S&OP in Service

J o u r n a l o f
Business FOrecasting
SOP in the
Service Industry
By Patrick Bower
2 0 1 5 | s u m m e r V o l u m e 3 4 | I s s u e 2
292619 Supply-Neutral versus
Unconstrained Demand
By Larry Lapide
SOP:
OrganicValley’s Journey
By Beth Wells
Cleanse Your Historical
Shipment Data? Why?
By Charles W. Chase, Jr.
4
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2nd Edition–2015
Michael Wachtel
Vice President
of Supply Chain
l’oreal
“Whether you are just getting
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looking to take your Demand
Planning to the next level,
make sure to pick up this book
to ensure your organization is
heading in the right direction.”
Jay Nearnberg
Director, Global Demand
SOP Excellence
novarTis
consuMer healTh
“I would recommend the book
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titioner as a practical way of
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ledge in this rapidly changing
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“ Leaders will find it perfect to
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book will be a must read for
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Chaman L. Jain
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Evangelos O. Simos
Editor, International Economic Affairs
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Manuscripts Invited
Submit manuscript to:
Dr. Chaman L. Jain
Tobin College of Business
St. John’s University, Jamaica, NY 11439
jainc@stjohns.edu
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© Copyright 2015
by Journal of Business Forecasting
ISSN 1930-126X
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Mark J. Lawless
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Paul Sheldon Foote
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Fullerton, CA
3 Answers to Your Demand Planning
and Forecasting Questions
4 SOP in the Service Industry
By Patrick Bower
19 Supply-Neutral versus Unconstrained Demand
By Larry Lapide
26 SOP: Organic Valley’s Journey
By Beth Wells
29 Cleanse Your Historical Shipment Data? Why?
By Charles W. Chase, Jr.
34 Will High Risk Events Trigger a Recession?
By Evangelos Otto Simos
40 The U.S. Economy to Bounce Back in
Second Quarter
By Nur Onvural
48 IBF Calendar 2015
J o u r n a l o f
Business ForecastinGV o l u m e 3 4 I s s u e 2 | s u m m e r 2 0 1 5
2 Copyright © 2015 Journal of Business Forecasting | All Rights Reserved | Summer 2015

[ Q ] How do companies arrive at a“forecast that matters?”
[ A ] A forecast that matters is the one that gives an error,
which is tolerable. How much error can be tolerated depends
on the company’s ability to adjust to the error and the cost
of error. If the lead time is too long, the company cannot
adjust quickly to the error, particularly, in the case of under-
forecasting. How much the error will cost also matters. The best
thing, therefore, is to generate ex post forecasts for a number
of periods to determine how much error can be expected. Try
to find ways to improve it further if necessary. If not, it has to
be compensated with additional inventory, or customer service
has to be compromised.
[ Q ] I am working on a portfolio review of a consumer
products company for the Executive SOP meeting. I want to
know how to go about deciding whether to keep a product or
eliminate it?
[ A ] The decision should depend on how much a product is
costing and how much revenue and profit it is generating. If it is
not providing enough profit, you may decide to discontinue it.
Cost comes in terms of holding inventory as well as in producing
it. The production cost usually goes up when products are
produced in smaller quantity. Furthermore, at times, we may
have to go beyond the profit generated by a product. Instead,
go by the profit generated by other products as a result of it.
I have seen cases where a customer places larger orders for a
product year in and year out simply because he/she cannot
get it anywhere else. If you discontinue it, you may lose that
customer too.
[ Q ] We are in the fashion industry. Although we operate on
a make-to-order model, we still wind up with huge inventory.
Can you offer a solution?
[ A ] There are three things to do, or are worth looking into:
1. Seeifthereisanopportunityforproductrationalization.
SKUs that yield little or no profit are good candidates
for elimination. Very often elimination of some SKUs
does not impact much the total revenue or profit.
2. Review point-of-sales data weekly. This will help in
determining which SKUs are moving and which ones
Answers toYour Demand Planning
and Forecasting Questions
are not, thereby helping to align better inventory with
demand.
3. Buy less raw material, buy more frequently. Doing so
will increase the cost, but it will pay in the long run.
[ Q ] Where in the organization would you recommend
statistical forecast be prepared and why—at the headquarters
by the central Global Demand team or locally by the market
teams?
[ A ] Forecasts should be prepared locally (by each country
or region), and then consolidated by the headquarters. They
should be prepared locally because they know their data and
market better than anyone else. They should be consolidated,
refined, and adjusted by the Global Demand team at the
headquarters, because they would be impartial. They may
detect a bias or issue ignored/overlooked by the local team.
[ Q ] In calculating forecast error, should we divide error by
actual or forecast?
[ A ] We should divide error by the actual simply because we
want to know how forecast deviated from the actual, not how
actual deviated from the forecast.
[ Q ] Do small companies need an SOP process to drive
financial planning/budgeting?
A. The ultimate goal of the SOP process is to drive financial
planning/budgeting, which is needed both for large and
small companies. So, the process is not limited to the size of a
company. It will benefit every company.
[ Q ] Is there any special metric for measuring forecast error of
slow moving products?
[ A ] I am not aware of any special metric for measuring error
of slow moving products. The only thing is here we should
compute error over a longer period of time.
Happy Forecasting!
Chaman L. Jain, Editor
St. John’s University | Jainc@Stjohns.edu
Copyright © 2015 Journal of Business Forecasting | +1.516.504.7576 | www.ibf.org | All Rights Reserved | Summer 2015 3

SOP in the Service
Industry
By Patrick Bower
E x ecu t i v e S ummar y | Countless manufacturing companies have tackled the challenge of implementing SOP.
Those that have nurtured the process to maturity reap considerable benefit streams. However, service sector companies—
with no products to build, no inventory to ship, and no shelves to stock—have missed out on similar advantages simply
because there’s no unified process model for mirroring the integrated planning strategies of the manufacturing world into
the realm of service.
4 Copyright © 2015 Journal of Business Forecasting | +1.516.504.7576 | www.ibf.org | All Rights Reserved | Summer 2015

O
ne of the more interesting
informal discussion topics at
business forecasting and supply
chain conferences centers around a
simplequestion:CouldtheSOPprocess
be leveraged in non-product service
industries such as (but not limited
to) municipal government, school
systems, universities, and correctional
institutions, or in non-public service
sector organizations such as consulting,
banking, and financial services? Over
time, these informal debates became
personally intriguing, leaving me
scratching my head and wondering—
could there be a better way to plan the
service sector, and could SOP be the
answer to an unasked question?
With my curiosity aroused, I set
about looking for SOP processes in
the service sector, focusing my personal
lens on examining planning processes
of all sorts and types. After more than
a decade of casual observation, I offer
with certainty that there are forecasting
and planning processes with varying
degrees of maturity and efficacy being
used in most service organizations. I
am also aware that only a handful of
these service-sector planning processes
are as sophisticated as a mature SOP
process, and none these were actually
called SOP. As I prepared for this article,
I deepened my search, and with the
exception of the occasional informal
debate, I found only scant treatment of
service-based SOP in literature such
as technical journals or white papers.
Service-based SOP it seems, is about as
elusive as the Loch Ness Monster.
SOP PROCESS
It may be useful to step back for a
momentanddefinetheSOPprocess.For
the uninitiated, SOP is a rigorous, multi-
step, cross functional, mid- to long-range
planning process model that is heavily
deployed in manufacturing companies.
TheSOPprocessusesaseriesofmonthly
review meetings to help facilitate
alignment and collaboration. The first
step in SOP, a demand review meeting,
is meant to build consensus around
demand. The unconstrained forecast
from the demand review passes to the
next step, during which supply review
meeting participants agree on a plan to
use productive capacity with the goal
of assuring that all future demand can
be met. Any shortfalls in revenue, profit,
or capacity that are based on the results
of the supply and demand balancing
process are discussed in a separate
meeting, during which issues are hashed
out and proposals are made to close
gaps. Once these steps are completed,
the results, issues, gaps, and metrics from
the operations are presented monthly to
senior management for their input and
direction. Figure 1 shows a simple model
of the process.
Patrick Bower | Mr. Bower is Senior Director, Global Supply Chain Planning Customer Service at Combe
Incorporated, producer of high-quality personal care products. A valued and frequent writer and speaker on supply chain
subjects, he is a recognized demand planning and SOP expert and a self-professed “SOP geek.” Prior to Combe, he
served as the Practice Manager of Supply Chain Planning at a boutique supply chain consulting firm, where his client
list included Diageo, Bayer, Unilever, Glaxo Smith Kline, Pfizer, Foster Farms, Farley’s and Sather, Cabot Industries, and
American Girl. His experience also includes roles at Cadbury, Kraft Foods, Unisys, and Snapple. He has also worked for the
supplychainsoftwarecompany,Numetrix,andwasVicePresidentofRDatAtrionInternational.Hewasrecognizedthree
times by Supply and Demand Chain Executive magazine as a “Pro to Know,” and Consumer Goods Technology magazine
considered him one of their 2014 Visionaries. He is the recipient of the inaugural IBF’s Excellence in Business Forecasting
and Planning Award.
Demand Review
(Meeting) Including
New Products
Supply Review
(Meeting)
Supply Demand
Balancing Exception
Management
Senior Management
Discussion
ISSUES, GAPS, METRICS, FINANCIAL IMPLICATIONS, STRATEGY
Figure 1 | Sales and Operations Planning Process (SOP)

