Building a Smart Supply Chain Intelligence Platform on AWS: Turning Business Data into Better Decisions

What if your business could see inventory shortages before they become a problem, identify unreliable suppliers, and understand procurement spending from one intelligent dashboard?
Muhammad Umair Ashraf

Cloud Data Engineer
Published Nov 11, 2026
Last modified Nov 11, 2026
Supply chains generate enormous amounts of data every day. Procurement systems record purchase orders, warehouses track stock movements, sales platforms capture customer demand, and suppliers provide delivery and pricing information.
But in many organizations, this information is scattered across different applications, spreadsheets, databases, and warehouse management systems.
The result is a familiar business problem: decision-makers cannot easily see the complete picture.
A procurement manager may not know which supplier frequently delivers late. A warehouse manager may have excess stock in one location while another warehouse is running out. A finance team may struggle to understand how procurement costs change over time.
What if we could bring all this information together and turn it into actionable intelligence?
In this article, we'll explore how to build a Smart Supply Chain Intelligence Platform on AWS using Amazon S3, AWS Glue, AWS Lambda, AWS Step Functions, AWS Lake Formation, AWS Glue Data Catalog, Amazon Athena, and Amazon QuickSight.
The objective is to create a centralized data platform that combines procurement, inventory, supplier, sales, and warehouse information to generate inventory insights, supplier scorecards, procurement analytics, and operational dashboards.
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1. The Business Problem: Supply Chain Data Is DisconnectedImagine a manufacturing or distribution company operating across multiple warehouses.
It uses:
An ERP system for purchasing and financial records.
A warehouse management system for stock movements.
A procurement application for purchase orders.
A sales platform for customer orders and revenue.
Supplier databases for vendor information.
Excel spreadsheets for operational reporting.
Each system contains useful information, but the data is not necessarily connected.
This creates several challenges.
Inventory visibility: Managers may not know the actual stock position across all warehouses.
Supplier performance: Procurement teams may struggle to compare delivery reliability, pricing, and order fulfillment.
Procurement costs: Finance teams may need to combine multiple reports to understand spending trends.
Demand planning: Sales trends and inventory levels may not be analyzed together.
Warehouse efficiency: Excess inventory, slow-moving products, and stock shortages can remain hidden.
Reporting delays: Teams spend time collecting and cleaning spreadsheets instead of analyzing business performance.
A centralized supply chain intelligence platform can help solve these problems by integrating business data into a common analytical environment.
2. What Are We Building?The proposed platform is a centralized data lake and analytics solution.
It collects data from different business systems, stores the original information, cleans and transforms datasets, and creates business-ready analytical tables.
Users can then query the data and view insights through dashboards.
The platform focuses on five major business areas:
Procurement analytics: Purchase orders, purchasing costs, spending trends, and supplier pricing.
Inventory intelligence: Stock levels, inventory value, low-stock products, and slow-moving inventory.
Supplier scorecards: On-time delivery, order fulfillment, quality, and supplier performance.
Sales analytics: Product demand, revenue trends, and sales performance.
Warehouse analytics: Stock distribution, inventory movement, and warehouse utilization.
The most important idea is that all these areas use consistent, governed datasets instead of separate reports that may disagree with each other.
3. AWS Architecture: Centralized Supply Chain Data LakeThe architecture follows a layered data lake approach.
At a high level, the workflow is:
Collect data from ERP, procurement, sales, supplier, and warehouse systems.
Land source data in Amazon S3.
Use AWS Glue and AWS Lambda to process and validate datasets.
Store cleaned data in a structured S3 layer.
Create analytical datasets for business reporting.
Register schemas and metadata in AWS Glue Data Catalog.
Govern access using AWS Lake Formation.
Query datasets using Amazon Athena.
Visualize business insights using Amazon QuickSight.
Monitor and orchestrate data pipelines using AWS services.
Architecture overview
             BUSINESS DATA SOURCES
  +------------------------------------------+
  | ERP | Procurement | Inventory | Sales    |
  | Suppliers | Warehouse Management Systems|
  +------------------------------------------+
                       |
