October 12, 2022 | Author: Anonymous | Category: N/A
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Data Warehousing and Multi-Dimensional Data Modelling
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It was January 15, 2015, and Vishal Mehta, Chief Information Officer of Acme Inc., an Indian retailer of consumer electronic and durable goods, had just wrapped up a meeting with Steve Barley, Vice President (Retail), (Retail), and several other key regional and natio national nal heads of Acme. The team discussed the company’s plans for the year ahead and the key decision support infrastructure woes faced by operational units in data access, integration, analysis, usage and interpretation. Mehta presented his ideas on how a data warehouse investment could help address many of these decision support infrastructure challenges. The management was quite receptive to his ideas and asked him to conduct a feasibility study, build a pilot and prepare a detailed implementation plan. Mehta had more than 15 years of experience successfully building and managing a number of
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IT projects. (DW)-related However, heprojects. lacked sufficient expertise in more business intelligence (BI) or than data warehouse BI/DW projects were challenging to execute normal IT projects and required organisation-wide commitment and support. Mehta, therefore, was contemplating how to build a data warehouse solution to meet his organisation’s business requirements. About Acme Inc
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Acme Inc. was one of the leading national retail chains in India. The company had over 150 stores spread across nine major states and 24 key cities with floor areas ranging from 15,000 to 24,000 square feet. It was a specialist retailer in the consumer electronics and durables market. A 2012 industry report had projected that the consumer electronics and durable market in India would reach INR 520 billion by 2015 at a compounded annual growth rate (CAGR) of 15%. 1 By 2013, the company had become one of the major players in the Indian market with a market share of 18-20% and INR 33 billion (3,300 crores) in sales revenue.
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Acme’s stores offered its customers merchandise in a variety of branded and non-branded categories — computers, tablets, cameras, home appliances, kitchen appliances, gaming, and so on. Each store stocked about 12,000 stock keeping units (SKUs) or products, 180 brands, and nine major product categories. The company had also established an online presence, selling its merchandise in around 300 cities and towns. 1ASSOCHAM.
du rables industry. (2012). Emerging trends in consumer electronics and durables
Prepared by Prof. Srikumar Krishnamoorthy, Indian Institute of Management, Ahmedabad. The case is written based on our analysis of a leading retail business in India from secondary data sources.
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Cases of the Indian Institute of Management, Ahmedabad, are prepared as a basis for classroom discussion. They are not designed to present illustrations of either correct or incorrect handling of administrative problems. ©2014 by the Indian Institute of Management, Ahmedabad.
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Store Operations
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Acme’s store operations involved a number of activities including indenting, inventory handling, customer service, workforce allocation and management. These activities were coordinated by a store manager and executed by sales assistants and other employees. The store manager replenished the items in the inventory by placing orders to the regional
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warehouse. The actual quantity of items to be ordered, including adjustments for lot sizes, were automatically determined by an information system using standard inventory models (e.g., economic order quantity or EOQ). The store manager raised an indent when the store product inventory reached its reorder limit. To synchronise activities across stores and a regional warehouse, the organisation followed a policy of indenting once a week on specific days. The store manager also raised indents on an ad-hoc basis for specific items, especially when there was a sudden surge in sales and the item stocked out. Store employees handled the shipments received from the regional warehouse. These shipments were first stored in a local storeroom. The items in the container (i.e., cartons, pallets or bags) were then unpacked to retrieve individual product SKUs. The products were then moved to the store locations and placed according to the store’s planogram design (or floor plan). These planograms were generally prepared by merchandising managers, who analysed
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numerous variables such as customer purchase histories, demand patterns, local demographics, customer survey ratings, and so on. Merchandising managers often sought inputs from the local store managers to come up with better floor plans to improve the store’s profitability. One of the responsibilities of the store manager was to ensure that the products were stocked as per the planogram design. Store employees restocked items at store locations by following the planogram design. Store assistants maintained the aesthetic appeal of the items by cleaning them and placing or orienting them correctly after a customer visit.
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Each store conducted a physical verification of its inventory once in two weeks and reconciled discrepancies in physical and actual inventory. Discrepancies could occur for a number of reasons, such as employee theft, shoplifting, misplaced goods, billing errors, administrative errors, damage during in-store material handling, expired product shelf life and so on. These discrepancies (commonly referred to as shrinkage in the industry) accounted for 1.2-1.5% of
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Acme’s total sales. The company aimed to reduce this percentage to about 0.6% over the next three years. Customer service was another important area of store operations. This included activities like handling in-store customer queries, assisting customers in identifying and selecting the right products, processing customer orders, addressing customer complaints and handling product returns. Effective handling of these activities was of utmost importance to Acme, and particularly to each of its store managers, in providing the best in-store experience to their customers. When a customer placed an order, an invoice was generated at the point-of-sale (POS) terminal and the item was packed and delivered to the customer’s premises. For specific product SKUs (say, home appliances like refrigerators, washing machines and televisions), the retail store used
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local delivery transporters to ensure the timely delivery of the items to the customer’s c ustomer’s locatio location. n.
