Supply Chain Planning Demand Planning

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Copyright (c) 2008 Accenture All rights reserved You may only use and print one copy of this document for private study in connection with your personal, non-commercial use of a Supply Chain Academy course validly licensed from Accenture This document, may not be

photocopied, distributed, or otherwise duplicated, repackaged or modified in any way

Note: interactive elements such as activities, quizzes and assessment tests are not available in printed form

SUPPLY CHAIN PLANNING: DEMAND

PLANNING

Introduction

Why is Demand Planning Important?

The goal of demand planning (DP) is to forecast what products customers will want, how

many of those products they will want, and when they will expect to have them

The primary business issues addressed by demand planning include:

• Consolidating multiple demand plans into a single plan usable by the entire

organization

• Achieving more stable end-to-end planning and improved visibility of demand

• Eliminating "seat of the pants" decision making

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Why is Demand Planning Important?

Demand planning is one component of the supply chain planning (SCP) process SCP is

an integrated process that allows companies to plan and integrate the supply chain

functions of procurement, manufacturing, and fulfillment

Demand, supply, production, and fulfillment planning operate as interdependent SCP

functions The goal is to integrate these processes so that all the plans are synchronized

with one another Plans generated during one process are used by one or more of the

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Objectives

After completing this module, you should be able to:

• Describe the purpose, objectives, and benefits of demand planning

• Describe the demand planning process and identify the stakeholders

• Describe key concepts related to demand planning activities, including planning

horizons, product hierarchy, and forecast allocation techniques

• Identify key inputs and understand other considerations that impact the demand

forecast

• Define demand forecasting techniques

• Describe the measurements and metrics for demand planning

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Introduction to Demand Planning

Overview

Demand planning helps a company develop their best estimate of:

• What customers will want

• How much they will want

• When they will want it

The answers to these questions serve as the foundation of the demand plan

The goal of the demand planning process should be to create a single demand plan

across the organization Often, organizational units within a company will have different

objectives and viewpoints, and consequently, develop separate demand plans For

instance, Sales may have an item-by-item forecast by key customer, while Marketing

may have a separate plan that reflects the planned promotions for the year

A good corporate demand plan is one that builds on the inputs from a variety of sources

to create a single consensus demand plan This plan should reflect the corporate vision

that has been accepted and is being used by the entire organization

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Demand Planning Stakeholders

The forecasting process has many different stakeholders, often with competing

objectives; organizations must consider input from all of the stakeholders The different

organizational units (Sales, Marketing, Finance, and Manufacturing) generate forecasts

and then work together with the demand planner during the Sales & Operations Planning process to generate a one number consensus forecast that is used to drive business

operations as follows:

Sales Function

Responsible for ensuring that sales quotas and corporate sales objectives are met

Typically, sales representatives generate time-phased forecasts for key customers or key product groupings Sometimes, they may also project the dollar volume of business they

expect to conduct with each customer They then monitor the performance of actual sales versus forecasted sales to determine their variance from forecast

Marketing Function

Responsible for developing promotion plans and advertisement campaigns that maximize product revenue streams Marketing uses historical data and competitive information to

determine the influence of promotion plans and ad campaigns on customer demand

Marketing is also responsible for forecasting the demand of new product introductions

Because no history is available for the new products, they forecast the demand for such

products by using historical patterns of similar products

Finance Function

Monitors forecasted sales to ensure that the organization will generate sufficient cash

flow to meet corporate financial obligations Finance also uses demand projections for

budgeting purposes, and depends on forecast accuracy to effectively manage operating

expenses and determine whether capital investments are appropriate

Manufacturing Function

Uses the demand plan to determine if enough resources are available to fulfill projected

demand If they do not have the resources, the difference between the manufacturing

capability and forecasted demand must be resolved

Demand Planner

The demand planner is part of the SCP organization, responsible for ensuring the

accuracy of the demand forecast and that the organization reaches a one number

consensus forecast

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Reaching Consensus

A single demand forecast is needed to effectively drive the operations and planning for a

company The adjustments required to reach this consensus plan are identified during

the Sales and Operations Planning process (S&OP) The goal of the S&OP process is to

coordinate supply and demand to develop a single plan for the company This plan will be used to maximize customer fill rates at the minimum asset investment