BENEFITS OF SOP
The benefit streams arising from
mature and properly orchestrated
SOP processes are well-documented:
inventory is reduced, capacity
is better utilized, throughput is
increased, revenue streams become
more predictable, new products
are introduced seamlessly, and
companies see improved cash flow
and profitability. Business leaders who
use SOP often feel as if they have a
better handle on—or control over—
their operations. Considering the
known benefit stream of SOP and
what appears to be an unmet need
for a more sophisticated planning
model in service industries, coupled
with a whole heap of presumption
(on my part) that SOP represents
a better solution for the sector, this
article will compare manufacturing’s
version of SOP and some service-
industry planning examples to see if
there is an opportunity to leverage
the conceptual underpinnings of
SOP as a service integrated planning
modality.
WHY NOT SOP
IN THE SERVICE
INDUSTRY?
With considerable SOP imple
mentation experience, and after
considering all of the potential benefits
of the SOP process model, I have not
been able to grasp why an integrated
planning approach has not emerged
in the service sector. Without doubt,
there are many potential reasons
for this disinterest. Is it possible that
service-related industries might have
simply viewed SOP as a peculiar
manufacturing process that did not
apply to them? Maybe. Is it possible the
very name SOP could be a problem?
Since SOP is an acronym for sales
and operations planning—and if sales
are measured in terms of mortgage
applicants, prisoners, or students, and
there are no real operations to plan—
why would anyone even consider
SOP for the service industry? It
would be like a square peg seeking
out a round hole. Even the sometime
synonym for SOP—IBP (Integrated
Business Planning)—would not work
since many organizations in the service
sector are not businesses. The moniker
SOP could be a small part of the
problem, but surely someone would
be inventive enough to integrate the
underlying concepts of SOP with a
different name?
At some point in my “why not?”
deliberations, I became fixated on
awareness as a potential issue. Maybe
service planners had not heard of
SOP, and perhaps a simple lack of
awareness prevented its application
and proliferation in this sector.
Certainly, during the aforementioned
literature review and Internet searches,
I turned up little in the way of usable
content, postmortems, case studies,
or discussions on a service-based
version of SOP. Research and articles
for any process model offer road maps,
benefit streams, and how-to guides,
and without these it would be hard to
replicate SOP in the service sector.
This was all a bit perplexing for me—
surely somewhere, sometime, a service
industry planner or manager must
have heard of SOP. Heck, the service
industry is full of MBAs, each required
to take an Operations Research class
as part of their core curriculum. They
definitely would have been exposed to
SOP in that course. Why would they
not try to apply some of the concepts
in their organizations? And SOP-
related content has certainly been
disseminated in all kinds of business
literature. Why wouldn’t someone
put thought into adapting the model
to the service sector? After all of
this mental gnashing, I came away
unconvinced that awareness was the
real issue. If anything, SOP has been
as overexposed as the Kardashian
sisters. It just seemed as if there
was something bigger preventing
acceptance of the process in the
service sector.
Having discounted the obvious,
and armed with a dozen examples of
service-based planning approaches
drawn from the real world plus
some experience working in the
service sector myself, I distilled my
observations into three hypotheses
that were not easy to refute:
First, I believe the language of
traditional SOP is not relatable or
accessible to the service sector. As
a planning process, SOP does not
appear to have language generic
enough (as traditionally defined) to be
understood within the service sector.
Second, I suspect that the lack
of homogeneous types of supply
and demand in the service industry
is a significant factor in the lack of
industry acceptance and proliferation.
This lack of similarity—in demand and
supply characteristics, in planning
approaches, and in metrics between
service-sector cohorts—prevents the
process model from broader adoption.
Because of these differences, the
process model cannot be easily copied
between non-similar sub-sectors of
the service industry (correctional
facilities and banking as examples).
Third, without substantive
documentation, case studies, and
examples, and without a clearly
articulated benefit stream, adopting
an SOP process model represents
considerable risk—invoking the classic

risk-reward challenge. Presuming high
risk and uncertain rewards, I would not
expect to find many early adopters.
TOWARD A BETTER
UNDERSTANDING
Before delving into the real-
world examples of SOP that I’ve
encountered in the service sector,
it would help to review some of
the process language related to
SOP. An examination of terms like
supply, demand, supply and demand
balancing, strategic alignment, and
portfolio management—within the
context of both manufacturing and
service industries—might go a long
way toward making the concepts less
foreign.
There is no doubt the vernacular of
supply chain professionals can be off-
putting. Even some of the common
terminology that manufacturing uses
to define SOP does not exist in the
service industry. We supply-chainers
certainly like our gobbledygook
words and acronyms. Even simple
words like inventory can create a
disconnect. For example, what is
inventory to a mortgage company
or to a large consulting organization
like PricewaterhouseCoopers? Is it
the paper in the storeroom? What is
demand to a radiology department
or a prison? To SOP practitioners,
demand is a forecast of future sales of
some tangible thing—of soda, candy,
cars, etc. It is an item, a product, or a
SKU. And it is always expressed in terms
of both units and dollars. In the service
sector, the concept of demand is less
uniform, more abstract—it can be
students, prisoners, clients, accounts,
applicants, patients, or prospective
customers. And the methods used
to estimate demand in the service
sector are more likely to be qualitative
assessments of potential outcomes
(probabilityofcompletedsoftwaresale)
or dispositions (criminal sentences).
This is a significant departure from
the math-laden, product-based, time-
series-heavy approaches employed by
manufacturing.
In contrast with the concept
of demand, the notion of supply
is conceptually a bit closer when
comparing the manufacturing vs. service
sectors. To a manufacturing wonk,
supply represents the combination of
both production line capacity (internal
and external) as well as inventory. Within
the service industry, supply has a similar
constraint-based connotation but is less
empirical. In manufacturing, capacity is
more formula-driven: production line
no. 1 is physically capable of producing
1,000 widgets per hour. In the service
world, capacity might be measured
in terms of classroom availability or
qualified teachers, the number of
empty prison beds or qualified loan
processors, the amount of time available
to operate a specific x-ray machine
on a particular day of the week, or the
number of specially skilled consultants
available for assignment to a particular
task for a particular client in a particular
industry. Unlike producing widgets
on a manufacturing line, however, the
utilization of service resources is much
less consistent and far more dependent
on the characterization of specific
service demands. (Do you need to know
the number of empty beds needed to
house criminals at a supermax prison
or at a halfway house? Are your x-ray
patients at a trauma center or at an
outpatient clinic? Are you planning
appropriate staffing levels for students
in a special-needs classroom?) Managers
in both sectors strive to achieve optimal
resource utilization, but the method and
language of estimating and measuring
utilization can vary significantly between
the two realms.
Both sectors are very much alike in
leveraging metrics, yet supply chain
metrics are more uniform and persistent
across different industries within the
sector. Most manufacturing companies
use measures of forecast accuracy—
perfect order fill rates, production
attainment, and utilization—while the
metrics used in the service sector can
vary widely (the number of satisfied
customers, claims or applications
processed in a specific time period, hotel
occupancy rates, even the hold time of
potential customers on the phone).
The service sector is more capabilities
focused vs. the capacity orientation of
the manufacturing domain. So while
the notion of supply is similar in both
sectors, it’s not exactly on the mark.
BALANCING ACT
In the manufacturing world there
is sometimes a sense that supply and
demand are acting out a Mothra vs.
Godzilla death match. Manufacturing
blames stock outages on bad
forecasts, and demand planners blame
manufacturing for failure to make
enough of what was needed. SOP
was devised to end such disputes. In
mature SOP processes, internal silos
are bridged via a collaborative exercise
that involves sales and marketing,
finance, and the operational aspects of
the organization.
Supply and demand balancing is a
key concept in SOP. It encompasses
the hard work of comparing and
balancing anticipated supply and
demand over an 18- to 24-month
forward-looking horizon. If there
are mismatches that arise from
the analysis, they are discussed
collaboratively so that disputes may
be resolved before they happen. In a
simple scenario, a manufacturer would