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As one can understand from the description of various store activities, there were multiple points where data could be collected. Acme collected sales data at the POS terminals, item receipt data for items received from the regional warehouse, physical store inventory information, and periodic customer survey reports.
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(See Exhibit 1 for a sample invoice that describes the natur naturee of data collected at POS terminals).
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Warehouse Management Management
The company built a network of regional warehouses to cater to the individual store requirements in the region. The primary warehouse activities included storage management and distribution, inventory management, liaising with raw material suppliers and manufacturers for item procurement, and liaising with transporters for proper and on-time delivery of shipments. As part of its distribution activities, each warehouse shipped items to a set of stores within its region. Typically, stores sent their requests for items or products to their regional warehouse in the form of an indent. The regional warehouse then prepared a pick list based on the indent, did the necessary packaging, and transported the items to the stores in delivery trucks/ vans. The warehouse coordinated with its transport service providers on the shipping of items and
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tracking ensurewarehouse their timely delivery. The company followed a weekly indent policy to shipments synchronise synchronisetovarious activities. Each warehouse received its supplies from multiple manufacturers on a periodic basis. The quantity and periodicity of order placement was determined based on forecast estimates made by the warehouse managers. Warehouse managers closely worked with manufacturers to identify the right products for specific regions, providing customer feedback and making sales data available to facilitate better production planning at the manufacturer’s manufacturer’s end.
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There were several areas in which the company was trying to make improvements. First, the forecast estimates were often inaccurate, deviating as much as 30-40% for some SKUs. This resulted in excess inventory in a few cases and stock-outs in most other cases. Second, there were frequent complaints from retailers over delayed shipments. Third, there had been many
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instances in handling which items hadthe been damaged.leading This typically occurred duringThe product shipment or material inside warehouse, to increased shrinkage. company was unable to clearly identify the root cause of the problem or bottleneck to take concrete actions to improve the situation. Product Promotions
Acme offered the latest and good quality products at reasonable rates, giving its customers high value for money. The company also partnered with several financial service firms who offered attractive terms (e.g., low down payment, low interest and flexible payment). In terms of promotions, the company helped customers get the best value for money through festive offers, product bundling, attractive discounts and psychological pricing. It also promoted its offering through advertisements across a variety of channels, including television, Internet, radio,
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billboards, social media contests/ games, and the company’s own online e-commerce portal.
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The sales and marketing team at Acme was interested in building a loyalty card programme to retain customers and improve overall profitability. The company also did not have appropriate metrics in place to measure the effectiveness of its promotional campaigns. Its marketing managers believed that significant improvements could be made in its promotional spend if the proper decision support infrastructure was put in place. While basic sales data was available, there was no way to get an integrated view of sales across multiple stores, slice and dice data, conduct multi-dimensional data analysis or build data mining models to identify the right customer targets. Information Technology Systems at Acme Inc.
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Historically, the company had grown through acquisitions. For example, in 2012, the company acquired a large distribution warehouse company with operations in multiple towns and cities in India. As a result of this inorganic growth strategy, business processes and IT systems at Acme were highly heterogeneous. A few operating units (retail stores and regional warehouses) used Enterprise Resource Planning (ERP) software as part of their order and inventory management processes; however, there were multiple ERP vendor solutions in different units. Even in units with the same ERP vendor solutions, their configurat configurations ions were very different. Each of the company’s stores and regional warehouses used a relational database to maintain
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operational data generated from their business processes. The individual units used different relational database products, and possibly different versions and/or configurations of the database products. While there were many differences in the way the data was stored, the underlying database design could be understood using an entity-relationship (ER) diagram. (The ER diagram shown in Exhibit 2 gives a partial view of how different entities pertinent to Acme’s business were related and the nature of their relationships relationships). ).
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The heterogeneity of IT systems at the hardware, system software, business application and data levels made it extremely difficult for Acme managers to get timely, relevant and accurate information for making better decisions. For example, information about the movement of products across the Acme supply chain was available in multiple systems. However, accessing the information from these systems in a timely manner to determine supply chain operational efficiency improvement was a daunting task.