As we have seen, companies have several demand forecasts from different stakeholders with competing objectives S&OP can assist in balancing these different plans and

objectives This is accomplished with a cross-functional team comprised of

representatives from Sales, Marketing, Finance, and Manufacturing, as well as planners

from the SCP function

Although S&OP is a broad concept, we will limit our discussion to the role of demand

planning within S&OP that strives to reach consensus with a one number forecast

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Scenario: Reaching Consensus

Consider the following example of a company with two customers that manufactures just

one product that sells for $5/unit In the table below, Sales and Finance have both

forecasted the sales of the product to each customer for the next three months

Finance has forecasted total sales of $8,500 based on corporate cash flow requirements, whereas the total dollar volume for Sales (who forecasted unit sales) is only $7,950

based on what they feel they can sell to their customers This creates a difference of

$550 that must be resolved

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Scenario: Reaching Consensus - continued

During an S&OP meeting, the team decides that there are many possibilities for resolving this issue, three of which are:

1 Finance revises its forecast to $7,950

The S&OP team agrees that all three solutions are viable However, the third option

exceeds the revenue requirements from finance and should be more thoroughly

evaluated Using historical sales data and statistical models, Marketing determines that

such promotions usually result in an increase (lift) of demand by 20 percent for each

customer The sales forecast is then revised as follows:

Hence, the sales forecast matches the finance forecast (the $10 difference is within

tolerance limits), and the one number consensus demand forecast is reached

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Activities of a Demand Planner

Once a consensus demand forecast has been reached and communicated to the rest of

the organization, the demand planner must focus on a number of other crucial activities,

such as the ability to:

• Manage by exception

• Review ABC classification

• Capture metrics to continuously improve forecast

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Manage by Exception

Depending on the industry, the total number of SKUs a company plans for could range

from a few hundred to several thousand From a workflow efficiency perspective,

therefore, companies strive to develop a systematic method of planning "high priority"

SKUs

Demand planners commonly use exception-based management to organize a large

number of SKUs Exceptions are generally defined as high volume SKUs that

consistently have high forecast error values By labeling these SKUs as "high priority,"

demand planners are able to manage the SKUs with highest impact on the business

before the other lower priority titles

First, a demand planner will establish how much the actual demand may differ from the

forecasted demand for each SKU This is known as the acceptable deviation If the

actual demand of the SKU is outside the established acceptable deviations, an exception

is generated for the demand planner to resolve The demand planner will then resolve the exception by one of the following three approaches:

1 Reallocating the demand to other SKUs (allocation is discussed later in this

module)

2 Revising the forecast for the SKU, in conjunction with the cross-functional S&OP

team, and aggregating the forecast up the product hierarchy (product hierarchies are discussed later in this module)

3 Doing nothing because there are some exceptions that the demand planner will

choose to ignore and not attempt to resolve

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Review ABC Classification

Another commonly used method of product analysis is known as ABC classification

Using this analysis, companies identify a small percentage of items that account for a

large percentage of the dollar value of annual sales (Class A items) Studies have

repeatedly shown that in most companies, five to 20 percent of all items account for 55 to

65 percent of sales (Class A); 20 to 30 percent of all items account for 20 to 40 percent of

sales (Class B); and 50 to 75 percent of all items account for only five to 25 percent of

sales (Class C)

Beyond demand planning, ABC classification is used in conjunction with forecast

accuracy metrics and other supply planning parameters to set the safety stock levels for

each SKU Since the safety stock is used for ensuring a certain service level to the

customers, it is important to ensure that the ABC classification is accurate and current

Hence, the demand planner is responsible for periodically reviewing the classification of

items, and initiating a reclassification effort if needed

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Capture Metrics to Continuously Improve Forecast

