BUSINESS
RESILIENCE
THROUGH
DEMAND
PLANNING
Finance professionals can help their
organizations weather uncertain market
conditions by overcoming demand planning
challenges.
BY BILL KOEFOED
February 2023 / STRATEGIC FINANCE / 25
T
he 2023 outlook looks stormy: Economic
downturn, continued inflation, volatile
foreign exchange rates, and a sustained
higher level of uncertainty are all on the
horizon. The ongoing pandemic, especially in China, coupled with geopolitical
conflicts and trends in deglobalization,
have struck global supply chains, bringing
instability and higher costs to many economies. Supply chain disruptions are putting long-standing
operational models at risk, from offshoring to Just-in-Time,
with potentially unforeseen consequences. This scenario
hasn’t been seen by business leaders in many years.
Amid this environment, a meaningful way to improve
financial performance is through a more effective demand
planning process. But accurately planning demand under these
volatile market conditions is tough, making it challenging to
positively impact financial results. It doesn’t have to be this
way. There are several best practices companies can follow to
improve demand planning and drive better financial results.
Developing the most accurate forecast for the future
demand of a product or service is critical to optimizing
performance and business results. Nevertheless, most
organizations struggle to accurately forecast and plan for
demand under current circumstances. And the consequences of those complexities are forcing organizations
to face higher inventory levels and costs, unsatisfied customers, and missed market opportunities. More effective
demand planning can help to unlock improved financial
performance. But organizations must first understand the
inhibitors to accurate demand planning in the current
environment. (See Table 1 for more on evaluating your
demand planning process.)
Understanding
Demand Management
Companies perform demand planning to ensure that they
have the right materials to produce the right products, in
the right quantities, delivered to the right location, at the
right time in order to meet customers’ expected service
levels. The duration of this cycle is often referred to as the
supply chain lead time (or sourcing lead time, production
lead time, or distribution lead time). Companies can’t wait
for customer orders before they start planning; they must
use demand planning to anticipate future demand and then
plan their supply chain activity accordingly.
Demand planning is part of the demand management
process that has been in use since the 1950s. In recent
years, demand planning has been gaining sophistication
as Big Data, analytics, AI, and machine learning technologies enable greater refinement in demand modeling and lower the entry barrier for more organizations.
Demand planning has a strong forecasting component
but involves much more than just statistics. When done
well, demand planning considers the inputs from various sources and experts to build the right assumptions to
be used in forecasting models. (See Figure 1 on p. 28 for
an overview of the methods involved.)
26 / STRATEGIC FINANCE / February 2023
Planning Demand
Today
Even in normal times, gaining an understanding of how
the market will react to new products and services, promotions, or price changes is difficult—and it’s much more
difficult under current market conditions. For many business leaders, figuring out price elasticity and its impact on
demand is becoming an obsessive pursuit for a simple reason: to help with decision making. For example, a premium
beverage manufacturer would need to know how sensitive
consumers are to a price increase in order to prevent them
from shifting to lower-priced drink options. But the manufacturer also needs to protect margins from surges in ingredient and energy prices.
Yet striking a balance between supply and demand, at the
right price point, with this level of variability is hard. High
forecasting error rates can cause a significant impact to a
company’s profitability. When the demand plan is inaccurate,
either excess or obsolete inventory is stockpiled, or, on the
contrary, having insufficient inventory will cause stock-outs,
resulting in negative revenue and reduced customer loyalty.
Given all of that, why is effective demand planning one
of the top priorities for business leaders looking to persevere in current market conditions? Because accurate
demand planning directly impacts and strongly correlates
with financial results. Here are some examples:
■ Profit and loss (P&L): increased revenue and market
share, enhanced margins from effective assortment planning and marketing, and reduced reactionary spending
on emergency actions (premium expedited freight, labor
overtime, etc.)