                       v
             DATA INGESTION LAYER
        APIs | Database Exports | CSV | JSON
                       |
                       v
                AMAZON S3
                LANDING / RAW
             Original Source Data
                       |
                       v
             PROCESSING & ETL
          AWS Glue (PySpark ETL)
          AWS Lambda (Lightweight
             Validation / Events)
                       |
                       v
                AMAZON S3
              CLEANSED / SILVER
          Standardized and Validated Data
                       |
                       v
             ANALYTICS TRANSFORMATION
                 AWS Glue
                       |
                       v
                AMAZON S3
              ANALYTICS / GOLD
             Business-Ready Tables
                       |
              +--------+---------+
              |                  |
              v                  v
       AWS Glue Data        AWS Lake Formation
          Catalog          Governance & Access
              |                  |
              +--------+---------+
                       |
                       v
                 AMAZON ATHENA
                  SQL Analytics
                       |
                       v
               AMAZON QUICKSIGHT
              Dashboards & Reports
                       |
                       v
              SUPPLY CHAIN USERS
       Procurement | Finance | Warehouse
              Management | Executives
﻿
       Supporting Services:
       Step Functions | CloudWatch | IAM | KMS
This is a reference architecture. The exact ingestion method depends on the source system, data volume, update frequency, and available connectivity.
AWS Prescriptive Guidance describes a modern data architecture in which Amazon S3 provides the storage layer, AWS Glue supports data integration and cataloging, Lake Formation provides governance, and services such as Athena and QuickSight support analytics and visualization.  https://docs.aws.amazon.com/prescriptive-guidance/latest/strategy-aws-data/aws-architecture.html 
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4. Step One: Collect Data from Business SystemsThe first challenge is bringing data from different business applications into one platform.
A typical supply chain environment may have the following sources.
ERP systems
Product master data.
Purchase orders.
Supplier information.
Financial transactions.
Goods receipts.
Inventory systems
Current stock levels.
Stock adjustments.
Inventory movements.
Reorder points.
Warehouse balances.
Procurement systems
Purchase requisitions.
Purchase order history.
Procurement expenses.
Contract information.
Supplier pricing.
Sales systems
Customer orders.
Product sales.
Revenue.
Order fulfillment.
Sales returns.
Warehouse management systems
Warehouse locations.
Product movements.
Receiving and dispatch records.
Picking and packing information.
Storage utilization.
Data can arrive through REST APIs, scheduled database exports, CSV files, JSON files, or supported database integration mechanisms.
For example, an organization might export daily purchase orders as CSV and upload them to S3, while another source provides supplier information through an API.
The ingestion layer should preserve source identifiers, extraction timestamps, and batch information. This helps trace analytical records back to their original systems.
For a first implementation, scheduled batch ingestion is a practical starting point. If the business later requires frequent inventory updates, a streaming ingestion path can be introduced.
5. Step Two: Use Amazon S3 as the Central Data LakeAmazon S3 acts as the foundation of the platform.
Instead of storing every dataset in a separate analytics database, the platform can use S3 to store source data, cleaned datasets, and business-ready analytical tables.
A suggested structure is:
smart-supply-chain-platform/
│
├── landing/
│   ├── erp/
│   ├── procurement/
│   ├── inventory/
│   ├── sales/
│   ├── suppliers/
│   └── warehouse/
│
├── cleansed/
│   ├── products/
│   ├── purchase_orders/
│   ├── inventory/
│   ├── sales/
│   ├── suppliers/
│   └── warehouses/
│
└── analytics/
    ├── fact_inventory/
    ├── fact_purchases/
    ├── fact_sales/
    ├── fact_deliveries/
    ├── dim_products/
    ├── dim_suppliers/
    ├── dim_warehouses/
    └── dim_date/
This design follows a medallion-style organization:
Landing / Bronze layer
Stores the original source data with minimal modification. It provides traceability and allows data to be reprocessed when transformation logic changes.
Cleansed / Silver layer
Contains standardized and validated datasets. This is where the platform handles missing values, inconsistent product identifiers, duplicate records, date formats, and data type differences.
Analytics / Gold layer
Contains business-ready tables and aggregations designed for reporting and analysis.