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The Promise of a Data Warehouse
Bill Inmon, considered to be the father of data warehousing, defined a data warehouse as follows: “A data warehouse is a subject-oriented, integrated, time-variant and non-volatile collection of data in support of management’s decision making process.” 2
The key terms in the above definition are:
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Han, J., & Kamber, M. (2011). Data mining: Concepts and techniques ( 3rd 3rd ed). New Delhi: Harcourt India Private Ltd, Morgan Kaufmann Publishers.
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Subject-oriented: The data in a data warehouse was organised by subject, for example,
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vendors, products, stores, partners, etc.
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Integrated: A data warehouse integrated data from multiple, heterogeneous data sources.
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The differences in data definitions and inconsistencies across multiple source systems were resolved before loading the data into the data warehouse.
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Time-variant: Unlike an operational system, a data warehouse maintained historical data. For
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instance, while an operational system held the most recent customer location details (or mobile number/address), a data warehouse could be used to maintain all changes in customer location details (mobile numbers/addresses). numbers/addresses).
Non-volatile: The data warehouse was a write-once, read-many type of system. That is, the
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data once written to a data warehouse was rarely updated. The implication of this was that a data warehouse system did not need to support expensive transaction processing and recovery control procedures as in the case of operational database systems. In short, a data warehouse organised and stored data in a way that was best suited to perform analytical queries. It served as a basic foundatio f oundation n for building analytical applications. applications. A number of companies in multiple industry verticals (retail, financial services, telecom, gambling,
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transportation, etc.) realised significant benefits by investing in data warehouse infrastructure. As per Gartner analyst estimates, the overall market for data warehouse systems alone was estimated to be about US$ 9 billion in 2013 and expected to grow at a CAGR of 7%. 3 The size of the market, its continued growth and industry success stories clearly demonstrated the value that organisations could derive from such infrastructural IT investments.
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Data Warehousing at Acme Inc.
After the senior management meeting, Mehta organised a team led by Aditi Patel, Head of IT, to initiate the data warehouse project. He also hired Neil Clarke, a SAP consultant specialising in data warehouse design and development, to help them in this initiative. Patel asked Clarke to explain the processes involved in building a successful data warehouse solution:
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Patel: Clarke:
Good Morning Neil, How are you doing? Great. How is your new initiative on data warehouse coming along?
Patel:
We are in the very early stages of this project and would like to seek your help on successfully driving this initiative. In this context, I have already shared with you our organisational business process details.
We are unclear on how to approach this project. Where should we start and how should we proceed? Can you share your thoughts on some of the best practices in the industry?
3 Gartner
Reports. The state of data warehousing in 2012, 2013 and 2014.
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Clarke:
Yes. I did go through the documents you had shared. Based on my review, I prepared a data warehouse business matrix [refer to Exhibit 3] and emailed it to you this morning.
Patel:
I did glance through the warehouse business matrix document. But how do I really interpret and use this business matrix for building a data warehouse?
Clarke:
Let me explain. The business matrix contains information about your business processes — the rows of the matrix, and their context or dimensions — the columns. The individual cells are marked with an ‘x’ if there is a context associated with the specific business process row. For instance, the context for the retail product sales business process includes time, product, store, customer, promotion and geography.
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While building a data warehouse, select a few business processes from the matrix and design a multi-dimensional data model. The model is then used to build a data warehouse. The above process is repeated for all of the business processes, based on your analytical requirements. Patel:
Okay. Is it a kind of bottom-up, iterative process for building a data warehouse?
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Clarke:
Yes. One needs to prioritise a few business processes based on the current business demands. A data mart (a mini warehouse) is then built for each of the selected business processes.
Patel:
Great. How should we proceed after prioritising prioritisin g and selecting a few business processes for implementation? implementation?
Clarke:
One of the industry best practices in this area is to apply a four-step process for designing and building a data mart/warehouse: (1) Select a business process, (2) declare the grain, (3) choose the dimensions, and (4) identify the facts. Let us assume that we wish to apply the four steps to one of your
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business processes. As a first step, let us choose the ‘sales order’ business process. In the second step, we need to declare the grain of the business process. The grain is chosen based on your analytical requirements. For example, if you wish to analyse your sales data at the lowermost granularity, then the grain is declared as the transaction line item. On the other hand, if your analysis doesn’t require transactional-level data, then the grain is declared at a weekly or monthly level.
Patel:
Well, if the grain is defined at a higher level, will it not constrain my ability to analyse the data?
