Business intelligence in retail

Business Intelligence in
Retail Industry
Analysis of Role of BI in Retail/FMCG Industry

Submitted By:
Team: Phoenix

Shweta Jain (09BM8031)

Abhirup Das (09BM8002)

VGSOM, IIT Kharagpur

MBA Batch 09-11

BI in Retail/FMCG Industry

Table of Contents

Introduction .................................................................................................................................................. 4

Overview of Retail/FMCG Industry ............................................................................................................... 5

Major Changes .......................................................................................................................................... 5

Current Needs ........................................................................................................................................... 6

Key Performance Indicators (KPIs) in Retail Industry ............................................................................... 7

BI in Retail ..................................................................................................................................................... 8

BI System Framework in Retail ................................................................................................................. 8

BI Applications .......................................................................................................................................... 9

Evolution in BI ......................................................................................................................................... 11

Business Intelligence Advantages in Retail Industry ............................................................................... 12

Data Modeling: Retail Scenario................................................................................................................... 13

Dimensional Design Techniques ............................................................................................................. 13

Brief Description of the Business Scenario ............................................................................................. 13

Dimensional Design Steps ....................................................................................................................... 14

Key Players in Retail BI Market ................................................................................................................... 18

BI Tools ........................................................................................................................................................ 19

Type of BI tools ....................................................................................................................................... 19

Various BI tools ....................................................................................................................................... 19

Key Data set used for BI tools ................................................................................................................. 20

Top Trends in FMCG & Retail Sector ........................................................................................................... 21

IT Budget Outlook by CIOs from the FMCG & Retail sectors .................................................................. 21

Technologies which companies will invest in to save operational costs using IT ................................... 22

Top challenges faced by CIOs / CTOs ...................................................................................................... 22

2


Predictions by Gartner about the BI (Retail) Market .............................................................................. 23

Conclusion ................................................................................................................................................... 23

References .................................................................................................................................................. 24

3


Introduction

Every organization generates large amount of data from their day to day operation. From the pool of

humongous data, some of them are able to harvest useful information; termed as Business Intelligence

(BI). Organizations which are able to develop business intelligence as a competitive advantage not only

focus on descriptive analytics and reporting, rather on predictive analytics to take more effective steps

towards delivering better performance and stronger bottom-line. Extended supply chains and globally

dispersed customer base has made managers more keen to use decision support systems based on

sound logical framework rather than going with their experience and gut-feelings.

BI refers to the skills, processes, technologies, applications and practices used to support decision

making. Thus a BI system can be called a decision support system (DSS).

In nutshell we can say Business intelligence (BI) is computer based techniques that are used to spot, dig

out, and analyze business data, such as Sales compared to budget/target or Sales compared to last year

(or any other period) or revenues per department/product. BI not only provides historical and current

view of business but also helps in predicting future view of business operations.

Various functions of Business Intelligence technologies are reporting, OLAP, analytics, data mining,

business performance management, benchmarking, text mining, and predictive analytics.

4


Overview of Retail/FMCG Industry

Major Changes

• Increasing Competition

FMCG as an industry has experienced explosion of products; division of markets (traditional

individual stores & organized retail); entry of new players, especially in organized sector;

increasing competition as a result of this; innovation and process capability of global players

• Increasing Customer Expectations

Customers, with larger disposable income now have greater appetite & demand for global

products. The global exposure has also resulted in requirements differentiated product &

service without any compromise on quality. In India a middle class segment bulging can be

observed which is full of vibrant young stars; fully conscious about the value proposition of the

product.

• Retail-FMCG space dynamics

Industry dynamic has evolved with time. Currently it has become commoditized market. There is

very low customer switching cost and high completion.

• Impact on strategy & operations

The industry is suffering from increasing supply chain bottlenecks due to internal and external

issues. Changes in these fronts have impacted strategy & operations of the industry.