For analytical datasets, Apache Parquet is a useful storage format because it is columnar and works well with analytical query engines such as Athena.
Partitioning can also improve query efficiency. For example, a purchase dataset might be organized by year and month:
analytics/fact_purchases/
    year=2026/
        month=10/
            part-0001.parquet
Partitioning should reflect common query patterns. Creating too many small partitions or very small files can make the data lake less efficient.
6. Step Three: Process and Transform Data with AWS GlueData from different systems rarely follows the same structure.
One system might identify a product as  PROD-1001 , while another uses  1001 . Supplier names may have different spellings, dates may use different formats, and currencies may need normalization.
AWS Glue can process these datasets and transform them into consistent structures.
For example, a Glue ETL job can:
Read CSV, JSON, and other supported source formats.
Standardize column names and data types.
Validate required fields.
Normalize product and supplier identifiers.
Remove or flag duplicate records.
Handle invalid or incomplete data.
Join related datasets.
Calculate business metrics.
Write optimized Parquet datasets to S3.
Example: Standardizing procurement data with PySparkConsider a purchase-order dataset containing inconsistent column names.
A simplified AWS Glue PySpark transformation could look like this:
from pyspark.sql import functions as F
﻿
purchase_orders = (
    spark.read
    .option("header", True)
    .csv("s3://smart-supply-chain-platform/landing/procurement/")
)
﻿
cleaned_orders = (
    purchase_orders
    .withColumnRenamed("PO Number", "purchase_order_id")
    .withColumnRenamed("Supplier ID", "supplier_id")
    .withColumnRenamed("Order Date", "order_date")
    .withColumnRenamed("Total Amount", "total_amount")
    .withColumn(
        "order_date",
        F.to_date("order_date", "yyyy-MM-dd")
    )
    .withColumn(
        "total_amount",
        F.col("total_amount").cast("decimal(18,2)")
    )
    .filter(F.col("purchase_order_id").isNotNull())
    .filter(F.col("supplier_id").isNotNull())
)
﻿
(
    cleaned_orders
    .write
    .mode("append")
    .partitionBy("year", "month")
    .parquet(
        "s3://smart-supply-chain-platform/cleansed/purchase_orders/"
    )
)
This is an illustrative transformation, not a complete production Glue job. The source schema, date formats, partition columns, deduplication keys, and write strategy must be adapted to the actual datasets.
For production pipelines, define how to handle invalid records, late-arriving data, schema changes, and repeated ingestion batches. Avoid blindly appending data when a source can resend records.
7. Step Four: Automate Data PipelinesA supply chain platform may process many datasets at different times.
For example:
Inventory data may arrive every hour.
Sales data may arrive throughout the day.
Purchase orders may be exported daily.
Supplier performance data may be refreshed weekly.
Warehouse data may arrive after each operational shift.
AWS Step Functions can coordinate multi-step processing workflows.
A typical pipeline could look like this:
Scheduled Trigger / New Data Arrival
                 |
                 v
          Start Workflow
                 |
                 v
        Validate Source Files
                 |
                 v
          Run AWS Glue ETL
                 |
                 v
        Check Data Quality
                 |
          +------+------+
          |             |
        Valid         Invalid
          |             |
          v             v
    Update Catalog   Quarantine Data
          |
          v
     Build Analytics
          |
          v
      Publish Results
          |
          v
     Notify Data Team
AWS Lambda can handle lightweight tasks such as validating incoming file metadata, checking expected file names, or starting a workflow.
AWS Glue should perform larger data transformations, while Step Functions coordinates the workflow and its error-handling paths.
Amazon CloudWatch can monitor job failures, workflow execution, processing duration, and operational metrics.
8. Step Five: Create a Centralized Data Catalog and Governance LayerA data lake is useful only when users can discover datasets and access them securely.
AWS Glue Data Catalog maintains metadata such as:
Database names.
Table definitions.
Column names.
Data types.
S3 locations.
Partition information.
AWS Glue Crawlers can help discover schemas and update catalog metadata, although production pipelines should use controlled schema-management practices rather than assuming every detected schema change should be accepted automatically.
AWS Lake Formation provides centralized data lake governance and fine-grained permissions for data stored in S3 and metadata in the Glue Data Catalog.
For example, an organization may define access such as:
Procurement analysts can access purchase-order and supplier datasets.
Warehouse managers can access inventory and warehouse datasets.
Finance analysts can access approved procurement-cost and sales datasets.
Executives can access aggregated business metrics.
Lake Formation supports fine-grained access controls, including database, table, column, row, and cell-level permissions in supported integrations.  https://docs.aws.amazon.com/lake-formation/latest/dg/ 
For organizations operating across multiple business units or AWS accounts, centralized governance and controlled data sharing can help reduce duplicated data-management effort.  https://docs.aws.amazon.com/prescriptive-guidance/latest/data-lake-for-growth-scale/introduction.html 
9. Step Six: Analyze Supply Chain Data with Amazon AthenaOnce datasets are cleansed, cataloged, and available in S3, Amazon Athena can query them using SQL.
This allows analysts to investigate supply chain performance without first loading every dataset into a traditional database cluster.
Let's explore some useful analytical questions.
Inventory value by warehouse
SELECT
    warehouse_id,
    SUM(quantity_on_hand * unit_cost) AS inventory_value
FROM fact_inventory
GROUP BY warehouse_id
ORDER BY inventory_value DESC;
This query calculates inventory value by warehouse, assuming the dataset contains a consistent unit cost and quantity measure.
Products below reorder level
SELECT
    product_id,
    warehouse_id,
    quantity_on_hand,
    reorder_level
FROM fact_inventory
WHERE quantity_on_hand < reorder_level
ORDER BY quantity_on_hand ASC;
This helps identify products that may require replenishment.
Supplier on-time delivery performance
SELECT
    supplier_id,
    COUNT(*) AS total_orders,
    SUM(
        CASE
            WHEN actual_delivery_date <= expected_delivery_date
            THEN 1
            ELSE 0
        END
    ) AS on_time_orders,
    ROUND(
        100.0 * SUM(
            CASE
                WHEN actual_delivery_date <= expected_delivery_date
                THEN 1
                ELSE 0
            END
        ) / COUNT(*),
        2
    ) AS on_time_delivery_percentage
FROM fact_deliveries
WHERE actual_delivery_date IS NOT NULL
GROUP BY supplier_id
ORDER BY on_time_delivery_percentage DESC;
This example measures on-time delivery among records with an actual delivery date. A production scorecard should also define how partial deliveries, cancelled orders, revised delivery dates, and incomplete records are treated.
Monthly procurement spending
SELECT
    year,
    month,
    SUM(total_amount) AS procurement_spend
FROM fact_purchases
GROUP BY year, month
ORDER BY year, month;
These queries are starting points. The actual table and column names should match the curated datasets registered in the Glue Data Catalog.
10. Step Seven: Build Business Dashboards with Amazon QuickSightSQL queries are valuable for analysts, but business managers often need visual dashboards.
Amazon QuickSight can turn analytical datasets into interactive reports for different business roles.
The platform could include several dashboard sections.
Inventory Intelligence DashboardDisplay:
Total inventory value.
Stock by warehouse.
Low-stock products.
Excess inventory.
Slow-moving products.
Inventory aging.
Stock movement trends.
Supplier Performance DashboardDisplay:
Active suppliers.
On-time delivery percentage.
Average delivery delay.
Purchase-order fulfillment.
Quality or rejection rate, when available.
Supplier spending trends.
Procurement Analytics DashboardDisplay:
Total procurement spending.
Monthly purchase trends.
Spending by supplier.
Spending by product category.
Purchase-order volume.
Price changes over time.
Sales and Demand DashboardDisplay:
Revenue by product.
Units sold.
Monthly sales trends.
Product demand by location.
Returns and order fulfillment.
Warehouse Operations DashboardDisplay:
Inventory distribution.
Receiving and dispatch volume.
Stock transfers.
Warehouse-level stock shortages.
Storage utilization, when available.
The important part is to create consistent business definitions. For example, the organization should define exactly how it calculates inventory value, on-time delivery, and slow-moving inventory so that different dashboards do not report conflicting figures.
11. What Would the Actual Product Look Like?Imagine a procurement manager logging into a web-based Supply Chain Intelligence application.
The dashboard could display:
Smart Supply Chain Intelligence
<text>Illustrative dashboard data — not live AWS metrics.</text>
Business metric
Example value
Total inventory value
$245,800
Active suppliers
128
Low-stock products
23
On-time deliveries
94.5%
Monthly procurement spend
$86,400
The manager could select a warehouse, supplier, product category, or date range to investigate the numbers.
For example, selecting a low-stock product could reveal:
Current quantity.
Reorder threshold.