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compare forecasts with production
line throughput rates to understand
future capacity requirements. For
example, “My forecast is a stable
500 widgets per month, and my
production capacity is capable of
consistently making 600 widgets
per month.” While this example of a
matching process is straightforward,
the important effort in the balancing
process is to project the demand and
supply requirements into the future,
to try to determine the point at
which demand will exceed supply (or
opportunistically find buyers for the
100 extra widgets of your available
supply).
Supply and demand balancing can
be much more complex, particularly
when different products compete for
or share production line time. When
I worked at Snapple, the forecasted
demand for 16-oz. glass bottles of
Snapple Lemon Tea was translated
into a product family called “16ozTea.”
This equated to a supply characteristic
of 0.025 minutes per unit on a 16-oz.
hot-fill production line, one of many
such 16-oz. hot-fill lines. Demand for
all Snapple 16-oz. tea flavor offerings,
including peach, lemon, mint, and half-
and-half, were aggregated into this
16ozTea product family. This product
family view enabled planners to easily
assess the entire capacity network and
to assure our ability to manufacture
product against the forecast over
time. Adding another level of
difficulty to the balancing process
was the inclusion of similar products
competing for the same production
line time. As you might expect, we
had multiple product families such as
16ozJuice and 20ozTea. The 16ozJuice
product family competed directly
with the 16ozTea family for available
production capacity, yet the product
ran slower, was much less seasonal,
and the product had shorter expiry
times, thereby preventing any pre-
building of inventory. Consequently,
the resulting supply and demand
balancing process was much more
intricate than one might expect, with
a lot of conflicting goals—maximize
production utilization, pre-build only
what was needed, manage expiry, use
the least amount of contracted supply,
all while meeting all demand in all time
periods. As I noted, it was challenging
work.
Working through the complexity
of the balancing process yielded a
number of visualization benefits,
including the ability to make decisions
on pre-building inventory in advance
of need (when the production would
not keep up with demand—usually
in the summer months) and when we
needed to add additional capacity
to the supply network via external
contractors. In addition, product
families were also leveraged to
summarize revenue streams. Each
unit of 16ozTea was valued at $0.23,
making it easy to calculate top-line
revenue estimates.
The tangible benefits of this
product family-based supply and
demand balancing act were realized
immediately.Productfamiliesprovided
both a common language and a point
of connection around which personnel
from supply, demand, and finance
could all rally. Forecasters, marketers,
salespeople, and operations personnel
were able to sit around a table and
communicate easily, as the product
family designation became something
of a pivot point for planning purposes.
The results: operations leveraged
working capital much more effectively
and inventory was reduced by
manufacturing products just ahead of
need without extensive pre-building
while also limiting the need for
contracted (expensive) manufacturing
resources.
This use of product families is
commonplace in manufacturing. In
fact, it is a core expectation in SOP;
and on observation, service industries
have a very similar (compared to
manufacturing companies) notion
of product families and supply
and demand balancing. These
organizations seek to balance their
available resources to meet the inflow
of demand for their services, but that
demand needs to be defined. Often,
managers in service industries seek to
control the demand inflow (or outflow)
by matching their estimates of demand
to the capabilities of their ability to
serve. Sometimes they’ll do this to
manage processing costs, other times
to manage the amount of workflow
through available resources, and
other times to orchestrate a desired
outcome. In all of my observations, the
end goal of such processes has always
been to seek balance. Service leaders
achieve this using a matching process
by which they align a service demand
characteristic to a service family;
this translates into service capacity.
It is manufacturing’s product family
concept revisited in the service sector.
It is:
Service Demand ➔
Service Demand Characteristic ➔
Service Family ➔ Service Capabilities
To be clear, in all of my observations
of SOP-like planning processes
deployed in service organizations, not
a single one referred to it as a service
family, and no one mapped out a data
translation flow like the one shown
here. This is merely an interpretation
of how they mimicked the concept
of product family in their planning
processes. Almost every observed

organization had distinct classes of
services provided to different types of
customers, and these different service
classes consumed the organization’s
resources differently. As consumers
of services in everyday life, we see
this dynamic happening all the time,
but we rarely examine the planning
model behind it. Every time we hop
on a plane, we are aware of coach,
business, and first-class seating. These
are different classes of service, each
with somewhat predictable demand
and finite capacity. It is not enough,
however, for airline schedulers to
know the total number of passengers
booked on a plane; they need to
estimate how many in each class or
family of service. From a planning
perspective, the demand for the
different service offerings should be
aligned with the capabilities to deliver
the service. This may seem as simple as
filling seats, but the real opportunity
is knowing how many people want
each class of service so the airline can
adjust the equipment used (either
the number of planes or the seating
configurations for each class) to
match the unconstrained demand
for premium service, and thereby
maximize revenue.
In another seemingly straightforward
example, consider the challenges of
trying to understand how to staff a loan
processing department in the mortgage
industry. You would need to plan
based on some historical reference for
demand—maybe a monthly tally of all
applicants over the last couple of years.
And in determining service capabilities,
a manager would need to understand
vacations, holidays, and work schedules
of existing employees, and then roughly
the processing time per mortgage, and
estimate—very roughly—the resource
requirements by month. This is an
effective planning process but one likely
to have considerable variances in the
estimates of both supply and demand
because of its relative simplicity.
Of course, this is an example that
screams for more data. A planner would
need to know not only the gross number
of mortgage applicants but also the
number of different types of mortgage
applicants: how many jumbo mortgage
applicants are processed in a month?
How many mid-tier mortgages, condo
or co-op mortgages, refinances, etc., are
normally processed? At most banks, a
potential customer applying for a jumbo
mortgage receives much more personal
attention, more hand-holding—a
premium level of service—compared
with an applicant for a comparatively
smaller mortgage. Applicants for jumbo
mortgages consume more of the service
capacity—the time—of mortgage pro
cessing agents, they require more follow-
up attention. Thus, the most senior loan
processors are typically engaged to
work with them. Borrowers applying for
jumbo mortgages are treated with more
of a “private banker” service model. It is
worth it because they represent a more
profitable service line. For applicants
seeking mortgages for condos or co-op
apartments, banks must understand the
variouscovenantsandbylawsattachedto
the property, which require a significant
amount of loan processing resources.
Conversely, mid-tier applicants—those
shopping for discounted closing costs
and the lowest interest rates—are
fairly straightforward to manage and
are easy on the resources, but they’re
not as profitable as others. Forecasting
service demand for clients in this
scenario requires enough granularity to
project their loan processing resources.
Jumbo, condo/co-op, and mid-tier
would seem to be perfect descriptors
for these various service families, each
requiring differing amounts of service
time. And being able to project the
average number of requests for each
type of loan application per month
would dramatically help staff the loan
processing department at appropriate
levels. All of these factors are important
elements to consider in achieving a
supply (service) and demand balance.
Two other important characteristics
that are representative of traditional
SOP are product portfolio manage
ment and strategic alignment. In manu
facturing terms, product portfolio
managementisthereviewofallproducts
(the portfolio), each month, throughout
their different product life stages. As
consumers of manufactured goods,
we are all aware of products going
through life cycle evolutions. We see
new products on the shelf, we see new
packaging or formulae (“improved”),
and we all have had a favorite product
or two discontinued. In some more
advanced SOP processes, portfolio
management is actually a “review”
meeting by itself, during which all issues
relating to product management are
discussed once a month. In the service
industry this is no different.
According to Dr. Chaman Jain,
a professor at St. John’s University,
“Managing product portfolio and
product mix are equally important
in the service industry, but the way
they view it or call it may be different.
Universities are constantly reviewing
their portfolio of services and eli
minating the departments/areas that
are least profitable and adding ones
that are most profitable. In recent
years, a number of schools have added
programs of supply chain, predictive
analytics, and big data, to name the few.
They are also changing the service mix
by offering more and more programs
online. Portfolio management in the
service industry represents the shifting
of service offerings toward the direction
of demand. It is not much different than

yourgardenvarietyconsumer-packaged
goods company.”
Finally, traditional SOP requires
a monthly revisiting of the strategic
imperativesofthebusiness.Thegoalisto
maintain alignment between demand,
supply, new products, and the strategy
of the organization. In manufacturing,
this may mean focusing limited capacity
on the products with the most strategic
importance or profitability. In the
service sector, I’ve observed a consistent
alignment to the strategic goals of the
organization in concert with a strong
attachment to metrics. Most of the
time, strategic goals were expressed
in terms of how customers were to be
served or markets to be developed.
A mortgage company wanted to be
“easy to do business with” and to “get
to money” quickly. They measure
customer satisfaction from surveys,
but they also measure time spent at
each process step, since customers
consider speed the most important
factor in overall satisfaction. In contrast,
a corrections department considers low
recidivism rates and taxpayer safety, as
measured by survey responses, to be
key elements of their strategic vision.
Such goals have a role in dictating the
type of services provided to inmates in
the correctional system. I have always
believed that SOP is easily replicatable
in the manufacturing sector because the
planning processes are similar in theory,
analytics, and language—despite cross-
industry differences in methods of sales
or modeling of capacity. In as much as
the manufacturing and service sectors
are very different, many of the planning
approaches I observed in service organ
izations were surprisingly similar to
those used in manufacturing-centered
SOP processes. Some of the examples
that follow are amazing in their depth
and level of integration, tooling, and
metrics,whileotherswerecomparatively
small and narrow, yet no less effective—
limited service planning models that
are mostly simple forecasting. What
gave me great hope of an adaptable
service-industry version of SOP is that
many of the processes overcame the
limitations of extremely divergent views
of supply, demand, and metrics, while
still retaining the core SOP tenets of
collaboration, continuous improvement,
and strategic alignment around a mid-
to long-term plan. You will see plenty of
such similarities as well as some of the
differences outlined in these examples.
Sometimes a single example can save
10,000 words of copy, with that in mind.
I offer six real-life observations.
CONSULTING SOP
EXAMPLES
A decade ago, I spent three years
at a boutique supply chain consulting
company where we had our own,
albeit limited, proxy for an SOP
process. We had a weekly pipeline
call, based on a detailed spreadsheet
of active and potential engagements
(with consulting assignments) and
the expected availability dates of all
the consulting talent. The pipeline call
was essentially a consensus meeting
during which sales and consulting
managers discussed future demand
and consultant availability. They
also sought agreement about the
assignment of consultants to future
engagements. If we needed a specific
skill set to close a deal—someone with
SAP APO expertise, for example, we
knew to project (and source) the talent
we needed as well.
This process was not perfect. It
was not a classic SOP model, and we
certainly did not call it SOP, but it
functioned very similarly. We balanced
supply and demand in both the long
and short terms. We had known orders
(existing ongoing engagements), a
forecast (the sales pipeline), inventory
(the consultants themselves), lead
time (the timing of the availability
of consulting resources), and new
product (new hires). All of these would
go into the aforementioned supply-
and-demand balancing process. Our
goals were to maximize revenue
and minimize consultant downtime
by matching the supply of talent
to the demands of the consulting
engagements. We also sought to direct
our talent strategically by pursuing
engagements that posed the highest
long-term value add.
In support of this process, we
coordinated input from our creative,
marketing, and human resources
groups, and we worked to maintain
alignment on strategic direction. We
created white papers, held seminars,
participated at conferences, leveraged
social media and email blasts, and
hosted webinars to shape demand
toward practice areas we wanted to
emphasize. All of these elements were
discussed as part of the pipeline call.
The process was very SOP-like, sans
the moniker.
SOFTWARE COMPANY
EXAMPLES
I observed a similar demand-
side process when I worked at
two different software companies.
Both companies had sales pipeline
discussions like the one I observed
at the consulting company. They had
numerous prospects representing
future demand and marked each
with a different level of progression
or maturation as it advanced through
the sales cycle. Included in these
pipeline spreadsheets were account-
by-account reviews, with an estimate
of revenue and a probability of closing