Performance monitoring is a fundamental and necessary part of achieving high marks in

forecast accuracy In many companies, forecast performance measures are not well

defined, which leads to a lack of motivation to improve forecasting In other companies,

the greater challenge is to consistently measure, track, and report forecast accuracy

metrics by relevant product families

Since many groups (Sales, Operations, Finance) are contributing to the final consensus

forecast, it is very important to capture the various adjustments made to a baseline

forecast as it develops over time Monitoring these inputs as independent contributions

allows for more valuable insight into areas for process improvements, and sometimes to

incentive-based rewards for those groups committed to forecasting accuracy

improvements

your personal, non-commercial use of a Supply Chain Academy course validly licensed from Accenture This document, may not be

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Topic Summary

Demand planning helps a company develop their best estimate of what product

customers will want, how much they will want, and when they will want it Different

organizational units (Sales, Marketing, Finance, and Manufacturing) will have different

objectives and develop separate demand plans A good corporate demand plan is one

that, during the sales and operations planning process, incorporates inputs from the

different sources to create a consensus demand plan The demand planner then

manages the plan by reviewing exceptions, and captures metrics to help continuously

improve the forecast

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Key Concepts in Demand Planning

Overview

Some of the key questions companies seek to answer about the process of forecasting

customer demand include:

• How often is the forecast updated?

• In what time buckets is the demand forecast—week, month, quarter?

• What is the right planning structure to support a corporate demand planning

process?

• What is the appropriate level of detail to develop a forecast?

• How do we aggregate or allocate the demand forecast throughout the planning

structure?

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Key Concepts - Demand Planning Horizons

A company should update the demand forecast for its products based on the type of

demand forecast that is generated, such as:

• Strategic Forecast - Used for strategic purposes, this forecast usually spans

several years Most companies will update their long-term strategic forecast twice

a year or quarterly at most

• Tactical Forecast - Used to understand the demand for the company's products

in the near future, the tactical forecast is generally updated every month as part

of sales and operations planning For products with long lead-times, the forecast generated during this horizon is critical to ensure that these special products are available for customers at the appropriate times

• Operational Forecast - Used to drive the business operations and to understand

immediate and near future demand for the company's products, the operational forecast is usually updated every week to reflect current market conditions

Companies who use advanced technology in an environment where decision is key can update the operational forecast more frequently (e.g., every shift or every few hours)

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Key Concepts - Forecast Bucket Granularity

Companies determine whether the demand forecast should predict the demand for each

day, week, month, or quarter This is referred to as forecast bucket granularity Buckets

are differentiated as follows:

• Quarterly Buckets - Equates to four buckets per year of data The forecast

number is a quarterly sales figure Generally this is most useful for forecasting long-term demand

• Monthly Buckets - Equates to 12 buckets, one for each month of the year

Monthly buckets are used for forecasting demand on a monthly basis, and the forecast is generally used for providing visibility into the upcoming near future demand

• Weekly Buckets - Equates to 52 buckets, one for each week of the year Weekly

buckets are used for providing visibility into the near-term and the immediate future demand

Most companies forecast demand for one year at a time, using smaller buckets for the

near-term quarter, and larger buckets to represent demand that is three or four quarters

in the future This ensures that they have explicit visibility into immediate future demand,

and aggregated information regarding longer-term demand

A soda can producer, for example, will forecast the next quarter's demand in 12 weekly

buckets, the following quarter's demand in three monthly buckets, and all future demand

in quarterly buckets

If a company is forecasting for strategic reasons, they may only use quarterly buckets

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Key Concepts - Planning Structure

As a demand planner moves through the various planning horizons over time, and closer

to the expected customer delivery date, they are challenged to provide more accurate

forecasts at lower levels of detail In addition, many companies view their data in a variety

of different ways; this capability is also referred to as a multi-dimensional view of data

Companies organize their data in such a way to have maximum flexibility to view the data

in different dimensions, e.g., total sales by product family, or total sales by sales regions

They may also use this structure to have the flexibility to aggregate or allocate forecasts

in different ways To accomplish this, companies utilize product and geographical

hierarchies

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Product Hierarchy

A manufacturer of women's clothing in the apparel industry may define their product

hierarchy in the following way:

• Product Family - Trousers

A product hierarchy can be several layers deep, and it will usually vary from company to

company and from product to product The lowest layer of a product hierarchy is usually

a SKU Thus, a blue business trouser would be considered a SKU

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Geographical Hierarchy

Companies also commonly develop a geographical hierarchy, typically used to segment

their customer base A sample geographical hierarchy might be:

• Company - Clothing Manufacturer

• Customer - Store 1, Store 2

The figure depicts a graphic representation of a geographic hierarchy

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Key Concepts - Methods of Forecast Allocation

Since manually entering a forecast for each SKU is cumbersome, companies strive to

find the optimal level of detail at which to generate forecast If using statistical forecasting

techniques, forecast generation at higher levels of detail reduces model error Once

aggregate forecasts are generated, demand planners use the product and geography

hierarchies, along with "allocation strategy," to create forecasts at lower levels of detail

A simplified version of the women's trousers example follows:

• Product Family - Trousers

• Product Category - Casual, Business

• Color - Blue, Black

A company could forecast the sales at any of the levels above and would need to allocate

or aggregate the forecast (depending on the level at which they forecast sales) There

are essentially three methods of allocation and aggregation:

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The process of forecasting at a lower level and then aggregating the forecast to a higher

level is known as bottom-up For the trouser example, a blue casual trouser would be one SKU

The company could choose to forecast at the SKU level In that case, they may need to

aggregate their sales into the different categories and/or family because the product

category manager may be more interested in managing the forecast of each product

category The product manager, on the other hand, may be more interested in managing

the total sales of all trousers

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The company could choose to forecast at the product category level, i.e., for each week,

they forecast both the number of business trousers and casual trousers they can sell

For manufacturing purposes, they then have to allocate the forecast to each color Most

companies use percentages to allocate forecast For example, the company may have

determined (from historical data) that of the business trousers they sell, 60 percent are

black and 40 percent are blue; of the casual trousers, 60 percent are blue and 40 percent black

Similarly, the forecast must be aggregated up to the product family level

This is known as middle-out

Middle-Out Forecast

The shaded forecast represents the product category level The forecast is then allocated

to each SKU, and aggregated up to the product family level

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The company could also forecast its sales at the product family level, i.e., they forecast

the total number of trousers that will be sold during each week They then use

percentages to allocate the forecast to each product category (e.g., 30 percent business

and 70 percent casual) and then allocate to each SKU This is known as top-down

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Topic Summary

Several concepts are important during demand planning to ensure that the company

generates a good demand plan These are:

• Demand Planning Horizons - the frequency at which a company updates the

demand plan for its products

• Forecast Bucket Granularity - whether the demand forecast predicts demand

for each day, week, month, or quarter

• Level of Forecast - the level at which product and geographical hierarchy

forecasts are generated

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Inputs, Techniques and Considerations

Overview

Organizations must consider a number of factors to arrive at an accurate demand plan

The figure shown illustrates the key inputs and outputs for demand planning

It is also important for companies to consider appropriate demand forecasting

techniques Common quantitative approaches include exponential smoothing and

moving averages

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Key Inputs - Historical Sales Data

Companies usually base a customer demand forecast on sales history, using one or

more of the following sources:

Shipments

Many companies do not have access to actual customer sales data, but they do

accurately capture customer shipments Since it is the most prevalent data available, it is often used as a starting point for companies to begin forecasting Companies then use

historical shipment data to forecast future demand (shipments)

Orders

A more accurate representation of actual demand is actual customer orders Historical

customer order data can be used to forecast future customer orders This is prevalent in

the consumer product goods (CPG) industry, e.g., a yogurt manufacturer would use

historical order data from customers, such as grocery stores, to forecast future yogurt

demand

Point-of-Sales Data (POS)