Average daily sales.
Open purchase orders.
Supplier lead time.
Stock availability at other warehouses.
This turns a dashboard from a collection of charts into a tool for investigating operational problems.
The figures above are sample values for illustrating the interface. They are not measured results or live data.
12. Supplier Scorecards: Measuring Supplier ReliabilityOne of the most useful capabilities of this platform is supplier performance analysis.
A supplier scorecard can combine several measurable indicators:
On-time delivery
Percentage of eligible purchase orders delivered on or before the agreed date.
Order fulfillment
Percentage of ordered quantities successfully delivered.
Quality performance
Percentage of received units accepted, based on inspection or rejection records.
Lead time
Average time between purchase-order confirmation and receipt.
Price consistency
How supplier prices change across comparable products and periods.
For example, an illustrative scorecard might look like this:
Supplier
On-time delivery
Fulfillment
Supplier A
98%
99%
Supplier B
91%
96%
Supplier C
84%
90%
These are example figures, not real supplier performance data.
A supplier score should not rely on one metric alone. Procurement teams should define weights, thresholds, and exception rules based on their business requirements.
13. Inventory Intelligence: From Reporting to Better PlanningA centralized data platform can help answer more advanced inventory questions.
For example:
Which products are at risk of stockout?
Combine current inventory, sales velocity, open purchase orders, and supplier lead time.
Which products are moving slowly?
Compare stock levels with historical sales or consumption over a defined period.
Where is inventory concentrated?
Compare inventory value and quantity across warehouses.
How much should we reorder?
Use demand history, lead time, safety-stock policy, and expected demand to support replenishment planning.
A simple reorder-point calculation can be expressed as:
Reorder Point =
(Average Daily Demand × Supplier Lead Time)
+ Safety Stock
For example, if a product has average daily demand of 20 units, a supplier lead time of 10 days, and safety stock of 50 units:
Reorder Point = (20 × 10) + 50
﻿
Reorder Point = 250 units
When available inventory approaches this level, the system can flag the product for replenishment review.
This is a basic planning formula. Real inventory policies may also need to consider demand variability, seasonal patterns, service-level targets, minimum order quantities, and supplier constraints.
14. Extending the Platform with Real-Time Inventory TrackingThe core architecture described here is primarily a centralized data lake and batch analytics platform.
That is appropriate for many procurement reports, supplier scorecards, and historical inventory analyses.
However, some businesses need near-real-time inventory updates.
For example, a warehouse employee scans a product barcode or RFID tag when stock moves between locations.
The event can be sent through an API or an IoT ingestion layer.
A possible extension is:
Barcode / RFID Scanner
          |
          v
      Warehouse App
          |
          v
     API / AWS IoT Core
          |
          v
   Amazon Kinesis Data Streams
          |
          v
     Stream Processing
          |
          v
      Amazon S3
          |
          v
  Supply Chain Analytics
AWS IoT Core can support connected device ingestion, while Kinesis Data Streams can handle event streams.
This real-time extension should complement the data lake rather than replace it. Historical and curated datasets can still be stored in S3 and analyzed through Athena and QuickSight.
15. Production Architecture: Security, Reliability, and CostA production supply chain platform needs more than storage and dashboards.
Data securityUse IAM roles and least-privilege policies for ingestion and processing components.
Use AWS KMS encryption for data at rest where appropriate, and protect data in transit.
Restrict access to sensitive supplier pricing, financial records, and customer information.
Data governanceUse AWS Lake Formation to manage fine-grained access to supported data lake resources.
For a multi-tenant SaaS product, design tenant isolation deliberately. Consider separate S3 prefixes or buckets, tenant-aware catalog and access policies, and strict application-level authorization. Do not rely only on dashboard filters to separate customers' data.
Data qualityValidate incoming data before publishing it to analytical tables.
Track:
Missing required fields.
Invalid product identifiers.
Duplicate source records.
Unexpected schema changes.
Invalid dates and numeric values.
Reconciliation differences between source systems.
Keep rejected records in a controlled quarantine area for investigation.
ReliabilityUse Step Functions to coordinate multi-stage workflows, define retries and timeouts, and route failures to appropriate recovery paths.