the deal.
Both software companies had
great urgency to predict future service
needs, specifically the availability of
implementation consultants, since the
company’s revenue recognition was
based on fully implemented software.
The pipeline call was a weekly effort
to understand future revenue streams
and to gain alignment between the
sales (demand) and consulting (supply)
organizations, which was necessary
to ensure the timely installation of the
software for clients. When there was
an imbalance between the availability
of internal service talent and the need
to sell the software as well as complete
implementation services, then the
overflow service requirement had to be
outsourced to certified partners.
In these meetings, we tracked
pipeline progress and estimated timing,
closure rates, consultant utilization,
consulting profit as a percentage of
revenue, and even training-class fill
rates. We measured our ability to predict
future demand and the utilization of
resources. And if a prospect needed a
specific feature added to the software,
we engaged RD in a discussion about
projected timing and complexity. In
hindsight, this all seemed to be very
SOP like.There were robust discussions
of demand (projected software sales)
and supply constraints (consultants),
understanding of future revenue
streams,engagementbetweensalesand
RD, and external communication with
key stakeholders (certified partners).
It was not SOP (odd, considering it
was supply chain software), but it was
a detailed, collaborative, short- to mid-
range (1-year+) planning process.
CORRECTIONAL
FACILITIES
Sometimes you get lucky, and
such was the case when I attended
an Institute of Business Forecasting
conference, and randomly walked into
a seminar that described a forecasting
process for a state prison system. In
some states, a prison system can be a
fairly big industry, one that needs to
be managed and balanced with the
needs of public safety in mind. So, how
do you forecast prison usage? It starts
with the court docket. A docket is a
listing of criminal charges against an
individual. In most instances, criminal
cases flow through the justice system
at a rather consistently timed, and
predictable pace, with sentences—in
the event of guilty plea or conviction
fairly easy to estimate. In this example,
projection is based on a rather narrow
set of likely outcomes. A first-time
offender found guilty of first-degree
larceny faces a sentence likely ranging
anywhere from six months to three
years. According to the presenter, the
average court case takes about six
months to be resolved, either by plea
or by trial; and once charges are filed,
conviction rates are very high—more
than 90%—which makes predicting
the timing and sentence duration
of new convicts relatively easy. By
analyzing the historical progression
and timing of yet-to-be-sentenced
offenders as their cases progress
through the criminal justice system,
a planner may reasonably forecast
future demand (prisoners) relative to
the supply (prison capacity) simply by
leveraging the criminal court docket.
Adding a wrinkle to the prison
planning process is that different types
of crime dictate the specific types of
capacity required. Each pre-sentenced
offender is classified in advance.
Violent criminals, for example, warrant
a higher level of security in terms
of capacity (e.g., supermax prisons)
while individuals charged with lesser
offenses, like white-collar crimes or
simple DWIs, require housing in less
secure facilities or perhaps even in
alternative prison programs, such as
home detention or halfway houses.
When capacity in a prison system
reaches its maximum, information
on the prisoner population is passed
to the parole board and to judges.
Capacity actually is part of a judge’s
decision tree when sentencing. It is
also used by parole boards to help
determine whether or not to grant
early parole. There is even a long tail
in prison systems—offenders nearing
the ends of their sentences are often
opportunistically moved to lower-
security facilities or even granted early
release to help free up prison capacity.
The presenter explained how this
specific prison system used an SOP-
like planning process to forecast long-
term capacity requirements while
also balancing supply and demand
with utmost attention on the strategic
imperative of public safety. Dangerous
convicts are held for the duration of
their sentences while lower classification
offenders are released opportunisitically
to make space. The presentation also
helped me to clearly understand pitfalls
unique to poor planning in the service
sector; prison overcrowding.
At the time of presentation, the
actress Lindsey Lohan was released
from custody after serving just a few
hours of a 90-day sentence because
correctional authorities failed to plan
for an adequate number of prison
beds. Needless to say, the presentation
proved to be a fascinating discussion
topic, and in the end it all came down
to how you visualize supply and
demand. I left thinking how clever
the presenter was for creating this
sophisticated SOP-like process with
elegant feedback loops to support the
criminal justice system.

SCHOOL SYSTEMS
I have observed the same approach
extended into education—a local
school district that uses statistical
models to predict the need for schools
(opening/closing/mothballing) based
on population statistics. Many districts
are closing or mothballing schools
as children of the baby boomers (the
echo boom) are now getting older and
the student population is temporarily
lower. Keeping schools open and hiring
or laying off teachers are significant
strategic moves that are best made
with a deep, accurate understanding
of the supply and demand balance of
students, teachers, and facilities. In
this model, schools and teachers are
the capacity, while projected student
population represents the demand.
Is this traditional SOP? No. But are
district leaders striving to balance
capacity with future demand? Yes.
In fact, this service-based planning
process is so essential that it is revisited
quarterly, as population estimates
fluctuate. This is clearly a smaller,
limited example of an SOP-like
process, because it has all the critical
elements. Satisfaction is measured in
terms of class sizes, standardized test
scores, and effective use of taxpayer
dollars.
One key factor that points to a
unique urgency for advanced planning
within school systems is the population
of students requiring extra care. This
may range from paraprofessionals
assistingdisabledchildrentoproviding
focused education for learning-
impaired students. There is not one
single type of student, but many. And
these differing types, whether based
on physical or cognitive need, must
be by law served with specific types
of services mandated to educate all
children.
SOP, not exactly. Long range
integrated planning—absolutely.
HEALTH CARE
During an Ops Research class, a
classmate once spoke about a supply
and demand planning process in an
atypical service application. He ran
the radiology unit for a large health
care system in the New York City area.
He was in charge of a wide range of
very expensive equipment, from x-ray
machines to MRI equipment, all of
which needed to be kept fully utilized
to help offset the investment in such
resources.
Obviously patients served by this
equipment arrived with different
levels of prioritization—from critical
care (a head MRI after a car crash) to
less time-critical usage (a knee MRI
prior to meniscus surgery). Scheduling
and allocating the equipment to the
highest-priority needs—as well as
predicting the future utilization and
capital requirements—were all vital
aspects of my classmate’s job.
Like most supply chain managers,
his challenge was to manage the
steady workflow of everyday, low-
priority volume but also to plan in
advance to expedite the unpredictable
demand of critical care patients. He
even employed classic notions from
the manufacturing sector, like buffer
time—periods when the machines
were intentionally planned to be
left idle (i.e., in reserve) even during
times of peak routine demand, to
accommodate the uncertainty of
critical demand—and deferrals,
periods of time when hospital patients
would have radiological procedures
performed overnight, on occasions
when the machines were overbooked
during daylight or evening shifts.
During the daytime and evening, he
would flex-fill this intentional slack
capacity at his discretion, assigning
readily available individuals such as
early outpatient arrivals and hospital
patients so that he was always pushing
any slack time to the back of the shift.
He also devised weekly and
monthly meetings to review forecasts
of loading, segmented by average
demand according to patient type (i.e.,
routine vs. critical care), and medical
department. His consensus group
gathered information from each health
care discipline to get a handle on near-
term needs like scheduled surgeries
and employee work schedules. He
was even able to project long-term
capital expenditure requirements by
determining when utilization was
routinely planned to exceed 80%.
His group used a modified version of
a finite scheduling tool to plan and
balance near- and long-term loading.
This approach was definitely not
full-blown SOP, but my classmate
used many supply chain planning
concepts and tools. He forecasted;
planned using a finite capacity tool;
incorporated effective concepts of
planning for uncertainty; buffered his
inventory of machine time by deferring
lower-priority procedures; pulled
forward demand opportunistically to
fill slack time; developed a balancing
process that matched the resources’
availability to the needs of the patients
over both long- and short-term
horizons; and collected, analyzed, and
incorporated numerous performance
metrics into his overall planning
strategy. He even projected future
needs. He used all of these methods
to gain maximum control over his
service-oriented business operation
because the implications of misusing
the capacity, or underestimating
loading, could result in delivering
potentially life-threatening levels of

