2. What is
Amazon
• Multinational technology company focusing on
• e-commerce,
• cloud computing,
• online advertising,
• digital streaming
• It was founded on July 5, 1994, by Jeff Bezos from his
garage at Washington
• Initially it was an online marketplace for books, and due to its
strategies today it has earned it the moniker "The Everything
Store"
3. Why
Amazon?
• This conglomerate is known as a disruptor of industries through
technological innovation
• As of 2023, it is the world's largest online retailer & marketplace as
measured by revenue and market share
• Revenue USD 574.8 Bn (2023)
• Employee Strength : 15,25,000 (Dec 2023)
• It is leveraging cutting-edge technologies for unparalleled Customer
Experience and Growth
• Continuously refining and developing custom solutions for optimal
efficiency
4. Few Core Business Functions of
Amazon’s e-commerce business
CUSTOMER
RELATIONSHIP
MANAGEMENT
(CRM)
INVENTORY
MANAGEMENT
SUPPLY CHAIN
MANAGEMENT
FINANCE &
ACCOUNTING
MARKETING &
SALES
Each of above processes are integrated
Each function has a self-contained information
systems, which works as sub-systems of overall
larger Business system
These information systems help taking
decisions at :
Operational Level (Op),
Management Level (M) &
Strategic Planning Level (S)
1 2 3 4 5
5. 1. Customer Relationship Management (CRM)
Systems capture customer
data, personalize
interactions, recommend
products, analyze feedback
to improve customer
satisfaction and loyalty.
Strong possibility of using
Salesforce CRM for
centralized customer data,
marketing automation, and
personalized interactions.
Example: recommendation
engines, chatbots, sentiment
analysis
Potential for custom-
developed solutions or
integrations with other tools
for specific needs.
6. MIS on CRM – How does it works
Input Data
• Customer Data: From
various sources like website
interactions, app usage,
purchase history, contact
center calls & social media.
• Product Data: Product
descriptions, pricing,
promotions, availability, and
customer reviews.
• Sales and Marketing Data:
Campaign performance, lead
generation, and customer
acquisition costs.
• Feedback & Complaints:
Customer surveys, social
media mentions and contact
center interactions
expressing satisfaction or
dissatisfaction.
• External Data: Market
trends, competitor analysis,
and social media sentiment.
Processes
• Customer Segmentation:
Customers are grouped based on
demographics, purchase history,
interests etc for targeted
marketing and interactions.
• Lead Management: Potential
customers are nurtured through
email campaigns, personalized
recommendations & targeted
offers based on their interests.
• Sales Automation: Repetitive
tasks like order confirmation,
shipping notifications, customer
inquiries are automated.
• Personalized Service: Service
agents have 360-degree view,
enabling personalized interactions
& tailored recommendations.
• Data Analysis: Reports and
dashboards provide insights into
customer behavior, campaign
effectiveness, loyalty program &
areas for improvement.
Output Information
• Personalized Marketing
Campaigns (S): Email
marketing, social media ads,
and product recommendations
tailored for individual customer.
• Loyalty Programs (M) : Track
points, customer engagement,
and offer customized rewards.
• Customer Service Efficiency
(Op): Improved response times,
reduced resolution times, and
personalized solutions.
• Customer Satisfaction
Insights (M) : Identify areas for
improvement based on churn
data, feedback & sentiments.
• Recommendations (S) : Data-
driven insights to improve
marketing campaigns,
personalize customer
experiences, and optimize
loyalty programs for increased
engagement and retention.
7. 2. Inventory Management
Systems predicting demand and
optimizing stock levels across
warehouses.
Real-time tracking of inventory to
ensure product availability.
Custom-developed system for
core functions like stock levels,
purchasing, and warehouse
operations.
Examples: Forecasting software,
inventory optimization tools.
Demand forecasting engine using
machine learning (ML) algorithms.
Warehouse Management System
optimized for efficiency and
speed, potentially leveraging
warehouse automation tools from
companies like Kiva or
GreyOrange.
8. MIS on Inventory Mgt- How does it work
Input Data
• Sales Data: Sale
transactions, website activity,
order history, and sales
trends
• Supplier Data: Lead times,
pricing, minimum order
quantities, and product
availability from various
suppliers
• Warehouse Data: Real-time
inventory levels, storage
capacity, location specifics,
and handling requirements
• External Data: Economic
indicators, competitor pricing,
weather patterns, and social
media trends influencing
demand
• User Input: Replenishment
thresholds, safety stock
levels, order fulfillment
priorities, and promotional
planning set by users
Processes
• Demand Forecasting: Algorithms
& ML analyze sales data, external
factors, historical trends to predict
future demand.