Make ingestion idempotent so that repeated source files or API responses do not inflate business metrics.
MonitoringUse CloudWatch logs, metrics, and alarms to monitor Glue job failures, workflow executions, data freshness, and query activity.
Track pipeline freshness so users know when a dashboard was last updated.
Cost optimizationUse Parquet and appropriate compression for analytical datasets.
Partition data according to common query filters, but avoid excessive small files and unnecessary partitions.
Use Athena queries that select only the columns and time periods required.
Choose Glue job capacity and scheduling based on actual workload measurements.
The AWS modern data architecture guidance emphasizes choosing services according to functionality, scalability, data latency, resilience, operational effort, and cost—not simply adding every available service.  https://docs.aws.amazon.com/prescriptive-guidance/latest/strategy-aws-data/aws-architecture.html 
16. A Practical Implementation RoadmapThe project can be developed in phases.
Phase 1: Build the data lake
Create the S3 bucket and landing, cleansed, and analytics layers.
Define source data formats.
Upload sample ERP, procurement, inventory, and supplier datasets.
Phase 2: Build ETL pipelines
Create AWS Glue jobs.
Standardize identifiers and data types.
Add data-quality checks.
Write cleaned Parquet datasets.
Phase 3: Add cataloging and governance
Create Glue Data Catalog databases and tables.
Configure Lake Formation permissions.
Define access for procurement, finance, warehouse, and management teams.
Phase 4: Build analytics
Create curated fact and dimension datasets.
Register them in the Glue Data Catalog.
Query them through Athena.
Validate business calculations with sample data.
Phase 5: Build dashboards
Connect QuickSight to the analytical datasets.
Create inventory, supplier, procurement, and warehouse dashboards.
Add filters for dates, products, suppliers, and warehouse locations.
Phase 6: Add automation and monitoring
Orchestrate workflows with Step Functions.
Use Lambda for lightweight event-driven tasks.
Monitor jobs and data freshness through CloudWatch.
Phase 7: Extend the platform
Add real-time inventory events if required.
Introduce forecasting using historical demand data.
Add supplier risk indicators and automated alerts.
Integrate additional ERP and warehouse systems.
Starting with batch analytics keeps the first version manageable. Real-time ingestion and predictive capabilities can be added when the business requirements justify them.
Conclusion: Turning Supply Chain Data into Business IntelligenceA Smart Supply Chain Intelligence Platform is more than a reporting dashboard.
It is a centralized data foundation that connects procurement, inventory, suppliers, sales, and warehouse operations.
Amazon S3 provides the data lake storage. AWS Glue processes and catalogs the datasets. AWS Lambda supports lightweight automation. Step Functions coordinates workflows. Lake Formation helps govern data access. Amazon Athena provides SQL analytics, and Amazon QuickSight turns the results into dashboards that business teams can use.
The biggest opportunity is not simply collecting more data. It is connecting information that was previously isolated and making it useful for everyday business decisions.
A procurement manager should be able to understand supplier reliability. A warehouse manager should be able to identify stock shortages. A finance team should be able to analyze purchasing costs. Executives should be able to see the overall health of the supply chain.
The real value of a modern data platform is turning disconnected business systems into one trusted source of operational intelligence.
With AWS, organizations can build that foundation incrementally—starting with a centralized data lake and growing toward more automated, predictive, and intelligent supply chain operations.
References and Further Reading AWS Prescriptive Guidance: AWS Modern Data Architecture 
 AWS Lake Formation Developer Guide 
 AWS Prescriptive Guidance: Designing a Data Lake for Growth and Scale 
 AWS Glue Documentation 
 Amazon S3 Documentation 
 Amazon Athena Documentation 
 Amazon QuickSight Documentation 
 AWS Step Functions Documentation 
 AWS IoT Core Documentation 
#AWS #SupplyChain #DataEngineering #DataLake #AmazonS3 #AWSGlue #AmazonAthena #AmazonQuickSight #CloudComputing #BusinessIntelligence
Business metric	Example value
Total inventory value	$245,800
Active suppliers	128
Low-stock products	23
On-time deliveries	94.5%
Monthly procurement spend	$86,400
Supplier	On-time delivery	Fulfillment
Supplier A	98%	99%
Supplier B	91%	96%
Supplier C	84%	90%
# aws-ambassador # aws-builders # aws-community # supplychain # aws-supply-chain
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Building a Smart Supply Chain Intelligence Platform on AWS: Turning Business Data into Better Decisions