customer service or, alternatively,
wasting incredibly valuable capital if it
were underutilized.
MORTGAGE
INDUSTRY
This final example comes from a
recent discussion with a friend who
runs the mortgage business for a
major bank. He told me of a planning
process similar to SOP that the bank
uses to plan its mortgage business.
He estimates demand for mortgage
applications over both short and
long horizons by looking at historical
ebbs and flows in interest rates,
housing starts, and general economic
bellwethers. Even so, his forecast
is intentionally conservative, to
hedge against downside risk such as
unexpected local economic changes,
like a large layoff by a significant
employer.
The company’s capacity is defined
by its capability to manage customers
(applicants) through the process of
mortgage application, risk assessment,
and closing. The entirety of the
process is measurable in terms of time,
mistakes, and customer satisfaction.
Throughput capacity is a function
of trained, well-qualified back-office
loan processers. The more and better
trained the people are, the greater the
throughput will be.
Thecompanykeepsaclosewatchon
its performance metrics—expected vs.
actual close rates, quality of applicants,
cycle time, step times—that we in the
traditional supply chain world might
view as forecast accuracy, attainment,
or cash-to-cash cycles. The marketing
arm of the bank even tries to shape
demand when mortgage application
rates dip below forecasted thresholds,
soliciting existing mortgage holders
to consider refinancing. The bank has
the advantage of knowing both the
existing and the potential mortgage
rates of its existing customers, and
thus the financial opportunity they’re
being offered.
Monthly planning meetings
focusing on demand and resourcing
are held at a corporate office, while
local offices hold similar meetings that
focus on the front end of the process.
If planners foresee shortfalls in their
projections—gaps in their forecast—
they can respond proactively by
offering teaser rates or discounted
points, typical levers to stimulate or
shape demand for mortgage inquiries.
Again, all of this activity seems a
lot like SOP, including coordination
between sales and marketing and
what most banks call their back-
office or operations group, but no
one calls it SOP. The benefits of
providing exemplary service based
on effective planning, however, help
validate the solid brand identity of this
organization—a bank that is easy to
do business with!
WHAT DO THESE
EXAMPLES
DEMONSTRATE?
From all of these examples, it is
obvious that integrated planning
exists in the service sector, and at
times it is rich, elegant, and robust. It
is not called SOP, nor by any other
related name. It is often shortsighted,
missing some integration points in the
SOP model that would make it better.
My friend in the radiology department,
for example, never shared his results
with his peers. So while he was busy
optimizing his own department, he
may have quite possibly been wreaking
havoc on others. The school system
that I mentioned failed to engage the
local community in its estimates while
pushing for renovations and build outs
of some of its schools. Collaboration
was a point of failure. And although
the corrections department example
was the most complete of all, in terms
of approximating true SOP, its leaders
did not carefully assess the financial
impacts of their decisions, and thus
missed out in meeting their operating
budget.
I did observe similarities in some
planning approaches within the
service sector. The consulting and the
software company examples were very
close in terms of planning tools and
concepts. It was almost as if they were
channeling a professional services
version of service-centered integrated
planning. The correctional and school
system examples were also very similar.
Both had issues relating to capacity
(beds/classrooms and teachers), and
the challenge of effectively classifying
their demand (inmates and students),
an important element in the supply-
and-demand balancing process. This
suggests the potential for a public
services iteration of SOP. I also found
the mortgage company example to
be similar to the operations of an
insurance company I once assessed,
and the radiology equipment example
had remarkably similar parallels to a
transit system with which I am familiar.
I was delighted to observe similarities
within these service segments, the
realization of which led me to believe
that, with the right implementation,
SOP could possibly spread within the
service sector.
When I began seriously considering
the viability of SOP for the service
sector, one of my hypotheses centered
on whether there was potential for
a well-articulated benefit stream.
During my review and assessment of
the various examples detailed here, I

recognized tremendous commonality
in the benefit streams of each of
the integrated planning examples I
observed. And although there were no
inventory reductions or improvement
in working capital utilization, the
remaining benefits were mostly
consistent with those typically
resulting from SOP processes
effectively deployed within the
manufacturing sector. These include:
Better Control of the Organization:
The process of actively and frequently
viewing the inflow of demand, as well
as the use of capacity and capabilities,
gives leadership a better feel for the
planning process. The examples that
had an executive review meeting
seemed to have the most overall
satisfaction.
Improvement in Forecasting: Every
organization claimed this benefit and
it seemed to have the most uniform
downstream effect, since it tended
to lead to better utilization of service
capabilities and yield with more
predictable results/revenues. Further,
those organizations that measured
their forecast accuracy seemed to have
the most overall satisfaction with their
planning process.
Demand and Service Shaping:
Several of the organizations were able
to shape their demand stream around
service constraints. The mortgage
company sought to move both
existing customers and new customers
toward refinancing (via incentives)
during the non-peak season, to help
level the load of resources in the loan
processing department. Similarly,
planners in the corrections department
regularly offloaded capacity based on
projected demand. And the radiology
department worked opportunistically
at a tactical/execution level by shift
ing demand to fill vacancies in the
schedule.
Long-Term Gap Detection/Capital
Outlays: The school system and
corrections department were both
skilled at determining long-term
capital needs and even shorter-term
capacity requirements. The corrections
department had on-site trailers to
use as alternate capacity to provide
housing for lower-level offenders if
they hit overflow. The school system
was excellent at planning in relation
to long-term cyclical trends based
on projected population expansion
and contraction. And the radiological
group assessed its own machine
loading statistics, and looked at
changes in diagnostics methods to
help determine both what type of
equipment to buy, and when, to best
supplement its existing inventory of
x-ray and MRI equipment.
Integrated Service Offering: The
mortgage company was very smart
in terms of integrating services.
Managers required mortgage holders
to have a (free) checking account with
the bank. As an added incentive, they
offered customers a small cash-back
percentage at the end of each year,
based on the value of transactions
processed through their checking
accounts, and thereby raising their
total cash balances. They provided
mortgage holders with premium
discounts on related property
insurance services, and offered highly
competitive rates on other products
such as auto loans.
Measurement Driving Improve
ment: In their metrics-heavy examples,
the leadership teams felt that regular
and public measurement of results
were a positive force for change and
led to improved revenues, lower costs,
or better utilization.
New Service Creation or Inte
gration: In many of the commercial
examples, especially those with senior-
level review meetings, managers were
able to readily identify and integrate
new service offerings.The expansion of
the mortgage platform and improved
planning enabled the bank to buy a
failing insurance brokerage. And cross-
marketing efforts between the two
groups helped increase the fortunes
of the insurance company. The
radiological practice partnered with
an outsourced group of radiologists
that afforded the capability to view
scans around the clock and was more
affordable than staffing up by hiring
additional internal resources.
More Consistent Revenue: This was
of great importance to the commercial
service providers, which was ach
ieved by a better understanding of
demand. Demand shaping based on
the constraints enabled managers
to focus on new—or otherwise
missed—opportunities to serve. The
mortgage company reaped the largest
improvement in benefit streams, while
the software companies and, to a
lesser extent, the consulting company
also saw markedly improved revenues.
A MODEL FOR GOING
FORWARD
Each of the examples described
here provides broad insight into
very different types of demand and
capacity as they are embodied in
different service industries. However,
by looking at the processes used
across all of the organizations, one can
find similarities in the ways supply and
demand are processed and balanced,
as well as how service offerings are
aligned to the strategies of the various
organizations. While no one entity
represents a perfect adaptation of the
SOP conceptual model, compiling a
composite best-of-show, across all of
the examples, suggests there are eight