Clarke:
You are right. One needs to make a trade-off trade-of f between the size of the data stored in a data warehouse and one’s analytical requirements. For example, if
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A000181 the business user rarely or never accesses the transactional- level data for analysis, then it will be an unnecessary waste of data warehouse space. Alternately, it is possible to create multiple data repositories with different levels of grains to meet different analysis requirements.
Patel:
Hmm. Perhaps we should declare the transaction-level transaction-level grain as the last 90 days of recent data. For historical data beyond 90 days, we could declare a
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weekly/monthly grain. This would allow the periodic archiving of the older data to a higher-level grain. Clarke:
You are spot on. This is precisely how organisations organisations meet their analytical requirements and at the same time optimise warehouse storage space. Let us move on to the third step of choosing the dimension, a fairly easy step if you have correctly declared the grain of the business process. For example, if the grain for the sales order process is declared as sales per line item, then the typical dimensions are the context of the sale such as item, store, customer, time and geography. The final step in the dimensional design process is the identification of facts.
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These are the performance measures that one wishes to monitor. For the sales order business process, the facts are generally quantity of items sold, dollar value of sales, gross revenue and gross margin. Patel:
This is quite interesting. Now, I get a clear sense of how the dimensional modelling for a data mart/warehouse works. We will soon start work on prioritising our business processes and applying this framework to design the data mart/warehouse.
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Neil, we shall connect with you again to understand the next steps. More specifically, we wish to understand the best practices in making the right architectural choices and building a data warehouse infrastructure.
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Clarke:
Sure. I’ll certainly certainl help week your organisation organisati in this Let us connect againy next for a deepondive on data data warehouse warehouse journey. design and development.
Patel and her team had a series of conversations with Clarke to better understand various aspects of data warehouse design and development. Through this process, the team gained a good understanding of data warehouse architecture and its components. (A sample data warehouse architecture diagram is given in Exhibit 4. One of the components in the architecture is the online analytical processing (OLAP) engine. OLAP is an interactive data analysis tool that allows one to view data from a multi-dimensional perspective. Exhibit 5 provides an illustrative list of operations one can perform in OLAP. Exhibit 6 depicts some of the key architectural choices available for data warehouse warehouse implementation).
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The team had to decide on which architecture would be the most appropriate for Acme.
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Pilot Data Mart Solution — Key Analytic Requirements
Patel and her team interviewed key stakeholders at Acme to understand their analytic requirements. Of the many requirements that were identified, the team, in consultation with the business units, prioritised prioritised the followi f ollowing ng set of requirements for the pilot project: Retail Business Users
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At the retail level, business users were keen to analyse sales performance along different dimensions. The ability to conduct a detailed and interactive inventory and promotional analysis to spot trends, patterns and correlations was also of critical importance. Some of the specific analytical queries of interest to them included: Which are the top 30 SKUs by sales revenue, sales volume and profitability?
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Which products and customers have the highest/lowest gross margins?
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What are the key sales and gross margin trends by product type, store and geography?
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What is the total sales value of an SKU (e.g. Samsung 48’’ LED TV) this quarter? How
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does it compare against the last quarter? •
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Which products were on promotion geography, product category and month.but did not sell? Slice and dice the data by
What are my inventory turns and total days of supply? Analyse the trends by store,
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product and time.
What is the total gross margin return on investment (ROI) for an SKU (e.g. Apple iPhone
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6)? Roll up the data by product category, time and store. Warehouse Business Users
Warehouse users wanted to know the time taken to move a product from the warehouse to the store. The quick and efficient movement of products helped companies minimise inventory levels and reduce holding or carrying costs. Warehouse business users wanted to have the
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ability perform workflow and identify bottlenecks order fulfilment process.toAn illustrative list of analysis analytical queries thatthe were of interestintotheir warehouse business users included: What are the maximum, minimum and average times to ship an order from warehouse
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to retail store? Slice and dice the data by store, product category, geography and time.
What is the average time taken for the individual activities (picking, packaging,
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shipping, transportation and delivery) in the order fulfilment pipeline? What are the maximum and minimum processing time values for the individual order fulfilment activities?
What are the delivery time performance trends by month, product category and store?
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Which are the key bottleneck areas?
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What is the order delivery time performance of third-party transporters? How does it
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compare against the contractual contractual service-level agreement? Analyse their performance by time, product category and geography.