■ Balance sheet: improved inventory and working capital
efficiency; asset and resource optimization by aligning purchasing, production, and distribution plans with
demand planning
■ Cash flow: improved capital allocation to high-demand,
high-margin items; improved days inventory outstanding
(DIO) resulting in lower interest expense
Helping demand planners to tame forecast bias and suppress error levels is critical to optimizing business results.
Why, then, is demand planning so hard?
The Difficulty of
Demand Planning
Let’s examine three of the top challenges of demand
planning. The first challenge is planning demand under
high-variability conditions like those that exist today. A
simple way to understand this is by looking at the Organisation for Economic Co-operation and Development
(OECD) passenger car registration figures for the United
States for the last few years (see Figure 2 on p. 29). For
2020—the year the COVID-19 pandemic emerged—the chart
shows a drastic decline from February to April 2020 followed by a rapid recovery in the subsequent months. If this
decline isn’t considered in the model as an outlier—and thus
TABLE 1: CHECKS TO EVALUATE YOUR DEMAND PLANNING PROCESS
Check
Rationale
Demand management is an established ongoing
process in which demand planning kick-starts to
support long-range and annual plans, and it supports
demand generation activities continuously.
Demand management consists of demand planning,
demand sensing, demand shaping, and demand
prioritization.
Demand management is a key process in the organization
in that it’s actively supported by top (C-suite) leadership.
Determining demand is key for an organization’s business
performance; it will determine the revenue potential and
can help to minimize cost and protect cash.
Demand planning is communicated and leveraged into
wider planning processes such as financial planning and
analysis, sales and operations planning, and/or integrated
business planning.
Communicating demand is key to the organization’s
success. Communication and collaboration among the
different demand management stakeholders is essential
to ensure the company meets its objectives.
Core stakeholder functions of demand planning should
include but not be limited to product development, marketing and sales, supply chain, finance planning and control,
workforce, and IT staff.
All these functions should have a say in the demand plan
and should leverage the plan for their responsibilities. In
some industries, customers, suppliers, and partners can
also participate in demand planning.
Collaboration among stakeholders is effective and aims at
reaching the consensus demand plan.
The consensus demand plan is one that has been agreed to
by all stakeholders and has been reviewed against
drivers and constraints that impact the business.
Demand planners spend most of their time in analysis and
value-add activity vs. gathering data.
Normally, demand planners are wrestling with disparate
sources. They want to minimize inaccurate, incomplete, and
otherwise low-quality inputs and biased assumptions.
Demand plan is expressed in ways that are understood by
both finance and operational organizations.
Each stakeholder views the same information through a
different lens. At the core volume (unit), measures must be
expressed in value (currency) measures to ensure an effective demand planning process.
Demand planning runs at a frequency (e.g., monthly,
weekly, or daily) to keep up with changing market conditions and supports the supply chain and delivery process.
Speed matters. The time elapse between a disruptive event
and a decision taken and executed is key to capture or keep
business value.
excluded—quantitative methods would yield less accurate
results in the annual forecasts for future periods.
Therefore, to improve prediction quality, businesses must
continually review and adapt their demand planning to constantly changing market conditions. The challenge is that
there are many statistical models available and a broad range
of quantitative and qualitative methods that need to be com-
bined and used depending on the circumstances and specific
characteristics of what’s being analyzed. The most complex
statistical method doesn’t always provide superior results.
Rather, the quality of the results ultimately depends on the
frequency with which data is being ingested, the ability to
effectively use various internal and external business drivers
to enrich the training of these models, the product character-
Assuming that demand just “happens” spontaneously
and can’t be managed is erroneous: An organization
must follow up a demand plan by sensing what the actual
demand is, trigger actions (marketing campaigns, promotions, new product launches, etc.) to shape the demand,
and have a strong governance to prioritize demands when
resources are limited.
February 2023 / STRATEGIC FINANCE / 27
FIGURE 1: DEMAND PLANNING BASICS
Demand planning is part of the demand management process and is a collaborative and ongoing effort
intended to determine the level of demand that can be satisfied with the available resources in each
period. Demand planning starts with the forecast, a statistical analysis enriched with inputs from
marketing and sales, product development, and the business strategy.
