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Have you applied forecasts to allocate resources in your organization?

Did you find it difficult to convince all stakeholders to leverage these forecasts as-is?

How did the forecasts help you in your day-to-day planning?

Statistical Forecasts have replaced the manual estimations in many organizations.

Experts have realized the benefits of driving portfolio strategies as well as business

operations through data-driven forecasts. One common opportunity area is to

effectively use these statistical forecasts across all functions without any

manipulations.

This reality is true for about 80% of organizations (which utilize forecasting process)

which leads to inconsistent demand planning thereby reducing the utilization of the

forecasts for operations planning on a day-to-day basis.

This paper will cover the benefits of managing demand through statistical forecast,

challenges in maximizing these benefits, and an alternative approach through a CPG

industry live example.

Introduction

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Retail out-of-stock levels increase significantly during promotion and new product

introductions

• 7% during non-promotion period

• 14% during promotions

• Higher product markdown(Pricing)

More supply returns by manufacturing/distribution nodes. These result in negative

impact on:

• Consumer uptake

• Manufacturer-retailer relationships

Business challenges in managing demand

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Accurate demand forecasts result in more efficient business value chain

Manufacturer Supply Chain

Retailer

Cash Flow

15% less inventory 17% higher OTIF delivery performance

Reduces stock – outs by 5 -15 %

35% shorter cash-to-cash cycle time

Yet, we do not see a 100% implementation of the demand forecasting where the values

generated are not tweaked. Here is a glimpse of the common adoption among

organizations:

• 87% of companies prefer statistical approach to generate the initial forecast which

is then subjected to human judgment

• Out of the 87% statistical approach users, 92% override the forecast

At a global CPG level, we did an analysis to measure the accuracy for market level

estimations

Accuracy estimates:

Process Step Accuracy vs. Naïve vs. Statistical

Naïve Forecast 65% - -

Statistical Forecast 75% 10% -

Analyst Override 70% 5% -5%

The difference between override and forecast accuracy is even higher for more granular

levels. Hence, a company with a large product portfolio will benefit the most out of statistical

forecasts.

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Common challenges in consistent adoption of data-driven forecasts

Conflict in goals - Demand planning targets are set based on forecasts at global,

regional and country levels across all product lines. While the top management would

like to stretch the targets and push the teams to achieve higher topline growth, the

country and regional teams are more conservative with respect to targets and

forecasted growth. This is a common scenario in other domains as well. Hence, teams

tend to override forecasts based on their assumptions and expected outcomes.

Ease of usage - Complexity of forecasting methods make it difficult for business

users to accept the outcomes, since they do not fully understand the conclusion based

on statistical algorithms. These business users are more comfortable with gut feel and

simple extrapolation methods, which can easily be reapplied to different scenarios. The

result may not be technically correct, but if it falls within their tolerable limits, teams

generally pursue their own methods.

Counter-intuitive forecast results - Consider a scenario where macroeconomic

parameters such as GDP, inflation, etc. are suggesting a declining economy while the

forecast shows saturated growth or 1-2% decline across the market. This could be a

reality in current market trends yet it is counter-intuitive. There are some business

assumptions which data-driven forecasts may have overlooked due to lack of historical

data to support its outcome.

Uniqueness of business - For a large portfolio of products/services, there may be

products where seasonal patterns are not so clear, purchase cycles have huge

variations, are not affected by macro-economic patterns, and no clear parameters to

explain sales. In such cases, data-driven forecast will not have high accuracy.

Poor data quality - Forecast results hold more relevance in regions where data

volume and quality are enough to show the right trend. However, in reality, we struggle

with poor data quality, less market coverage, and unexplained variations in the trend.

Hence, business teams in those regions are skeptical about forecasts and prefer their

own business assumptions.

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Effect of special events - Any recent events like oil crisis, earthquake, or other

disasters may not have been captured in these analyses, since the data is typically 1-2

months older. Even if it includes these data points, the time period of the event will be

minimal to hold any relevance in forecasts. However, the impact in reality can be really

significant.

Real time action – Usually, it takes a long time to complete the entire forecasting

model and share the final result which reduces the urgency of call to action and real

time decision-making. By the time the entire process is aligned within all the teams to

create an SOP, 3-6 months have passed with respect to historical data. Hence, the

results are no longer actionable.

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How to institutionalize data-driven decisions: Generate Centrally, Collaborate Locally

We can increase adoption through institutionalization of forecast process across the

organization. This ensures that data-driven forecasts are strengthened with necessary

business inputs and implemented across all organizational functions.