core elements that would best enable
SOP for the service sector. These are:
A Notion of Demand: Projections
of the service demanded should occur
formally in a consensus meeting based
on collaboration and inputs from both
internal (statistics and history) and
external sources (such as collaboration
with service partners). All service
estimates should be measured using
a proxy for forecast error (deals
converted, for example, or revenue
projected vs. actuals, etc.). Accurate
projections of service demand will
likely be more difficult to ascertain
than traditional forecasts compiled
in a manufacturing realm. And they
will vary in calculation, in context,
and in the manner by which such
forecasts are presented by company
and industry. Some of these forecasts
will be less like firm projections and
more like an expression of subjective
probabilities. For example, a software
deal with a revenue expectation of
$100,000 and a 90% closure potential
is not as firm as a production forecast
for 100,000 widgets. However, this
inherent uncertainty should not
be an impediment to developing a
forecasting process with an 18- to
24-month horizon. The quality of such
projections should be part of your
discussion in a demand consensus
meeting.
Projected Supply: The service
capabilities/utilization of the organ
ization should be projected into the
future. Depending on the industry, of
course,thisconceptmaybeexpressedas
the number of available machine hours,
for example, or a projection of future
capabilities, like the number of SAP APO
consultants expected to be available
to work by June of the following year.
Manufacturing companies understand
their process capabilities (We can
make 1,000 widgets per hour). Service
industries need to similarly model such
service constraints around their own
unique demand characteristics. For
example, by analyzing factors like the
average number of critical care patients/
time-in-machine versus the number
of non-critical care patients/time-in-
machine. And as in manufacturing,
service industries should seek to identify
any significant constraints relative to
the throughput of their service delivery,
in an effort to optimize their ability to
serve.
Supply and Demand Balancing:
Projections of demand, expressed in
terms of service families, is matched
against service capability (i.e., I have 100
mortgage applicants estimated for each
of the next 12 months. Can I meet that
demand?), and mismatches are elevated
to a senior-level meeting. As previously
noted, mismatches may actually
indicate opportunities to process more
applicants and identify more available
prison beds than expected, or perhaps
identify other mismatches such as an
MRI machine being overcommitted two
months out. Either way, mismatches
should be escalated to a management
review meeting with a goal of evaluating
the situation—as well as any potential
tradeoffs relative to decision making,
one way or the other—based on the
strategic imperative of the service
entity and understanding of all relevant
financial implications.
New Products: New service offerings
should be part of any discussion
relating to demand. This could be a
new MRI machine, new online classes,
completely new service offerings, or
even an extension of current services.
A bank with a mortgage company
might acquire an insurance company to
develop a suite of services to market to
new homeowners. This suite approach
might (should) change the revenue
potential. It will also likely impact other
operational factors, such as potential
back-office throughput and human
resources requirements. Their impact
on revenue and throughput should be
carefully considered and discussed in
the senior-level discussion.
Strategic Alignment: Strategic goals
and imperatives should drive demand,
supply, or decisions made in relation
to the balancing process. In addition,
metrics should be aligned toward the
achievement of strategic objectives,
which are normally expressed as some
level of quality service expectation.
Deviations and departures from strategy
are topics for discussion at the senior
management review meeting.
Metrics: Measures of the process,
cycle times, costs-to-serve, and utiliza
tion should be part of a regular review.
Satisfaction—whether it is expressed
in terms of mortgage applicants or the
taxpayer’s sense of security—should be
tracked along the customer experience
curve. Similar to the manufacturing
version of SOP, service-related SOP
should review metrics—particularly any
variations from expected results—at a
senior management review discussion.
Meetings: The planning process
should have a regular rhythm or cycle
to it. Weekly, monthly, or quarterly,
meetings should be part of the process
and modeled in such a way as to foster
collaboration and focus on service-
related demand, capabilities, new
services, and measures. Each of these
steps should incorporate a regular
review component that is designed
to obtain alignment with appropriate
cross-functional teams. As in traditional
SOP, the notion of collaboration—
whether in pursuit of the smartest, or the
most profitable, or the least expensive
solution—is paramount. And a meeting
involving senior leadership should occur
monthly, to inform them of the latest
demand estimates, problems, issues,

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conflicts, capability shortfalls or excess,
metrics, etc.
Financial Assessment: Top-line
revenue and net profit or direct brand
contribution may or may not apply in
the service version of SOP, but certainly
public institutions have budgets and
capital plans. Decisions should be made
based not only on balancing supply
and demand and on aligning with the
strategic imperative, but also on a least-
cost/greatest-profit, and they should
project capital requirements over a two-
to three-year horizon.
SERVICE INTEGRATED
PLANNING PROCESS
MODEL
Long-time SOP practitioners are
keenly aware that sales and operations
planning is not about a specific type
of industry planning; rather, it is
about creating a culture of planning,
measurement, strategic alignment,
and collaboration that permeates an
organization. Knowing this, and after
reviewing all of the prior examples, it is
obvious that the SOP planning model
is extensible and can be adapted for
use in the service sector. To this end,
I propose a process model based on
the common traits culled from these
examples.
While it might make sense to
borrow from process model diagrams
commonly used to illustrate SOP
implementations in a manufacturing
context—most often a circular or
stair-step model—I think that a simple
linear model serves best as a prototype
to depict an optimal service integrated
planning (SIP) process. You will note
that this model is nearly identical to
the one depicted in Figure 1, which is
the process model for SOP with only
changes in terminology to better aid in
understanding.
As shown in Figure 2, the model
first calls for generating demand
projections or forecasts for baseline
services and any new services. These
are defined as service characterizations
or types. This step is followed by
capacity or capabilities modeling to
represent current estimates of ability
to serve (available capabilities). The
next step is a balancing process by
which managers seek to identify
any inabilities to serve or excess
capabilities by comparing all identified
service-demand characterizations to
the available capabilities. Finally, a
senior management discussion serves
as the monthly process capstone
meeting within which all measures,
issues, gaps to operating plans, and
discussions regarding strategies are
discussed.
A service-based integrated plan
ning process (SIP) differs from SOP
in a few ways. The service sector is
based on people serving other people.
Decision-making has a different feel
as well. Planning participants must
carefully consider the service objective
or strategy (which should be clearly
spelled out) and always anticipate
how end users may feel about the
service being planned. However, the
mechanics of the process are not
unlike those of a manufacturing-based
SOP process. It is hard not to see
the upside of incorporating an SOP-
like process into the service industry,
or to recognize the great potential
to provide demand and/or revenue
predictability and stabilization.
Although there was not a complete
implementation of SOP in the service
industries surveyed, it’s evident there
are some great integrated planning
practices at work. None of these
matched the high level of process
maturity of the traditional SOP world,
but there is hope.
Documenting examples of these
SOP-like instances—whether as
case studies or in journal articles—
would make significant strides toward
articulating the benefits (and reducing
the risks) of advancing such planning
approaches to a next level of visibility,
awareness, and industry acceptance.
Maybe this article will serve as a spark
for future discussion along these lines.
Call it SOP, SIP, or anything you
want. As the examples in this article
suggest, the basic underlying tenets
and benefits of SOP can be cascaded
into diverse service industries. The
challenge is to find suitable proxies
for evaluating supply and demand, to
align these with your own business
strategies, and then collaborate,
collaborate, collaborate.
—Send Comments to: JBF@ibf.org
.
Figure 2 | Service Integrated Planning Process
Demand Review
(Meeting) Including
New Products
Supply Review
(Meeting)
Supply Demand
Balancing Exception
Management
Senior Management
Discussion
ISSUES, GAPS, METRICS, FINANCIAL IMPLICATIONS, STRATEGY
ISSUES, GAPS, METRICS, FINANCIAL IMPLICATIONS SERVICE STRATEGY
Service Demand
Estimation Incl.
New Service Offerings
Service Capacity一
Capabilities Estimation
Service Supply
Demand Balancing
Senior Management
Discussion

E x ecu t i v e S ummar y | This column discusses what is commonly known as unconstrained demand, which
represents customer demand devoid of any impacts due to supply limitations. It recommends extending the concept to
focusing on “supply-neutral” demand that also reflects demand devoid of distortions due to supply surpluses and other
supply-related factors. Over time, forecasting demand that is not supply-neutral can “condition” customers to demand
product based on available supply rather than on true demand needs. Several examples of these distortions in real-world
settings are discussed and forecast data cleansing methods are recommended to estimate true demand from the data.
Larry Lapide | Dr. Lapide is a Lecturer at the University of Massachusetts, Boston and an MIT Research
Affiliate. He has extensive experience in industry, consulting, business research, and academia as well as a broad
range of forecasting, planning, and supply chain experiences. He was an industry forecaster for many years, led
supply chain consulting projects for clients across a variety of industries, and has researched supply chain and
forecasting software as an analyst. He is the recipient of the 2012 inaugural Lifetime Achievement in Business
Forecasting Planning Award from the IBF. He welcomes comments on his columns at llapide@mit.edu.
(This is an ongoing column in the Journal, which is intended to give a brief view on a potential topic of interest to practitioners
of business forecasting. Suggestions on topics that you would like to see covered should be sent via e-mail to llapide@mit.edu.)
Supply-Neutral versus
Unconstrained Demand
By Larry Lapide
I
recently attended an interesting
IBF Boston chapter meeting
hosted by forecast managers at
a Stonyfield Farm Yogurt plant in
New Hampshire. The meeting started
with a plant tour and snacks, and
was followed by a presentation by
its forecasting team. The managers
discussed how forecasting is done
there, a lot of questions were asked,
and discussions ensued to make it a
learning experience for everyone.
After the meeting, I noted to
the leader of the team that I was
impressed by the fact that the
managers had mentioned several
times that they had implemented
forecast methods aimed specifically
at generating “unconstrained” de
mand forecasts. Most forecasters
recognize that a forecast organization
is ultimately responsible for providing
planners (such as in a Sales and
Operations Planning [SOP] team)
with “unconstrained” forecasts rather
than ones “constrained” in any way by
limited supply. These are essentially
projected business that would be
generated if a company had an infinite
and immediate supply to fill customer
demand—when, where, how, and in
what quantities demanded. Some
forecast organizations, however, don’t
recognize or realize the need, nor do
some take the effort to go far enough
in this regard. Yet from a competitive
perspective, they should, despite the
fact that it is often easier said than
done.
In my Journal of Business Forecasting
(JBF) column, “Forecast Demand or
Shipments?” (Spring 1998), I stated
that “forecasters out there that are
currently using a product’s historical
shipment (or sales) data to forecast
customer demand should take heed.
Use of this data may be dangerous to
your demand forecasts! The primary