Demand planning may use both quantitative and qualitative methods.
Quantitative
methods
Time series
models
Qualitative
methods
Causal
models
Naïve
Regression
Delphi
Smoothing
Econometric
Decision trees
Exponential
smoothing
Neural network
Market research
Holt-Winters
ARIMA
Focus groups
Executive views
Subject-matter
expert views
No model is inherently better than another—in other words, different models may work for different
industries, product types, or market conditions. A good demand planner juggles various inputs and
models to improve outputs and accuracy.
istics (e.g., an upgrade vs. a totally new product), and more.
With higher market volatility, the forecasting and planning
error rate grows and trust in the demand planner’s results
diminishes.
Second, demand planning is hindered by organizational
disarray. Demand planning is a key step in sales and operations
planning and integrated business planning. These processes are
underpinned by cross-functional collaboration between leadership, finance, sales, marketing, supply chain, operations, and
other functions. Yet in most organizations, these business units
operate in isolation from one another and occasionally act on
contradictory assumptions and conflicting targets. There needs
to be further unification of these functions.
28 / STRATEGIC FINANCE / February 2023
Even if demand planners generate solid forecasts, those
forecasts are neither followed nor trusted. Why? Because
the demand forecasts aren’t supporting the planning
assumptions of every business unit. And that results in each
function using its own self-generated forecast, creating different views of a shared reality across functions.
Another organizational concern is the placement of
demand planning within the overall organization, both
functionally and hierarchically. There’s no straightforward
answer to the question of where demand planning should
be placed, and the options are diverse. Logistics, marketing,
sales, finance, strategic planning, supply chain operations,
or an independent team—any of these functions can own
FIGURE 2: OECD PASSENGER CAR REGISTRATION IN THE U.S.
(Percentage change 2015 = 100)
100
80
60
40
20
0
J
F
M
A
2018
M
J
2019
J
2020
S
A
2021
O
N
D
2022
Source: “Passenger car registrations,” OECD Data, 2022, bit.ly/3YBPCkz.
the demand planning mission. Not to mention, demand
planning that’s untrustworthy or excluded from the C-level
agenda also tends to get pushed down in the hierarchy.
Finally, many demand planners are working with obsolete technology. Using legacy technology may sound paradoxical in the Digital Age when technology is ubiquitous and
data is abundant, but it happens. In today’s world, a wide
variety of planning tools are available alongside abundant
expertise and insights to input into the demand planning
activity. While these factors should raise the accuracy of
demand planning, the results are quite the opposite many
times. Why is that? Because demand planners often need to
handle a mix of different data sources and tools. This mix
includes spreadsheets, paper-based reports, and flat files
that must be uploaded manually to the planning tools.
Sometimes there’s a direct integration to a source system,
but data management and transformation may be required
when preparing data to feed into statistical models—and that
takes time. If the planning and modeling tools aren’t suited
to blend all this data intelligently, more manual manipulation will be required by the demand planner or technical
expert. That added manipulation will then create a bottleneck and create more risk for potential errors.
Consolidating all these disparate inputs is what consumes most of the time and effort of demand planners,
leaving little time for modeling, analyzing, and interpreting
the results. Therefore, demand planners are forced to make
a suboptimal compromise: to discard some input sources
and simplify the analysis or miss critical deadlines. If the
demand planning process is overly cumbersome or timeconsuming, the probability of that forecast being appropriately refreshed is unlikely.
When businesses don’t refresh their technology to capitalize on the rich and abundant data sources available, demand
plans are delayed, erratic, incomplete, or lacking the required
level of detail. And whatever the effect, it diminishes the
value added to the overall business planning process.
Modern, cloud-based demand planning technologies must
therefore be leveraged to handle the growing number of internal and external business drivers, factors, and data volumes.
Now is the time for businesses to elevate demand planning as
a critical activity to conquer complexity, uncertainty, and risk.