POS data is the most accurate form of actual demand since it is captured at the time a

customer sale is made This was made popular by diaper manufacturers, who

collaborated with their customers to capture actual diaper sales at the points of sales,

and then used that to forecast future demand

Generally, a company needs about two years of sales history to capture any trends and

seasonal variations More data is even better If sufficient data is not available for

statistical forecasting, a company should immediately start to capture data so that they

can use statistical forecasting in the future

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Key Inputs - Promotions

Promotions are used to increase product sales, but companies must ensure that enough

product is available during the promotion period to avoid losing sales and customers

Marketing is responsible for determining the timing and the type of promotions run for

products

Suppose that, due to some advertising promotions in July, a company's demand for a

product increases during August This is important information for demand planning to

model for several reasons For instance, if the company does not run a promotion during

July next year, the demand for its products during next August will be lower than the

August demand this year Alternatively, if the company decides to run the promotion at a

different time of the year, they can use this information to better estimate the anticipated

increase in demand

Marketing uses many types of promotions, such as:

In-store Coupons

Coupons for items are provided in the aisles where the product is sold This type of

promotion is very common for food items and other commonly consumed items (e.g.,

toothpaste, soaps, cereal, etc.) found at grocery stores and large drug store chains

While promotional timing is determined by Marketing, the impact may be based on

historical analysis or the managerial judgment of demand planners

Buy-One-Get-One-Free

Most commonly used for grocery and other food items, the timing of these events is also

determined by Marketing, but the impact on sales can be based on historical analysis or

managerial judgment

Mail-in Rebates

Mail-in rebates are a commonly used promotion for several types of items, such as

computer disks, CD ROM disks, televisions, cell phones, tax software, etc Mail-in

rebates usually require the consumers to purchase a product during a specific time

period Once again, it is important for demand planners to model the effect of mail-in

rebates on product sales

Free Standing Inserts (FSI)

Popular in the Consumer Packaged Goods (CPG) industry, a company will offer a free

tenth item in a 9-piece box of cooking utensils, for example This is referred to as an FSI Once again, such promotions are usually run for very specific and short time periods, and the impact on sales is taken into account in the forecasting models

Model Close Out

As the life cycle for a model comes to an end, companies may offer incentives for their

distributors to push product to customers

Quantity Discounts

Often companies will offer their distributors quantity discounts if they purchase product in large quantities This type of promotion generally generates a significant amount of

demand activity during the promotion period If not accounted for during demand

planning, it may lead to a serious efficiency in the supply

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Key Inputs - Causal Factors

Sales of certain products are influenced by some external factors For example, the sales for beer will increase during a period when the weather prediction is for unseasonably

higher temperatures If a company can model the effect of temperatures on the sales for

beer, the results from their forecasting model will be more accurate Other causal factors

include sporting events such as the World Cup Soccer Tournament

For many items, sales increase or decrease during specific times of the year For

example, ice cream sales will usually soar during hot summer months, candy sales will

increase around holidays, and snow blower sales will increase during cold winter months

On the other hand, ice cream sales are likely to dip during those same cold winter

months

There are a number of statistical techniques used for forecasting the sales for products

with seasonal variations Demand planners need to ensure that if seasonal effects are

suspected for a particular product, they include sufficient data to capture past seasonal

effects This usually translates to at least two years of data

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Additional Considerations

There are additional inputs that the demand planner may take into account while

developing a demand plan We classify these as additional considerations:

• New Product Introductions

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Additional Considerations - New Product Introductions

Statistical forecasts are usually based on historical data In the case of new product

introductions, where there is no sales history, companies often analyze items similar to

the new product, and use their demand patterns as a gauge for the demand pattern of

the new product—referred to as like-item analysis Marketing usually provides inputs to

demand planners who compare product characteristics, features, functions, price, etc

Demand planners then use historical data and forecasting models for "like-items" to

develop forecasting models for the new product introductions

As new products are introduced, some products are phased out The complete cycle of a

product (from introduction to phase-out) is known as the product life cycle During

demand planning, planners must also consider product phase-out, which leads to the

idea of product life cycle planning The demand for products at the end of their cycles

may decrease rapidly, and planners must use like-item analysis to determine how quickly the demand deteriorates

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