• Inventory Optimization: Based
on above, system determines the
optimal stock level to balance
costs and availability.
• Replenishment Planning:
Automatic purchase orders are
triggered based on safety stock
levels.
• Warehouse Management:
Optimizes picking routes, allocates
storage locations, and coordinated
systems for efficient order
fulfillment
• Data Analysis: Real-time reports
provide insights into inventory
levels, turnover rates, carrying
costs, and potential bottlenecks.
Output Information
• Purchase Orders (Op):
Generated automatically to
ensure timely product
availability at all warehouses
• Picking Lists and
Warehouse Instructions
(OP): Optimized instructions
to efficiently pick and pack
orders, considering product
location, order priority, and
handling requirements.
• Inventory Dashboards (M):
Visualizes inventory turnover,
carrying costs, order
fulfillment efficiency, and
potential stockouts
• Alerts and Notifications
(M): Flag potential issues like
low stock, supplier delays
• Replenishment
Suggestions (M): Suggest
adjustments based on actual
demand patterns
9. 3. Supply Chain Management
Streamlines logistics and
optimizing delivery routes for
efficiency.
Monitors performance metrics for
cost-effective and efficient
deliveries.
Custom-developed platform for
logistics, transportation, and
network optimization.
Examples: Transportation
management systems, route
optimization algorithms.
Route optimization engine likely
incorporating AI/ML for efficient
delivery routes.
Transportation Management
Systems from industry, like
Manhattan Associates or BluJay
Solutions for carrier selection,
freight rates, and shipment
tracking.
10. MIS on Supply Chain Mgt - How does it work
Input Data
•Order Data: Customer orders
and forecasts from various
channels & Sources (i.e.
website, app, stores).
• Inventory Data: Real-time
inventory levels across
warehouses and fulfillment
centers.
• Supplier Data: Lead times,
pricing, delivery routes, and
capacity information.
• Transportation Data:
Available transportation
modes, costs, and delivery
times for different routes.
• External Data: Weather
forecasts, fuel prices, traffic
conditions, and geopolitical
events impacting logistics.
• User Input: Route preference,
delivery speed expectations,
and cost optimization
parameters set by users.
Processes
• Order Management: Orders are
automatically routed to the optimal
fulfillment center based on
inventory availability, delivery
speed, and cost considerations.
• Transportation & Logistic
Planning: Selects the most
efficient and cost-effective
transportation mode (air, truck,
ship). Track shipments in real-time,
adjust routes based on disruptions
and exceptions
• Warehouse Management: The
system optimizes picking, packing,
and shipping processes within
warehouses, considering order
urgency, product characteristics,
and resource availability.
• Performance Analysis: Reports
and dashboards provide insights
into key metrics like delivery times,
transportation costs, and on-time
fulfillment rates.
Output Information
• Fulfillment & Transport
Instructions: Instructions for
warehouse personnel to pick,
pack, and ship orders.
Optimized routes and carrier
selection for each shipment.
• Shipment Tracking
Information: Real-time
visibility on shipment location,
expected arrival time, and
potential delays.
• Performance Reports and
Dashboards: Visualize
shipping costs, delivery times,
order fulfillment efficiency &
identify improvement areas.
• Cost Optimization
Recommendations:
Suggestions for adjusting
transportation modes,
delivery routes, and inventory
levels based on changing
demand & market conditions.
11. 4. Finance & Accounting
Systems track financial
transactions, generate reports,
analyze financial performance,
and automate accounting
processes.
Enterprise Resource Planning
(ERP) systems, potentially
supplemented by custom
solutions, track financial
transactions, generate reports,
and automate accounting
processes
Examples: enterprise resource
planning (ERP) systems, financial
reporting tools
Potential for custom-developed
solutions or integrations with other
tools for specific needs.
12. MIS on Finance & A/c – How does it work
Input Data
• Transaction Data: Sales
invoices, purchase orders,
payroll information, tax
documents etc.
• Inventory Data: Product
costs, inventory levels, and
depreciation calculations.
