of that item is really $130.81
($127 × 1.03). Because the SPI is a standardized metric, it allows compari-
sons between suppliers. A supplier with a higher SPI has a higher total cost
than one with a lower SPI. It is important to compare suppliers within the
same commodity to ensure “apples to apples” comparisons.
SPI Drawbacks. Although the SPI can be an eective tool, it is by no
means perfect. In fact, it has some potential drawbacks that users must
understand. First, because it is an index, the actual unit cost of an item
from a supplier is not considered directly in the SPI calculation—only the
value of the total shipments and infractions are considered. A higher sup-
plier unit cost inates the value of the shipments compared with a supplier
that has a lower unit cost, making any infractions look smaller given the
shipment value. Mathematically, this makes the SPI value lower, all else
equal, for the higher- price supplier. Let’s illustrate this with two suppliers
that ship the same number of units with the same infraction charges but
with dierent unit costs:
Supplier A Supplier B
50,000 units @ $9.00 per unit 50,000 units @ $10.50 per unit
$27,500 non conformance charges $27,500 nonconformance charges
SPI = ((50,000 × $9) + $27,500)/$450,000 SPI = ((50,000 × $10.50) + $27,500)/$525,000
= 1.06 = 1.05
e dierence here is a mathematical artifact of the dierent unit costs.
Ideally, the buying company would employ a total landed cost model dur-
ing supplier selection so any issues regarding dierences in unit costs
would have already been considered.
e SPI calculation also has a built- in bias against small volume sup-
pliers. Assume three suppliers within a commodity group commit
the same infraction that resulted in a $3,000 nonconformance charge.
e rst supplier provided $15,000 worth of goods during a quarter, the
256 • Supply Chain Risk Management: An Emerging Discipline
second supplier provided $10,000 worth of goods, and a third sup-
plier provided $30,000 worth of goods. e SPI for the rst supplier is
1.20 (($15,000 + $3000)/$15,000), the SPI for the second supplier is 1.30
(($10,000 + $3,000)/$10,000), and the SPI for the third supplier is 1.10
(($30,000 + $3,000)/$30,000). Even though each supplier committed the
same infraction, the smaller supplier appears worse from an SPI perspec-
tive, particularly compared with the larger supplier.
is bias requires the calculation of a Q adjustment factor, which is
essentially a weight applied to the nonconformance costs. e adjustment
factor allows valid SPI comparisons by removing the bias against suppliers
with a lower total value of deliveries. It makes sense to calculate an adjust-
ment factor that removes this bias if suppliers within a commodity provide
widely diering volumes. If we want to make total cost models as accurate
as possible, then we have to think about the Q adjustment factor. Figure13.1
illustrates the step- by- step calculation of the Q adjustment factor.
A nal drawback to the SPI approach is that it requires a great deal of
discipline and cross- functional support to stay on top of the required data
collection. Supplier infractions can occur or be discovered at dierent
points along a supply chain. Inconsistency in collecting and allocating
supplier charges will quickly undermine the validity of this model.
Supplier ASupplier BSupplier C
1
st
quarter deliveries 10 12 10
Total value of
deliveries
$10,500 $18,000 $35,000
Average delivery
value
($10,500/10) = $1,050 ($18,000/12) = $1,500 ($35,000/10) = $3,500
Non-conformance
charges assigned to
each supplier
$1,000 $1,400 $2,500
1
st
quarter SPI
($10,500+$1,000)/$10,500
= 1.10
($18,000+$1,400)/$18,000
= 1.08
($35,000+$2,500)/$35,000
1.07
Average shipment
from all suppliers*
$2,500 $2,500 $2,500
Q adjustment factor $1,050/$2,500 = .42 $1,500/$2,500 = .60 $3,500/$2,500 = 1.4
Adjusted SPI 1.04 1.05 1.10
Adjusted SPI for Supplier A = $10,500 + ($1,000 × .42)/$10,500 = 1.04
Adjusted SPI for Supplier B = $18,000 + ($1,400 × .60)/$18,000 = 1.05
Adjusted SPI for Supplier C = $35,000 + ($2,500 × 1.4)/$35,000 = 1.10
* Average shipment from all suppliers equals (total value of shipments/total shipments) for all suppliers
within a commodity, not just those listed in this table. is is a provided piece of data in this example.
FIGURE 13.1
Supplier performance index with Q adjustment.
Risk Measurement • 257
Life- Cycle Cost Models. Life- cycle cost models may be what comes most
to mind when thinking about total costs analysis. is type of model is
most oen used when evaluating capital decisions that cover an extended
time period, such as equipment and facilities. Life- cycle models are very
similar to net present value models used in nance. Life- cycle cost models
are used when evaluating capital decisions, such as plant and equipment,
rather than the purchase of everyday components and services. e other
cost models described here are more applicable for repetitively purchased
goods or services. Life- cycle costs apply whether equipment is sourced
domestically or internationally.