A Better Way
Organizational and technological issues in demand planning aren’t new. The current market conditions, however,
highlight the impact these issues are having on business
performance. Understanding the issues hindering demand
planning and the importance of making the process more
February 2023 / STRATEGIC FINANCE / 29
CONSIDERATIONS THAT ENABLE
BUSINESS TRANSFORMATION JOURNEYS
leadership must be engaged in demand planning and consume its outputs. The key objective for
1 Top
demand planning is to depict what the demand is going to be for the products and services, and this
should be a key driver for strategic goal setting. So it’s essential that the C-level is fully committed to
the process.
must collaborate, and team collaboration starts with a clearly communicated vision
2 Stakeholders
and setting the expectations from the start, defining KPIs that are aligned to strategic objectives and
financial attainment.
needs to support demand management with visibility on how a demand plan (or change in
3 Finance
the plan) affects profit and loss, the balance sheet, and more. This is the best way the finance organization can provide value to the demand management process and ensure every action taken by
marketing, sales, and supply chain operations supports the business goals and financial objectives.
or an independent function should own demand management. To avoid biased forecasts
4 Marketing
and plans, demand planning should be done by an independent team or be placed within marketing.
If, for example, demand planning is owned by supply chain, it may be biased by the resources and
supply constraints and won’t reflect the true demand picture in the market. Notwithstanding, demand
management must be closely connected to key commercial and supply chain activities, such as
promotions, inventory, purchasing, and more.
management processes are to be challenged by removing deviations, reducing variations,
5 Demand
taking away steps and complexity, redefining measurements, and ultimately automating tasks. When
a single view of the plan is instilled and teams are collaborating, the need for data checks, file, and
data reconciliation is eliminated. When a single solution is implemented, the data verification and
certification can be signed off on easily. All this is net contribution to simplification, and it unleashes
productivity that can be geared up to higher-value activities for the demand planners.
in technology to drive higher demand planning accuracy and support faster decision making.
6 Invest
Each product family reacts differently to demand variability, so equip demand planners with the best
tools. Demand planning technology today leverages machine learning and multiple data sources to
increase forecasting accuracy, accelerate the run frequency, make simulations, and show the same
information in ways that satisfy the needs of all stakeholders.
effective is critical for businesses seeking to optimize financial performance.
Fortunately, as organizations deal with increasing turmoil, technology is making steady progress. Big Data analytics, AI, and machine learning offer good examples, and
these technologies are now making an impact in demand
planning and forecasting.
AI and machine learning can help drive more effective
demand forecasting by ingesting higher volumes of varied
data, determining the relationships between drivers (features and events) and their impact on the forecast, and executing thousands of forecasting models at a fraction of the
time required with prior methods. The time saved frees up
30 / STRATEGIC FINANCE / February 2023
the demand planner, allowing for more trials with different
forecasting methods in a shorter time frame. The benefit?
AI solutions can execute far more simulations and recommend to the planner which statistical method drives the
most accurate prediction for a given product and business
context.
What, then, can organizations do to improve the effectiveness of demand planning? A preliminary step is to
identify the root causes of demand planning not yielding
accurate results. Organizations can then rank the causes by
severity and the effort required to resolve them. Through
this process, organizations may gain valuable insights for
building the business case for change.
A few important considerations will allow businesses
to shape their own transformation journeys and serve as
guardrails along the way once an organization is determined to revamp demand planning.
■ Unify all planning activity and identify demand
planning as a pivotal piece. Planning doesn’t work
well when done in silos. Why? Because doing so creates
redundancy, ring-fences the impact of the plans, and
blurs the vision. Unifying planning across business lines
and functions instead offers a single view of the business
that removes organizational bias and provides agility in
return. In an ideal world, demand forecasting should be
one of the first steps in overall business planning.
■ Seek sponsorship from finance leadership. Under a
unified approach to planning, the CFO and the finance
organization can better support demand prediction and
align it with other planning activities. Financial planning
sets the organizational targets, but how realistic is a revenue target when the demand forecast is falling short?
