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How does MIS at Amazon work - explained in detail
- 1. How Information Systems helped Amazon grow 1995 2000-Present Name Roll No. Avinash Bhor 21-F-203 Abhijeet Bhosale 21-F-204 Bhavin Doshi 21-F-227
- 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