Proposed change:

Integrate forecasting process with the business users’ inputs regarding any major

market change and internal strategic initiatives to develop business-centric data-driven

mass forecasting system. This will make the forecast more comprehensive and

integrated for the stakeholders to appreciate and can be referenced as a single version

of truth for all product portfolios throughout the organization across geographies. This

process is illustrated below:

Define Scope of solution

Data preparation

Set up Forecast platform and algorithm

Visualize the trends and highlight exceptions

Review with relevant business stakeholders

Key stakeholders:

• Top leadership (Annual strategy)

• Finance Team (budgeting and allocation)

• Sales and Marketing (Setting sales targets, plan marketing spends)

• Brand and category managers ( brand strategy, launch plans)

Repeat the entire exercise every quarter to review, track and update the results. This

cycle takes 3 weeks to complete.

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Local collaboration: business reviews

This is the process where different business units review the forecasts based on

history, recent market behavior, and current political and financial status in a country or

region. After the forecasts are reviewed by the business units, they are finally approved

by regional heads.

Stages of Alignment:

• Review and recommend adjustment in forecast

wherever needed based on local market dynamics

Regional strategy Teams

• Review the recommendation made by Regional

Teams.

• Provide feedback based on product specific trends

and expected changes

Portfolio strategy teams

• Review product teams’ and Regional strategy teams‘

inputs

• Engage in discussions with both the teams to arrive at

a consensus.

Functional Teams (sales and finance)

• Make the final decision on the numbers. Business leadership

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Business inputs needed corresponding to variations in data and volume

To ensure that the forecasts with varying data quality are applied across all regions,

different treatments across the market with respect to quantity and variability of the data

are used.

Low priority items Accuracy

low

Business drives final decision

With critical Inputs from sales

teams, forecasts give good

results

Use extrapolation with

growth parameters and sales

inputs

Statistical Forecasts work

really well. Need minimal

intervention.

Hig

h

Lo

w

Low High

Va

ria

bili

ty

Speed of decision-making - Mass forecasting system enables the forecast process

to be published within 3 weeks, along with the whole business review.

Collaboration with stakeholders - Regional teams share their critical inputs on

special events and local estimates on demand growth trends. Final numbers are achieved

based on added rationales shared by all stakeholders.

Increased business relevance - With the help of quarterly revisions, the forecasts

remain relevant to current changes in the market trends and keep aligned with the recent

developments and gaps against the actual numbers, if any. The increase in business

relevance is also attributed to the right markets and product segments/attributes in order

to understand the impact better.

Consistent impact on strategy and operations - Once aligned and published, the

results will help plan the weekly and monthly demand targets for production and logistics

planning. These numbers may also be referenced for budget planning for the entire

product portfolio and allows for a better view of the opportunity landscape and the

whitespace countries.

Quantity

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References

• What should you measure and when you should not even try to forecast- Accenture

• Infosys Whitepaper- Effectively Managing Demand Variability in CPG Industry

Mike Gilliland’s Article, Informs Analytics, August 2011

• Forecastpro Webinar: Integrating Statistical forecasts with other Inputs to create a

demand plan for S&OP

• Research paper: Demand management: Enabling Sell Side Collaboration to Improve

Sales Revenue

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Author

Pragya has 7 years of experience in CPG business and has worked across multiple

strategic solutions related to product portfolio management, pricing, market estimation,

marketing and business process re-engineering. She has worked on forecasting solution

for fortune 500 clients and has helped them increase the accuracy of forecasts and reduce

the overall process redundancies.

Pragya Gaur,

Senior Consultant,

Fractal Analytics

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About Fractal Analytics

Fractal Analytics is a global analytics firm that serves Fortune 500 companies to gain

a competitive advantage by providing them a deep understanding of consumers and

tools to improve business efficiency. Producing accelerated analytics that generate

data driven decisions, Fractal Analytics delivers insight, innovation and impact

through predictive analytics and visual story-telling.

Fractal Analytics was founded in 2000 and has 800 people in 13 offices around the

world serving clients in over 100 countries.

The company has earned recognition by industry analysts and has been named one

of the top five “Cool Vendors in Analytics” by research advisor Gartner. Fractal

Analytics has also been recognized for its rapid growth, being ranked on the

exclusive Inc. 5000 list for the past three years and also being named among the

USPAACC’s Fast 50 Asian-American owned businesses for the past two years.

Learn more at www.fractalanalytics.com

For more information, contact us at:

+1 650 378 1284

info@fractalanalytics.com

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