Organizations are taking below steps to improve their operations

 Increasing Flexibility

 Outsourced Contract Manufacturing

 Demand driven supply chain

 Improving Customer service
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 Retail outlet segmentation

 Customized Retail service

 Supply Chain segmentation

 Greater Organizational Alignment

 EPM, Divisional Scorecards

 Integrated Sales & operations planning

 Greater Customer focus

 Demand analysis

 Product characteristics

Current Needs

Below are the current needs of FMCG/Retail Industry

• Organization Alignment & Strategy Management

• Financial Analytics for greater ROCE

• Customer Analytics

• Trade Marketing Analytics

• Workforce Analytics

• Supply Chain Analytics

• Operations Analytics

6


Key Performance Indicators (KPIs) in Retail Industry

The Supply Chain Council in their Supply Chain Operations Reference (SCOR®) framework has developed

a system of metrics applicable to retailer-supplier context. The highest level is level 1 which is

mentioned here along with respective performance attributes. Level 1 metrics can be drilled down to

subsequent levels to get more specific answers. KPI’s can be combined into sets according to the need

of different functions.

SCOR® Defined KPI’s Other Popular KPI’s

Performance Attributes Level 1 Metric 1. Sales compared to
budget/target
Reliability Perfect order fulfillment
2. Sales compared to last
Responsiveness Order fulfillment cycle time year (or any other period)
3. Sales per Square Foot
Flexibility Capacity change (Upside and
4. Wage Cost Recovery
downside )
5. Average Sale per
Costs Cost of goods sold and Supply Customer/Transaction
chain management cost 6. Units per
Customer/Transaction
Asset Management Cash to cash cycle and ROA
7. Conversion rate
8. Sales per Hour (for store
or associate) – selling hours
only
9. Sales per Hour (for store
or associate) – total labor hours
10. Time Spent in the Store

7


BI in Retail

BI System Framework in Retail

The BI System Framework involves establishment and integration of hardware and software

components.

 Hardware’s facilitates data possession, storage, access and management.

 Source system comprises of RFID and barcode reader, handheld scanner, sales force and CRM,

visual audits, SAP ERP, WD System, PLM, human resource management system etc. which

capture and store data from daily transactions.

 ETL layer is responsible for data extraction, cleansing, staging, transformation & loading. Data

warehouse stores data to serve different needs in terms of business plan, forecast, market

research, outlet sales, Invoices, purchase orders, inventory, transport orders, stock orders,

salary advice, re-distribution costs , account receivable, account payable, visual merch audit,

production plans and jobs etc.

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 The BI layer does with number crunching applying business logic and generates metrics, regular

and ad hock report. Output of this layer feeds performance management system of corporate

office.

BI Applications

BI generally supports analytics function of an organization. Major areas under this function are listed

below:

Financial Marketing

Working Capital Optimization (C2C Cycle) Trade promotion effectiveness, WD profitability,
Outlet performance, Channel utility, Integrated
(Including Receivable Analytics, Payables
consumer demand & market performance
Analytics, Inventory management) and
visibility, Responsiveness
Treasury Cash Analysis

Supply Chain Organization Strategy

Supply Chain Performance Management Balanced Scorecards (Strategy Maps, Dashboards)

Product cost & profitability analysis Budgeting, Planning & Consolidation systems

Operational planning and execution Operational performance management

Inventory visibility & safety Operations

Route optimization Plant Performance Management

Supply network efficiency MES Analytics

Spend Analytics Bottleneck alerts

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BI helps to raise the level of collaboration among supply chain partners. In a retail-FMCG set-up as

retailer-supplier relationship matures, risk sharing occurs among supply chain partners. Partners can be

convinced of sharing risk only when the proper information is available across the chain through BI so

that they can take right decision at the right time.

Most basic type of alliances is called a Quick Response Program (QRP) where retailer provides point of

sale (POS) information to supplier. Supplier may use this for scheduling production and delivery or

determining inventory level. The retailer still submits individual orders and supplier maintain own

forecasting system.

In a Continuous Replenishment program (CR), supplier delivers at an interval mutually agreed by both

the parties. The relationship gradually moves to an on-going relation from a transactional one.

Vendor managed inventory (VMI) is a more sophisticated collaboration than QRP or CR and still evolving.

Here, supplier takes over inventory functions that the customer would deal with in a traditional

arrangement (e.g. decisions regarding storage and display of products, access to bins or storage

facilities, replenishment, record keeping and managing delivery ).

Collaborative planning, forecasting and replenishment (CPFR) is a set of best practices formalized in

1998 by the Voluntary Inter industry Commerce Standards Association (VICS).It provides a model for

trading partners to jointly plan key activities from production to delivery of products and encompasses

joint strategic planning, demand & supply management, execution and analysis based on sharing

relevant information. To be successful, VMI and CPFR both needs intensive sharing of not mere data, but

the predictive power of the data that resides in the systems of collaborators.