Demand planning acts as a check-and-balance step
against the financial targets by providing business context to the financial ambitions of an organization. Therefore, the CFO should have a vested interest in supporting
demand planning.
■ Align demand planning key performance indicators
(KPIs) with financial objectives. Higher demand forecasting accuracy is essential to helping optimize replenishment and safety stocks, but how does that affect
financial performance? What if demand scenario modeling could be checked right away against the P&L, balance sheet, income statement, and cash flow statement?
Providing that visibility to the business would accelerate
the assessment and selection of the operational planning
scenario that optimizes both business and financial performance. The demand forecast must be expressed both
in value (dollars) and volume (number of units) to serve
as a connector between finance and operations.
■ Make use of the best data sources available. Modern
demand planning technology allows planners to plug in
different data types with high frequency—structured and
unstructured; internal and external; financial, operational,
and transactional—to inform the forecasts. Adding more
sources helps incorporate market trends, seasonality, and
cyclicity where applicable. But there’s a caveat: Sometimes access to new data sets is difficult or restricted. For
example, having access to point-of-sale data is key for the
consumer goods industry. Yet most of that data is proprietary to retailers and wholesalers, who may not be eager
to share it. In this case, using weather data to forecast
demand of certain consumer products can help improve
demand forecast accuracy.
■ Incorporate more demand forecast modeling capabilities. A one-model-fits-all type of system for demand
forecasting has never been a good approach—and certainly
not when the demand plan must cover a variety of product types and categories, disparate markets, and demand
segments. Thus, a good demand planning practice is to
constantly look for alternative models to plan for the same
product and expand the toolbox with techniques and technologies that broaden the spectrum of modeling methods.
A good example is a supermarket chain changing its mix of
dairy products. Rather than relying on historical forecasting models, the chain should test other models, especially
when new products are placed on the shelves, new data
sets become available, new promotions are run, or factors
that influence consumer behaviors change. That doesn’t
mean statistical models have an expiration date. Rather,
the more models tested on a specific demand set (product,
price, data, etc.), the better the chances that errors can be
contained or reduced as market conditions change.
■ One version of the numbers isn’t debatable. Having
one version of numbers is hard, no doubt, because organizations are broken down into multiple functions and
business lines. Accordingly, the systems at hand can only
produce limited and incomplete plans. But it shouldn’t
be this way. Software solutions exist today that can unify
all the planning and forecasting activities within one
platform and with one data model. Utilizing a data-first
approach to planning should be the first step to consider
for an organization looking to reach consensus demand
agreements and reduce bias.
Where to Start
Improving demand planning is a tactical starting point for
leaders who are interested in unifying financial and operational planning, and a specific way to deliver a positive impact
on their business. Instead of marginalizing such planning
because current market dynamics may increase errors, organizations should view demand planning as a key mechanism
for optimizing business performance. Getting leadership sponsorship is critical. And pursuing a data-driven approach that
can produce demand planning models connected to financial
objectives can considerably improve results.
A data-first approach provides a unique and unquestionable view of the numbers. In turn, that view will serve
as the glue to connect and keep all functions and business
lines rowing in the same direction. And the benefit comes
in the form of saving time in costly organizational design
and culture changes that sometimes don’t last very long.
Business and finance leaders who want to take advantage
of new opportunities in uncertain market conditions should
consider effective demand planning as a critical capability
to improve financial performance. A simple question helps
clarify the benefits: If demand planning errors are reduced,
how much would that imply in terms of the following?
■ $M savings from lower inventory or obsolete stock
write-offs.
■ $M cost avoidance by reducing last-minute decisions in
purchasing, freight, and manufacturing.
■ % market share gained due to timely and complete
product availability.
The way forward to realizing those benefits starts
with CFOs and finance teams partnering with the demand
planning organization. SF
Bill Koefoed is the CFO of OneStream Software. He can be reached at
bkoefoed@onestreamsoftware.com.
February 2023 / STRATEGIC FINANCE / 31
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