reason for this is that a shipment-based
forecast is often not a clear indicator
of what your customer’s demand for
a product might be in the future.” I
also discussed several anecdotes in
which companies were (unbeknownst
to them) using constrained data for
forecasting, because what appeared to
be unconstrained demand was really
constrained or influenced by other
supply-related factors. I then covered
various methods that might be used to
better align historical shipment data to
better reflect unconstrained demand.
This column updates my view on the
subject.
DEMAND CAN BE
DISTORTED BY
OTHER SUPPLY-
RELATED FACTORS
I recall a comment made by the late
Dick Clark during a discussion about
the difference between constrained
and unconstrained forecasts. Dick,
the consummate industrial forecaster
(who was PG’s forecasting guru for
several decades before he passed
away a few years ago) doubted that
“true” unconstrained demand even
existed. I never really understood
what he meant by this until recently,
largely because I was simply viewing
unconstrained demand as just demand
devoid of any impacts due to supply
shortages—such as distortions caused
by lost sales due to stock-outs or late
shipments due to backorders.
There are times when other supply
factors, such as a surplus of supply, can
affect demand as well. Thus, the term
unconstrained demand is a bit of a
misnomerinthisregard,andtheproper
term should be extended to supply-
neutral demand. Therefore, forecasters
should give the matter more attention
than they do today, because these
other supply factors, that influence
and distort true demand, may not be
as transparent as those that relate to
supply shortages.
I believe that this was what Dick
was somewhat referring to with his
comment. Many companies“condition”
their customers’ ordering behavior to
align with time periods when product
availability is plentiful. For example,
there might be times of the year
when product availability is scarce (at
a reasonable price), and this might
foster customers to avoid buying the
product during these times, despite
the fact that that is when they really
need it. This type of conditioning
caused by supply factors is often done
unconsciously, is not planned for, and is
not transparent. Certainly promotional
activities that influence demand are
consciously done and planned out in
great detail, because the main job of
sales and marketing organizations
is to shape and create demand.
Conceptually, supply-side managers
should not be influencing demand to
the extent that they are conditioning
customer-buying behavior. Yet these
factors, in conjunction with marketing
and sales demand-shaping activities,
lead me to believe that it is no wonder
that Dick believed it is very difficult to
get a good handle on true demand,
devoid of both supply- and demand-
shaping factors.
That said, forecasting demand
devoid of any supply issues is still
important from a competitive
perspective. Conditioning customers
to buy product when, where, how, and
in what quantities it is most convenient
for a supplier might well suffice in the
short-run. However, it could foster a
false sense of comfort in perceived
customer loyalty. For example, in the
short run a customer might be willing
to align its demand to suit its supplier’s
product availability, possibly because
there aren’t other suppliers that can
meet the customer’s needs. However,
there is a risk that a competing
supplier may come along and steal the
business away in the long run. There is
no such thing as long-term guaranteed
business in a competitive free market!
SUPPLY-RELATED
DEMAND
DISTORTION
EXAMPLES
While supply shortages due to
backorders and stock-outs are not easy
to gauge and correct for, at least they are
relatively transparent and purposeful.
Demand influenced by supply surpluses
and other factors is often inconspicuous
and not purposeful. The following six
anecdotal illustrations I’ve encountered
show how these supply factors can
unknowingly influence demand.
1. During a workshop I conducted
with the SOP team of a global
tire manufacturer, the topic of
constrained versus unconstrained
demand forecasts came up. The team
leader went around the room and
asked each region’s process leader
what type of forecast was submitted
to the planning process. The first
three leaders that represented
North America, Latin America, and
Europe stated that they submitted
unconstrained demand forecasts.
The last, the Asian-Pacific leader,
to the surprise of all, said that they
submit a constrained demand
forecast. Flabbergasted, the SOP
team leader asked: Why? The leader
glibly answered that “we never get
the supply we ask for, so we submit
a forecast reflective of what supply
we think we may be able to get.”

Thus this leader was essentially
distorting true demand and likely
hampering the growth of the region
by submitting demand forecasts that
were not supply-neutral.
2. Anewstoremanagerwasresponsible
for ordering inventory for each
week’s promoted sale items. She did
this by reviewing reports showing
each item’s sales performance during
prior promotions. Her predecessor
was conservative in nature, so he
always under-ordered promoted
items to insure none would be left
after the promotion was over. His
store frequently ran out of promoted
items by Friday, despite the fact that
promotions went through Saturday.
Was the new store manager looking
at true demand in reviewing the past
performance of an item? Obviously
not. If she uses this data, her store
will tend to run out early, and leave
little or no inventory for customers
who come in for promoted items on
Saturday. The reports she looks at
represent supply-influenced demand
or demand distorted from the loss of
business from an untold number of
Saturday shoppers—and due to the
conservative nature of the prior store
manager.
3. Every August a company shuts down
its plants for summer vacation.
Thus, historically shipments in
August are extremely low, while
shipments in July and September
are extraordinarily high. This is due
to customers ordering earlier than
they wanted, ordering later than they
might like, or just being backordered
because the plants are shut down.
While customers have potentially
gotten used to this over the years, it
is likely that this conditioning might
not bode well for the company in the
long run.
4. Corporate buyers for an apparel
retailer always send a mix of sizes to a
store based on the store’s prior sales,
which are similar to the mix of the
average store. The store, however,
is in an ethnic Asian neighborhood
where the population is somewhat
smaller than that of the average store.
Every season the store’s manager has
to drastically mark down the larger
sizes because few people need them.
When she finally marks them down
to below cost, they eventually sell
out. Since all sizes eventually sell,
this indicates to the corporate buyers
that the store’s size mix forecast was
accurate because every size sold
out. The drastic markdowns are not
visible to the corporate buyers, so
they continue to send the store the
same mix of sizes year after year;
and the store manager continues to
mark down the prices of larger sizes
to clear up the surplus stocks. In this
case, the corporate buyers are not
using true demand to allocate sizes.
They are using shipments and sales
that are distorted by a surplus of the
larger sizes that has to be drastically
marked down every year. Obviously,
while there are markdown sales of
the larger sizes in this store, there
really is little true supply-neutral
demand for them.
5. A distribution center (DC) in Boston is
frequently out of stock of a particular
item because the manager thinks
the item is too cumbersome, takes
up too much space in his DC, and
consumes too many labor hours to
handle. Whenever a local customer
orders it, the manager often gets the
item shipped to the customer from a
Hartford DC. Corporate distribution
planners that use DC shipments to
determine how much inventory to
deploy, see little being shipped from
Boston; thus they deploy very little
inventory there. Meanwhile, they
deploy a lot in the Hartford DC. It
is no wonder that Boston is always
out of stock and Hartford always has
a surplus. Since Boston customers
typically have to wait longer for their
deliveries coming from Hartford, they
have been conditioned over time to
accept later deliveries, or possibly
gave up and starting ordering from
a competitor. Thus, true demand has
been distorted by the whims of the
Boston DC manager.
6. The last situation involved a grocery
storechainthatdidbusinessinPuerto
Rico (PR). Each week, the stores
ordered goods from a warehouse in
Florida where the goods were loaded
in a container for shipment. Often,
after all the ordered goods were
loaded, there would be a lot of extra
space left in the container. So to save
transportation costs, workers filled
in the extra space with paper-goods.
When a store manager in PR got
the extra paper goods and realized
that there was a surplus, he would
conduct a sale to get rid of them.
Over time, the store managers were
running weekly sales—that is, until it
was discovered what the warehouse
workers were doing. In effect, to
reduce transportation costs, the
warehouse workers invariably forced
store managers to heavily discount
paper goods and conditioned
consumers to buy on promotion.This
definitely distorted true demand,
all by creating unnecessary supply
surpluses.
Ineachillustrationabove,shipments
and sales do not reflect supply-neutral
demand for reasons other than just
supply shortages. These include
distortions resulting from supply-
chain manager behaviors/whims,
SOP planner miscommunication, ad
hoc distribution execution, and an
overreliance on shipment/sale data to