10


Evolution in BI

BI has evolved with time; initially it was more about MIS Reports. Since the BI has matured form just

static reports to on the spot answers via dynamic Dashboard at gives current and updated snapshot of

information enabling Real time analysis.

Instead of just showing data, BI gives insights into the data, alerts and root cause analysis that helps in

decision making. BI has gained greater integration power, thus enabling data merger for various

external and internal sources. It has become a collaborative business planning tool.

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Business Intelligence Advantages in Retail Industry

Business Intelligence addresses issues like business strategy planning, location wise analysis, quick

response to market volatility, cash flow management and inventory rationale, and merging data from

varied internal as well as external sources. Below are few advantages of using BI in Retail Industry

 Align business with client needs: Help managers design marketing campaigns for specific

products by identifying opportunities.

 Differentiate from competitors: Improved business by collecting and analyzing data from

various systems (financial, point of sale, inventory, distributors, and customer relationship

management systems) in various formats.

 Optimize resource allocation: Integrate financial data to control operation costs and optimize

business performance with better profitability.

 Input to Decision Support Systems

o Reporting capabilities for key performance metrics

o Performing complex analysis (e.g. promotion, pricing strategy, product mix)

o Developing statistical models that predict client needs and behaviors

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Data Modeling: Retail Scenario

Dimensional Design Techniques

The data modeling can be done by applying dimensional design techniques. It arranges the data in a way

that it can satisfy specific business query. Steps involved in dimensional design are:

 Select the Business Process: A major operational process that is supported by some kind of

legacy system(s) from which data can be collected for the purpose of the storage and modeling

Example: sales , orders, invoices, shipments, inventory

 Decide Granularity: The fundamental level of data represented in a fact table for the selected

process. Every data mart / warehouse should be based on the most atomic)data that can

possibly be collected and stored. Example: individual transactions, individual daily snapshots

 Choose the Dimensions: Choose the dimensions that will apply to each fact table record

 Identify the Facts: Choose the measured facts that will populate each fact table record

 Conforming the dimensions: Common dimensions across the Facts/ data marts required to be

exactly same or subset of the main dimension table

 Adding Attributes to Dimension Tables: The depth of analysis can be augmented by increased

number of attributes

Brief Description of the Business Scenario

 Smart-Mart is a leading grocery retail chain

 Each stores has departments like grocery, bakery, dairy, healthcare, cosmetics etc

 60,000 individual products(SKUs)

 Bar Codes (UPCs) imprinted on packages

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 Hand-held scanners feed data directly to POS

 Management goal is efficient ordering , stocking and selling of products while maximizing profit

 Decisions to do with pricing and promotion

Dimensional Design Steps

1. Select the Business Process: “POS retail sales ” process to be analyzed: what products are

selling on what stores on what date under what promotion scheme?

2. Decide Granularity : Individual line item on a POS transaction

3. Choose the Dimensions:

4. Identify the Facts:

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5. Dimensional Table Attributes:

Product Dimension: It is used to describe the stock keeping units (SKU) in a store and contains

many descriptive attributes of each SKU. Some of them represent the merchandise hierarchy

(e.g. individual items can be combined to brands and brands to category and so on).

The analyst can browse through dimension attributes and we can roll up and drill down using

attributes as constraints even if they do not belong to the merchandise hierarchy. Drilling down

actually adds one column more. Product Dimension table design should be taken with great care

covering almost all necessary attributes to ensure exhaustiveness.

Date Dimension: This is a very familiar dimension across all industry. Qualitative attributes bear

more significance for this dimension (e.g. sales on weekends versus working days). Due attention

should be paid to ensure that the columns had important values such as holiday/no holiday

/weekends etc versus a cryptic substitute (e.g. yes/no)for them; which in turn will reduce the
15


decoding effort required by reporting department. More time series factors like retailing season or

fiscal periods can be included to make the design robust as the business needs to slice and dice data

by these nonstandard attributes.

Store Dimension: This is the primary geographic dimension which describes every store in a retail chain.

Stores can be rolled up using attributes like ZIP code, districts, regions and county etc. Store dimension

is mainly used in the corporate reporting and the necessary components of the store dimension from

multiple operational sources assemble generally assembled at headquarters.