Muhammad Umair Ashraf

1. The Business Problem: Supply Chain Data Is Disconnected

2. What Are We Building?

3. AWS Architecture: Centralized Supply Chain Data Lake

Architecture overview

4. Step One: Collect Data from Business Systems

5. Step Two: Use Amazon S3 as the Central Data Lake

6. Step Three: Process and Transform Data with AWS Glue

Example: Standardizing procurement data with PySpark

7. Step Four: Automate Data Pipelines

8. Step Five: Create a Centralized Data Catalog and Governance Layer

9. Step Six: Analyze Supply Chain Data with Amazon Athena

Inventory value by warehouse

Products below reorder level

Supplier on-time delivery performance

Monthly procurement spending

10. Step Seven: Build Business Dashboards with Amazon QuickSight

Inventory Intelligence Dashboard

Supplier Performance Dashboard

Procurement Analytics Dashboard

Sales and Demand Dashboard

Warehouse Operations Dashboard

11. What Would the Actual Product Look Like?

12. Supplier Scorecards: Measuring Supplier Reliability

13. Inventory Intelligence: From Reporting to Better Planning

14. Extending the Platform with Real-Time Inventory Tracking

15. Production Architecture: Security, Reliability, and Cost

Data security

Data governance

Data quality

Reliability

Monitoring

Cost optimization

16. A Practical Implementation Roadmap

Conclusion: Turning Supply Chain Data into Business Intelligence

References and Further Reading