forecast demand. In all the cases, the
supply-related distortions were not
transparent to demand forecasters. In
addition, it took a lot of investigation
and analysis to assess if true demand
was being distorted by supply, as
well as to identify the specific supply-
related causes.
SUPPLY-NEUTRAL
DEMAND DATA
CLEANSING
A demand forecasting organi
zation’s primary role is to provide SOP
planners with a demand forecast that
incorporates the impacts of all future
demand-shaping activities planned by
the sales and marketing organizations.
It should not, however, include impacts
due to supply-related factors. This is
what is often termed the unconstrained
demand forecast, though it should be
better extended to a supply-neutral
forecast, devoid of any distortions due
to supply-related factors.
While that sounds reasonable, how
should one develop these forecasts
from historical sales, shipment, and
booking data that include distortions
to true demand caused by both
demand and supply-related factors?
Basically the historical data must first
be cleansed of these distortions before
using it to forecast true demand.
Typically forecasters start with the “de-
promotioning” or demand-cleansing
of the data, which involves sifting
out the effects of sales and marketing
promotional activities aimed at
demand-shaping. Methods for this are
not discussed in this column.
Next the demand-cleansed data
needs to be cleansed of supply-
related distortions to true demand.
While this is normally done today for
supply-shortage distortions to true
demand, this also needs to include the
cleansing-out of other supply-related
distortions. Two general approaches
to cleansing are described below.
The first approach is to try to
capture data at the time of orders that
better reflect supply-neutral demand.
These include:
• Capture the date a customer really
wanted the product instead of the
negotiated due-date between the
customer and the company’s sales/
customer service representative.
• Capture “lost sales” by keeping track
of orders that were not placed due to
a lack of product availability.
• Capture the date of the order, rather
than the date of its shipment.
• Capture shipments based on
customer ship-to locations instead
of a company’s ship-from locations.
Ship-to locations would be used
in historical shipments to get
geographical demand profiles. (This
method would have been useful for
the Boston DC example described
above.)
The second approach is to adjust history
to more closely reflect true demand
such as by adjusting shipment and sales
data prior to using it to forecast. Some of
these adjustment methods include:
• Capture out-of-stock information
and adjust the shipment/sales data
during out-of-stock periods. For
example, estimate lost sales that
occurred during out-of-stock periods
and add them to shipments in these
periods. (This method would be
useful for the retail store example
described above. That is, estimate
what an item’s promotional sales
would have been on Saturday if the
product were in stock. Then add
the estimate to actual historical
sales from Sunday through Friday.
This would give an estimate of true
demand for the promoted item for a
whole week.)
• Capture information on backorders,
as well as order, manufacturing, and
distribution processing delays. Use
the information to adjust historical
order shipment dates.
• Capture pricing information and use
it to reduce sales data during periods
where prices were marked down to
“bargain basement prices” to “dump”
unwanted merchandise. (This would
be relevant for the apparel size mix
example described above.)
In addition to these general
approaches, there are also a variety of
ad hoc corrections that will depend
on the nature of the supply-related
distortions. For example, in the case
in which the Asian Pacific SOP leader
was submitting constrained demand
forecasts, this was easily rectified at the
meeting once he realized it should have
been unconstrained demand forecasts.
In the case of the DC workers stuffing
extra paper goods on to unfilled trucks,
this was solved by setting a policy to
stop doing it. A detailed analysis would
have to be conducted in the case of the
Augustplantshutdownstoestimatehow
much business was lost, and how much
product was bought earlier or later than
when customers really wanted it. These
estimates would be used to correct the
supply-distorted shipment data.
In summary, forecasting managers
should evaluate if there are any
demand signals being used that are
distorted by supply-related factors.
Their job is to provide (for example)
SOP planners with a supply-neutral
demand forecast rather than just an
unconstrained one. Failure to do so
might work in the short-term, but does
leave open the risk that a customer
might get tired of being conditioned
by supply-related factors and move on
to a competitor in the long run.
—Send Comments to: IBF@ibf.org

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SOP:
OrganicValley’s Journey
By BethWells
Beth Wells | Ms. Wells is an experienced demand manager with 10 years of experience in forecasting,
economic analysis, and related fields. She has worked at Organic Valley for over seven years in demand planning
and production departments, where her responsibilities have included forecasting, demand planning process
improvement, supply chain analysis, and SOP. Her extensive experience in agriculture and food production
has given her unique insight into the challenges and opportunities of demand planning in these industries. She
holds a Master of Science in Agricultural Economics from Kansas State University, and is a Certified Professional
Forecaster (CPF).
A
s I began to write this article,
a Mark Twain quote came to
mind. It may be an unlikely
pairing, a literary giant and the
discipline of forecasting. However, if
I have learned one thing in my early
career as a forecaster, it is to open
your mind to all the possibilities and
connections, not just the ones that
are obvious. So, as Twain once stated,
“The secret of getting ahead is getting
started.” This can be applied to life in
general, but I would like to take the
liberty of applying it to the discipline
of forecasting. This is the lens I am
choosing to describe a journey we
took at Organic Valley, resulting in a
successful demand planning software
implementation, process maturation,
and lessons learned.
Organic Valley is a farmer-owned
organic cooperative, headquartered
in scenic, southwestern Wisconsin.
In the past 25 years, Organic Valley
has grown from a small, local co
operative of a dozen members to a
global supplier of organic consumer
packaged goods, and 1,800 farmer
members strong. Incepted as a farmer
marketing cooperative, Organic Valley
is owned by the farmer members
who supply raw materials that are
produced, distributed, and, ultimately,
purchased by consumers in grocery
stores throughout the United States
and in Asia.
In the spring of 2013, Organic
Valley’s Sales Planning-Demand
Management department (a part of
the Sales organization), engaged in a
project to purchase and implement
a new demand planning software.
Our goal in implementing a new
system was to support the needs of
a collaborative, promotionally driven
forecast in a centralized planning
system. The goal of this project was
not only to implement new software,
but to mature planning processes and
forecast performance by improving:
item level accuracy when using top-
down forecast adjustments, access
to statistical analysis, short life cycle
planning, and promotional planning.
E x ecu t i v e S ummar y | Inanefforttosupporttheneedsofacollaborative,promotionallydrivenforecast,Organic
Valley designed and implemented a project to improve the company’s demand planning system and related processes. This
project resulted in a journey that led to successful software implementation, process maturation, and lessons learned. The
experience fostered growth in the business’s understanding of forecasting, and the value it provides.

We were also aiming to gain process
efficiencies including improving data
confidence, streamlining data and
system integrations, providing ex
ception management, and enhancing
data visualization. The technical
challenges we were facing in meeting
this goal included no dedicated
demand planning software, extensive
use of MSExcel for forecasting, and
building a tiered sales and operations
planning process into a functional
demand forecasting software. To
address this goal and the associated
challenges, we identified the fol
lowing project objectives: increase
process efficiency, increase forecasted
item level accuracy, increase process
flexibility and incorporate advanced
planning functionality.
To achieve our goal, address the
challenges, and fulfill our objectives,
we engaged a cross departmental team
including core team members as well as
business stakeholders. The core team
of seven included a business lead, two
subject matter experts in forecasting
and reporting, IT business intelligence,
integrationanddatabasespecialists,and
aprojectmanager.Theextendedteamof
business stakeholders included subject
matter experts throughout the supply
chain. After several demonstrations of
demand planning software, we selected
a vendor. We worked through planning,
design, and testing. We went live with
the new software in November 2013.
In Twain’s words, this was the “getting
started.” Which leads to the question,
“How have we gotten ahead?”
Primarily, we were successful in
implementing a functional demand
planning software. We were able
to increase efficiency with the im
plementation of the new software.
One metric we used to measure this
was reducing employee hours spent
on plan maintenance (outside of
forecasting). Prior to implementation
of a dedicated demand planning
software, 30 hours per week were spent
“fixing” the forecast, but not adding
value. We now spend 10 hours per
week on data maintenance, a 67%
reduction in non-value added fore
casting work. We also increased
flexibility in our data integration
processes. We are now able to pass
a portion of the intended plan
to downstream applications, which
allows us to send updated forecasts on
isolatedproductswithoutcompromising
the integrity of the aggregate demand
plan. It has improved our response time
in updating our signal to the production
line. Functionally, a dedicated demand
planning software provided us with the
ability to refine and revise the demand
plan within the software system,
eliminating the extraction of data into
MSExcel for analysis. We now do our
work in the forecasting system instead
of in MSExcel. Our extraction rate went
from 90% pre-implementation to 5%
post-implementation. This has also
improved work productivity and process
efficiency.
Additionally, our process matured
with the implementation of a dedi
cated system. We successfully de
signed a tiered planning process.
This process not only included all
elements of demand planning, but
also included our sales and operations
planning (SOP) responsibilities. It
allows us to take a deliberate and
consistent approach to forecasting,
while continuing to serve the need
of our one number SOP culture.
This tiered approach is conducive
to incorporating the art and science
of forecasting. We not only have the
ability to consider judgment and
collaborative inputs, but can build
these inputs on a statistically driven
base. We have a design that allows the
forecaster to build consensus to a one
number SOP. Figure 1 represents the
Detail Stat.
Forecast
Aggregate
Stat. Forecast
Customer
Forecast
Market
Intelligence
New
Business
Total Stat. Base
(user control on
driver 1,2 or 3)
Lost
Business
Demand
Override
Demand Plan
The
Forecaster’s
Plan
Consensus
Override
Consensus
Demand Plan
Influenced by
Sales and Marketing
Judgement
Supply and
Production Capacity
Constrained
SOP
Override
SOP Plan
Figure 1 | Process Design

S&OP in Service

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