Promotion Dimension: Common promotion activities are end of the season sales, provisional price

reductions, festival offers and coupons etc. which are represented by promotion dimension. Trade

promotion effectiveness is a management concern for achieving volume and profitability. Various

promotional measures should be judged individually for a more straightforward administration. As these

measures are highly correlated, multi-co linearity factor need to be accounted for decision making.

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17


Key Players in Retail BI Market

1) IBM Cognos

2) SAS

3) Microsoft Dynamics

4) SAP

5) SRIC BI

6) WebFOCUS BI

7) QLIKVIEW

18


BI Tools

Type of BI tools

1. Spreadsheets

2. Reporting and querying software: tools that extract, sort, summarize, and present selected data

3. OLAP: Online analytical processing

4. Digital Dashboards

5. Data mining

6. Decision engineering

7. Process mining

8. Business performance management

9. Local information systems

Various BI tools

1. Open source free products

a. Eclipse BIRT

b. Rapid Miner

2. Open source commercial products

a. Palo (OLAP database): OLAP Server, Worksheet Server and ETL Server

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b. Pentaho

3. Proprietary free products

a. InetSoft

b. MicroStrategy: MicroStrategy Reporting Suite

4. Proprietary products

a. IBM Cognos

b. Informatica

c. Teradata

Key Data set used for BI tools

1. Sales Data

a. POS

b. Turns

c. GMROI

2. Market Data

a. Market share

b. Competitor pricing

c. Competitor product lines

3. Promotional and marketing data

a. Success of past promotions / customer feedback

b. Total cost of promotion

20


c. ROI on promotion

4. Customer-centric data

a. Demographics

b. Frequency

c. Loyalty

5. Supply chain and operations data

a. Demand

b. Identify profitable products

c. Keep track of units sold

6. Merchandising data

Top Trends in FMCG & Retail Sector
Business Intelligence is Top Technology Priority (Source: Survey - FMCG - Infrastructure Agenda
2010)

IT Budget Outlook by CIOs from the FMCG & Retail sectors

Most of the IT budget would be allocated to BI, ERP, CRM, Security and Virtualization tools

21


Technologies which companies will invest in to save operational costs using IT

Top challenges faced by CIOs / CTOs

22


Predictions by Gartner about the BI (Retail) Market

1. By 2012, more than 34% of the leading 5,000 international firms will fail to make astute choices
about considerable alterations in their operations and markets, due to inadequate data,
equipments and techniques.

2. By 2012, business units will control at least 40% of the total budget for business intelligence

3. By 2012, 33% of analytic applications applied to business processes will be delivered through
course-grained application mash ups.

4. By 2012, half of the systematic functions related to business operations will be supplied via
coarse-grained appliance.

5. By 2013, the commercial divisions will administer at least 40% of the entire financial plan for BI.

6. Asia Pacific BI market will grow at CAGR of 15.3% through 2012, reaching across US$150 million.

7. The local BI market will be sustained through maintenance revenue.

Conclusion

TODAY IN EVERY BUSINESS the most challenging job is to make you efficient enough for surviving in

this volatile market place. With increase in growing completion in Retail industry the profit margins

have become thinner. In order to address the ever changing business demands retailers need to have a

secure and dynamically scalable infrastructure. As a solution, companies should focus on implementing

technologies that will enhance the customer experience. They should view IT initiatives as major

component that will help the business drive market share rather that just supporting the business. In

given scenario Business Intelligence and analytics will play a key role and using BI tools will make

decision making easier and faster.

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References
1. Gartner Reveals Five Business Intelligence Predictions for 2009 and Beyond – Gartner Report

2. Kimball R. & Ross M., The Data Warehouse Toolkit, 2nd Ed., John Wiley & Sons, Inc.

3. Enterprise BI Strategy for ITC – by ITC Ltd.

4. http://www.dmsretail.com/kpi’s.htm

5. http://en.wikipedia.org/wiki/Business_intelligence

6. http://www.apics.org/Resources/

7. http://supply-chain.org/resources/scor

8. http://business.mapsofindia.com/business-intelligence/for-retail-industry.html

9. http://www.prlog.org/10123229-the-competitive-game-is-changing-for-retail-business-
intelligence-for-retail-industry.html

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