As an immediate next step, the team needed to apply the design principles of dimensional dimensional data modelling and build a data mart. The team had to consider several alternative design choices and choose the one that best addressed their business analytics requirements. Subsequently,
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other components of the data warehouse (or data mart) had to be put in place to complete the identified use cases. Conclusion: Building a Decision Support Infrastructure
Mehta and his team were preparing a plan to meet the organisation’s analytic requirements. While the team believed that a data warehouse was the best way to meet those requirements, several questions lingered in their minds. What were the potential pitfalls in integrating a warehouse system with the existing IT systems? What were the design considerations that had to be made while meeting all of the analytic requirements? How should they go about performing sizing/estimation for making decisions on the hardware infrastructure needed? What were the available architectural choices and
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which was the option that would best suit their needs? At an organisational level, what change management initiatives and communication plans would be needed for a successful implementation? Mehta carefully contemplated the above questions. He knew the answers would be critical in arranging a pilot study, preparing a detailed implementation plan and presenting the findings to Barley and other key stakeholders in July 2015, just six months away.
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A000181 Exhibit 1: Sales Invoice INVOICE
Acme Inc 345 Victoria Street
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Kolkata 702 207 Bill To Address:
Ship To Address:
Mr. Vaghela, Shankar
Mr. Vaghela, Shankar
Mint street, Kolkata
Mint street, Kolkata
Invoice No
I10005
Order No
O123456
Store No
S303
Order Date
06-Sep-14
Emp Id
E777
Promo code
None
Unit Price Serial No. 1 2
Product Description 24'' LCD TV
W1002
Extended warranty
3 4 5
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Product Code TV1001
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Amount
Qty 1
(INR) 35000
(INR) 35000
1
3000
3000
Sales Amount
38000
Sales Tax Freight Discount Total Amount
4750 300 Nil 43050
Source: Created Source: Created by the author.
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A000181 Exhibit 2: Entity-Relationship Diagram
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Sample Table Description for the E-R Diagram Table Name
Representative Attributes
CUSTOMER
Customer ID, name, address, city, state, country, pin code
ORDER
Order ID, customer ID, store ID, salesperson ID, order date, sales tax, discount, freight, amount
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STORE SALESPERSON
Store ID, name, description, location Salesperson ID, name, designation, age, date of birth, address
INVOICE
Invoice ID, customer ID, customer name, address, order ID, amount
LINE ITEM ITEM
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Line item ID, order ID, name, description, price
Item ID, product name, brand, category, department
PROMOTION
Promotion ID, promotion name, description, start date, end date
SUPPLIER
Supplier ID, supplier name, description, location
SHIPMENT
Shipment ID, shipping date, delivery date
CATEGORY
Category ID, category name, description
CUSTOMER PROFILE
Customer ID, demographic profile, psychographic profile
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Source: Created Source: Created by the author.
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12 of 15 Exhibit 3: Data Warehouse Business Matrix
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t c u d o r P
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r e m o t s u C
Retail Product Sales
X
X
X
X
Retail Inventory
X
X
X
Retail Product Receipts
X
X
X
Retail Promotions
X
X
X
X
Frequent Shopper
X
X
X
X
Retail Product Returns
X
X
X
X
Warehouse Orders
X
X
X
Warehouse Inventory
X
X
Warehouse Receipts
X
X
Warehouse Sales Forecast Purchase Order
X X
X X
Business Process
Source: Created Source: Created by the author.
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n o i t o m o r P
y h p a r g
X
X
e s u o h e r a W
r e t r o p s n a r T
r e i l p p u S
P o r o e o G
X
X
o py
X
X
X
X
X
X
X
X
X
X X
X
X
X X
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13 of 15 Exhibit 4: High-level Data Warehouse Architecture
OLAP
DATA
AD-HOC
BUSINESS
MINING
QUERY
APPLICATIONS
M
P o r o S
Enterprise Data Warehouse / Data Mart
E T A
E
C
U
R
D
I
A T
EXTRACT, TRANSFORM, LOAD
A
DB
ERP
Source: Created Source: Created by the author.
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CRM
FILES
T
Y
ERP
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A000181 Exhibit 5: OLAP Operations
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Source: Created Source: Created by the author.
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A000181 Exhibit 6: Data Warehouse Architecture Alternatives
Independent Data Mart
Data Mart with Linked Dimension
Users
Users
Users
Users
DM-Finance
DM-Marketing
DM-Finance
DM-Marketing
ETL
ETL
ETL
Source Data
P o r o ETL
Source Data
Virtual Data Warehouse
Enterprise Data Warehouse Users
Data Warehouse
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Users
Virtual Data Warehouse
ETL
Source Data
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Source Data
Enterprise Data Warehouse and Data Mart Users
DM-Marketing
DM-Finance
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Users
Users
Enterprise Data Warehouse
ETL Source Data
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