trusted experts in business analytics

BUSINESS ANALYTICS FOR DEMAND PLANNING: ARE YOU READY FOR 2020?

September 2014

Trusted Experts in Business Analytics

Copyright 2014, QueBIT. All rights reserved.

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BUSINESS ANALYTICS FOR DEMAND PLANNING: ARE YOU READY FOR 2020?

ANALYTICS AND DEMAND PLANNING

Few will dispute that businesses succeeding in 2020 will be those able to integrate technology that

allows them to anticipate, control and react to customers’ shifting demands. Their objective is to offer

the best customer experience while remaining price

competitive. As part of this effort to deliver better customer

experiences, build competitive differentiation and ensure cost

efficiency, demand planners developing a 2020-ready

organization are incorporating business analytics tools into their

technology strategy.

Demand planning is critical to competitiveness especially in industries in the midst of major

transformation such as retail, distribution, financial services, manufacturing, services or

pharmaceuticals. As an example, the retail industry is in

the midst of most profound transformations in the way

consumers shop, where they shop and when they shop.

Having the right product in the right location is vital to

driving customer satisfaction and sales. Equally

important is having the right inventory that is turning and

not taking up unnecessary space. Too much product

requires pulling the inventory back, or even reducing

sales; lack of inventory available may result in premium

freight costs to get the inventory into location. The ideal

is the lowest adequate quantity at the right location as

this will decrease overall carrying costs and improve

cash flow.

In our experience, for many companies, planning demand has typically been a semi-manual process.

For example, an insurance company might model premium income by starting with policies currently

in force, estimating attrition rates, forecasting new policies due to marketing efforts, and adding in the

impact of increased or decreased premiums, in order to come up with projected policy volumes and

premium income. This process relies upon the subjective judgment of someone who is reasonably

expert in the particular insurance market. Another example might be a retailer where purchasing

managers estimate demand volume at a product group/distribution center level. This demand plan

QueBIT’s integrated demand

planning solution maximizes

revenues through forecast

accuracy while controlling costs.


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must then be pushed down to a store/SKU level by some mechanism for consumption by inventory

management systems. For many manufacturers the forecasting process consists of entering their

retailers’ best demand estimates into a spreadsheet, resulting in a predictive analysis that is human

driven and in effect controlled by their customers.

The disadvantages of these manual forecasting techniques are significant. Manual estimation of

demand at this granular level is error prone and laborious. Even though, in the insurance example

quoted above, an underwriter might use historical attrition percentages and other historical data to

help project volumes, there remains a significant degree of human judgment in the process. In the

case of the retailer, ideally the planner would estimate demand volumes at the store/SKU level, but in

practice the amount of effort required for manual

forecasting makes this approach impractical.

The better approach is to predict demand using a data

driven approach. This technique, called predictive

analytics, is gaining popularity. In the case of retail

demand, historical data can be analyzed to detect

seasonal patterns, product lifecycles and the effect of

causal variables such as weather, promotions, pricing,

etc. Historical insights are used to develop models that

predict customer needs and then prescribe business

decisions to support customer demands within the supply

chain.

Modern software makes what would have been an impossible task a few years ago, an exercise that

anybody can do. By providing planning and execution decisions that are optimized across the

enterprise, predictive analytics solutions deliver better customer experiences. They ensure the perfect

product flow, create a more effective merchandising mix, and improve inventory efficiency.

If we combine this predictive analytics capability with a financial modeling tool it now becomes

possible to predict demand and model a full profit and loss account, balance sheet and cash

flow. Imagine a world where the retail demand planner can increase marketing spend in a region

while lowering some prices, and immediately see the impact on demand as well as the impact on the

bottom line.

The integration of IBM Cognos

TM1 and IBM SPSS Modeler

combines a financial modeling

tool with predictive analytics

capabilities.

Demand planners are now able

to predict demand and model its

impact on a full P&L account,

balance sheet and cash flow.