• Customer Data: Billing
information, payment
records, creditworthiness
assessments.
• Supplier Data: Invoices,
payment terms, and
contract details.
• External Data: Currency
exchange rates, economic
indicators, and regulatory
updates.
• Budgets and Forecasts:
Financial plans, sales
projections, and expense
estimates.
Processes
• Transactions Recording &
General Ledger Management:
Tracks all financial transactions
in a centralized system for
accurate reporting and analysis.
• Financial Reporting & Tax
filings: Generates financial
statements like income
statements, balance sheets,
cash flow statements & Tax
calculations.
• Budgeting and Forecasting:
Compares actual performance
to budgets and forecasts &
identifies variances.
• Cost Management: Tracks and
analyzes costs across
departments and projects to
identify areas for optimization.
• Financial Analysis: Provides
insights into financial
performance, profitability,
liquidity, and solvency.
Output Information
• Financial Statements &Tax
Complainces (Op): Accurate and
timely financial reports for internal
stakeholders, investors,
regulators & authorities.
• Budgeting and Forecasting
Reports (M): Identify variances
between actuals and plans,
enabling informed decision-
making.
• Cost Analysis Reports (M):
Highlight areas for cost reduction
and improve operational
efficiency.
• Financial Performance
Dashboards (M): Real-time
visibility into key financial metrics
like revenue, expenses, and
profitability.
• Investment Analysis Reports
(S): Support informed investment
decisions by analyzing potential
risks and returns.
13. 5. Marketing & Sales
MIS analyzes customer
behavior, identifies target
audiences, personalizes
marketing campaigns, and
measures campaign
effectiveness.
MIS analyzes customer
behavior, identifies target
audiences, personalizes
marketing campaigns, and
measures campaign
effectiveness.
Example: marketing
automation platforms, A/B
testing tools.
Marketing automation
platforms and A/B testing
tools enable data-driven
decisions and campaign
optimization.
14. Marketing & Sales – How does it work
Input Data
• Customer Data:
Demographics, purchase
history, website interaction,
app usage, and preferences
from CRM & loyalty
programs.
• Product Data: Information
on features, pricing,
promotions, inventory levels,
and reviews.
• Sales Data: Historical sales
performance, campaign
results, lead generation data,
and conversion rates.
• Market Data: Industry
trends, competitor analysis,
economic indicators, and
social media sentiment.
• External Data: Weather
patterns, cultural events, and
other factors potentially
impacting consumer
behavior.
Processes
• Campaign Planning and
Management: Develops targeted
marketing campaigns based on
customer segments & objectives.
• Marketing Automation: Uses
automated tools to trigger
personalized emails, promotions,
and recommendations based on
customer behavior & interactions.
• Sales Lead Generation and
Nurturing: Identifies potential
customers, qualifies leads, and
nurtures them through targeted
campaigns and interactions.
• Sales Pipeline Management:
Tracks leads through the sales
funnel, manages opportunities,
and forecasts sales based on data
analysis.
Output Information
• Targeted Marketing
Campaigns (M): Personalized
advertising, email marketing,
social media content, website
recommendations etc.
• Content Distribution (M):
Publishes and promotes content
based on performance data &
customer engagement.
• Marketing Performance
Reports (S): Measures
campaign effectiveness, tracks
ROI, click-through rates, and
conversion rates.
• Sales Leads and Pipeline
Insights (Op): Qualified leads
passed to sales teams, data-
driven insights for optimizing
sales strategies.
• Sales Team Insights (M): Sale
performance, lead conversion
rates, targeted coaching for
performance improvement.
15. Future Outlook
Input Data
• Massive data stream:
Internal: Sales, surveys,
complains, social media,
inventory & logistics etc
External: Market trends,
competitor analysis,
weather patterns, economic
indicators etc.
Processes
• Big Data
• Machine Learning & Artificial
Intelligence
• Cloud Computing
• Data Warehouse
• Data Visualization
Output Information
• Actionable Insights
(S+M+Op):
• Data-driven insights,
• Automated decisions,
• Predictive models,
• Preventive solutions etc
Emerging Technologies to uncover hidden insights & predict future
Personal search results, Automated query resolutions, Dynamic pricing
Business Intelligence & Collaborative Platforms
Regulatory compliances
Ethical boundaries on usage of personal information
Environmental impact due to advance technologies