Developers of life- cycle cost models oen allocate their cost elements
across four broad categories that reect usage over time. e life- cycle is
essentially one of buying, shipping, installing, using, maintaining, and
disposing:
• Unit Price—Includes the price paid along with purchase terms
• Acquisition Costs—Includes all costs associated with delivering equip-
ment, such as buying, ordering, and freight charges to the customer
• Usage Costs—Includes all the costs to operate the equipment, includ-
ing installation, energy consumption, maintenance, reliability, spare
parts, and yield and eciency during production
• End- of- Life Costs—Includes all costs incurred when removing
equipment from service, less any proceeds received for resale, scrap,
or salvage
Companies should compare the assumptions made during the devel-
opment of life- cycle estimates with actual data as they become available,
particularly since life- cycle models oen look years into the future. is
will provide insights regarding how to improve the life- cycle models.
A popular misconception is that having a total cost model inherently
provides better information than not having a total cost model. We like
to think this is true, but the reality is that total cost models, like fore-
casting models, almost always have some degree of inaccuracy. is is
especially true if a model is populated with data that are based largely on
estimates or averages rather than actual data. Or, the model may fail to
take into account some important cost elements. Do not underestimate
the value, however, of a well- specied total cost model when managing
supply chain risk. Total cost measurement is one of the best risk manage-
ment approaches that we can put in place today.
258 • Supply Chain Risk Management: An Emerging Discipline
SUPPLIER CAPACITY ESTIMATE MEASURES
Most of us have heard stories about a buyer placing an order with a sup-
plier only to nd that the supplier’s assurances about available capacity
simply are not true. Or, how about when total demand in a marketplace
increases and a buying company nds it has been placed on the short end
of a supplier’s allocation schedule. Either of these conditions creates oper-
ational risk as supply is not readily available to satisfy demand.
A risk management approach that will provide some clues into available
capacity is something we call rough cut supplier capacity analysis. It is called
rough cut because it is not meant to be a precise estimate of available supplier
capacity. We are simply trying to get a feel regarding what might be avail-
able and comparing that to a specic requirement. And when we get a better
feel for the amount of available capacity, we can engage in some worthwhile
discussions with suppliers. Let’s illustrate how this technique works.
Table13.2 presents data for the three suppliers that appeared in Chapter6
when calculating Z– Scores. We are now introducing two new additional
pieces of data—the estimate of the average capacity utilization rate at each
supplier and a quoted price per unit for the item of interest. is table
presents a methodology for estimating the capacity available at each sup-
plier. is analysis reveals that only one supplier appears to have adequate
capacity available. According to these numbers the buying company could
face major operational risk if it decided on a single- source contract with
FASE Chemicals. e risk could be so severe that it aects the success of
a product, which then leads to strategic risk. And placing a single- source
contract with DMS will require some serious discussions to verify these
estimates and, if veried, to work out a plan to meet demand requirements.
e point of this exercise is to provide a broad understanding of where a
supplier stands in terms of capacity. One risk here is that a supplier could
also be speaking to other buyers that are interested in the available capac-
ity. It is also possible some contracts at the supplier are expiring, which
could make additional capacity available. One thing we are condent
about is these suppliers are not likely to drop existing customers simply
to fulll a new contract, at least in the short term, making the capacity
discussion a key part of the risk assessment process.
Risk Measurement • 259
TABLE13.2
Rough Cut Supplier Capacity Analysis
A pharmaceutical company has forecasted that it requires 20 million pounds of a
chemical compound to support the launch of a new product next year. e following
data are collected to help in the estimate.
Ninaka
Materials
FASE
Chemicals DMS NV
Quoted price per pound $4.75 $5.75 $5.20
Current installed capacity utilization 98% 95% 94%
Sales 2014 generated from the chemical
compound
$6,500,000,000 $550,000,000 $1,355,000,000
Rough cut estimate of available capacity 27,926,960 lbs. 5,034,325 lbs.
16,632,570
Current installed capacity utilization indicates that portion of the supplier’s production
capacity that is currently utilized for the production of chemicals. For example, if
current installed capacity is 98%, then this supplier is utilizing 98% of its production
capacity and therefore has 2% of its capacity available for new business. is does not
indicate how many available pounds this represents.
Ninaka
$6,500,000,000 sales generated from the compound/98 capacity points used to generate
the sales = $66,326,530 in sales generated by each point of used capacity × 2 capacity
points available = $132,653,060 potential capacity available in dollars/$4.75 quoted
price per pound =
27,926,960 estimated pounds available
FASE
$550,000,000 sales generated from the compound/95 capacity points used to generate
the sales = $5,789,474 in sales generated by each point of used capacity × 5 capacity
points available = $28,947,368 potential capacity available in dollars/$5.75 quoted price
per pound =
5,034,325 estimated pounds available
DMS
$1,355,000,000 sales generated from the compound/94 capacity points used to generate
the sales = $14,414,894 in sales generated by each point of used capacity × 6 capacity
points available = $86,489,362 potential capacity available in dollars/$5.20 quoted price
per pound =
16,632,570 estimated pounds available
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