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IBM SPSS Modeler is a predictive modeling tool which integrates seamlessly with IBM Cognos®

TM1®. The integration between these two tools allows demand planning and financial modeling, all

in one single environment. This integrated platform delivers a solution that increases the company’s

ability to forecast demand more accurately and to evaluate its impact on the organization’s financials,

providing visibility into where your inventory is accurate, where your spend is effective and where you

can generate free cash flow—all essential to the company’s success.

BENEFITS OF IBM TM1 AND SPSS MODELER INTEGRATION

QueBIT is one of few IBM partners able to implement the integrated IBM Cognos TM1 and IBM SPSS

Modeler, the most advanced business analytics solutions.

IBM Cognos® TM1® is a performance management solution for companies of all sizes to transform

slow, expensive, disconnected processes into more dynamic, efficient and connected experiences.

A highly scalable and very fast enterprise financial performance management solution, TM1 supports

the entire planning cycle including forecasting, statutory reporting, consolidations, customer/product

profitability, what-if scenario modeling, strategic financial planning, revenue and expense allocations.

Demand planners load historical data into TM1 which will then build mathematical models based on

whatever factors are relevant or appropriate. You can run multiple what-if scenarios to evaluate the

historical impact of multiple variables such as marketing spend on demand.

IBM SPSS Modeler allows demand planners to discover historical patterns and trends in their

structured and unstructured data and predicts future outcomes. An intuitive visual interface is

supported by advanced analytics such as classification, regression, association, time series

forecasting, survival, and clustering analyze historical and current data to capture relationships

among many factors and uncover patterns.

Often it isn’t practical for an individual to go in and determine what model to use. Large stores may

generate 100,000 store SKU forecasts, and trying to determine manually what model to use for each

product would be overwhelming. SPSS Modeler generates the forecasts automatically by analyzing

the data and determining what model provides the best fit – a more reliable and scalable process.

Forecasts are generated rapidly which in turn makes it easy to conduct multiple what-if scenarios.


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The models classify customers or prospects into groups and identify different relationships between

groups and products. These patterns identify risks and opportunities by determining the likelihood of

an event occurring given a particular set of conditions or the probable outcome of a future event. For

instance, decision makers can forecast sales volume next month in Connecticut based on historical

analysis, future market expectations and managers’ estimates, or forecast how unseasonably warm

weather will affect demand.

The integration of TM1 and SPSS Modeler allows organizations to converge financial modeling with

predictive analytics. Too often companies think they already have a demand planning tool. Yet, they

use siloed data and rely on spreadsheets that are not integrated with operational, corporate and

financial goals. They don’t have a full view of the impact of their demand forecasting.

The SPSS predictive models project a full P&L and cash flow, therefore providing an integrated view

of the impact of what-if scenarios. Analyze sales based on historical seasonal patterns and long term

trends, measure the impact of the weather, marketing spend and macroeconomic factors to predict

demand patterns and understand their impact not only on demand but across the enterprise.

Evaluate complex scenarios at a very granular level such as what inventory levels will be based on

opening balance sheet and sales, manufacturing costs and collections; how a 5% increase in the

Euro will impact demand and profitability; how a 10% change in the price of raw materials will affect

demand, or how a 10% increase in marketing spend in a specific store will impact inventory levels in

the regional distribution center. Rely on data driven analysis to determine your optimum selling price,

your optimum marketing spend, and how all this affects demand and cash flow.

What is really novel about the TM1/SPSS Modeler integration is its capacity for real time responses

about the impact on demand. The ease of conducting the analysis means demand planners have the

capacity to create rolling forecasts to reflect changing conditions, with the ability to rerun the

predictive model each week or each month. The predictive model keeps getting better as additional

actual history is included– the rolling week 52 forecast gets better for every week into the model.


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TM1/SPSS INTEGRATES WITH LEGACY SOFTWARE

Comprehensive enterprise resource planning suites of integrated application software used for order

management and supply chain management generally don’t feed into the financial systems very well.

In many instances, companies have to manually extract data and use spreadsheets to attempt to

model its impact on forecasts. A solution like TM1/SPSS is specifically built for both financial

planning and forecasting, and also built to easily integrate into existing order management and supply

chain planning systems.

This is why companies with legacy order management/supply chain management solutions (e.g. JD

Edwards), are choosing to incorporate a business analytics solution like TM1/SPSS into the

process. In these instances the system is entirely integrated. SPSS Modeler extracts data from the

order management/supply management systems, analyzes the data to create a forecast and then

pushes back the approved forecast into the order management/supply chain system every night.

The advanced modeling capabilities of the solution also allow companies to model demand for cases

where traditional approaches fall apart. Predicting demand for low velocity items is a prime example.

Most tools model demand for low velocity items at a rolled-up level (such that the model can treat the

item as high velocity). The disadvantage of this approach is that spreading the forecast back down to

the lowest level (e.g. store level) is very error prone. IBM SPSS Modeler can model inventory turns

for the slowest moving inventory at the lowest level with a high degree of accuracy. It has been


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applied with a high degree of success to inventory that turns only once per year at the store/SKU

level.

Additionally, IBM SPSS Modeler can generate accurate forecasts for new store/SKU combinations.

By modeling demand as the intersection of customer and product attributes, IBM SPSS Modeler can

predict demand for product where there is no peer product in the supply chain. Whether the item is

new the store or new to the entire supply chain, IBM SPSS Modeler can generate accurate forecasts

for that item. Imagine having the ability to simulate new product introductions into new stores,

markets or companywide and with an accurate demand forecast that can be financially modelled in a

highly interactive IBM Cognos TM1 interface.

IMPRESSIVE AND MEASURABLE RESULTS

Companies that deploy the integrated TM1 and SPSS Modeler benefit from being able to:

Calculate the ideal price points, discounts, and promotions by product, store or channel to

maximize profits.

Plan for inventory demands across locations and channels to have the right product in the

right location at the right time

Lower inventory levels while reducing out of stocks, improving sales and reducing

transportation costs

Determine the best responses to shifting customer demands

Improve decision-making through what-if scenario analysis

Evaluate the impact of marketing campaigns on sales

The combination of these capabilities has a direct impact on the bottom line due to optimal use of

working capital, higher inventory turn, less product obsolescence, lower inventory carrying costs,

improved financial planning and improved cash flow. This increase in productivity and

competitiveness means higher profits.

QueBIT recently applied TM1 and SPSS Modeler at a F500 retailer with a very large SKU base. The

objective was to align their supply chain with actual demand by using an accurate demand forecast.

As a result, the retailer:

Reduced overall inventory by 7%

Increased inventory turn by 4%, a very large number considering their massive inventory

Increased sales by 13%


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These impressive results showcase the benefits of business analytics because retailers are seldom

able to decrease inventory and also increase sales and inventory turn. The TM1/SPSS integration

also allows the retailer to model what-if scenarios in real time, adjusting parameters to instantly see

the impact of changes on inventory turn, inventory costs and sales.

ABOUT QUEBIT

Trusted experts in business analytics strategy and implementation, QueBIT is dedicated to helping

organizations improve their agility to make intelligent decisions that create value. An IBM Premier

Partner, QueBIT has conducted hundreds of successful implementations of IBM® Cognos® TM1®, IBM

Cognos BI and IBM SPSS—we are one of few partners that offer such a broad range of analytics

solutions. Financial, sales, marketing and operations departments in over 350 organizations in all

types of industries say QueBIT’s singular approach to business analytics produces tangible results—

which is why we are repeat recipients of IBM’s Business Analytics Partner Excellence Awards.

www.quebit.com

Contact our team to learn more about how QueBIT’s integrated Demand Planning solution

maximizes revenues through forecast accuracy while controlling costs at [email protected]

http://www.quebit.com/

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