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When a grocery eCommerce platform faced challenges in managing their high volume orders, here’s how INTECH improved their operational efficiency, reduced errors, and enhanced customer satisfaction through automation and real-time tracking.

About the Client

The client is a leading grocery eCommerce platform that aims to provide seamless online shopping for fresh and packaged groceries. 

 

With an increasing shift in consumer preference towards home delivery, the client needed a robust solution to manage their inventory, streamline order processing, and optimize delivery logistics.

Client’s Challenge

As the demand for online grocery shopping increased, the client struggled with inefficiencies in their order fulfillment process, warehouse operations, and last-mile delivery. 

 

Their key challenges included:

 

1. High Order Volumes:

 

The platform struggled to manage an increasing number of online orders efficiently.

 

2. Inventory Accuracy Issues:

 

Stockouts and overstock situations led to fulfillment delays and customer dissatisfaction.

 

3. Warehouse Management Constraints:

 

Manual picking, packing, and order fulfillment processes led to errors and slow processing times.

 

4. Last-Mile Delivery Challenges:

 

Poor route planning and resource allocation caused delayed deliveries.

 

5. Lack of Real-Time Tracking:

 

Customers lacked visibility into their order status, leading to high customer service inquiries.

The Solution

To address these challenges, INTECH implemented an integrated Order Management System (OMS) and Warehouse Management System (WMS). 

Key Features
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    Barcode-based picking & packing for reduced human error and improved order accuracy.

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    Identification of vehicle assignment to effectively assign vehicles and drivers based on capacity and availability.

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    Data lake & real-time analytics to aggregate structured and unstructured data for real-time operational insights.

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    Automated reports to provide metrics on inventory turnover, order fulfillment speed, and customer demand forecasting.

Implementation Approach

1. Planning & Analysis:

 

The project began with a thorough analysis of the client’s existing processes. This assessment uncovered inefficiencies in inventory management, order processing, and logistics, highlighting areas that needed improvement.

 

2. System Integration:

 

To address these challenges, INTECH developed an Order Management System (OMS) and a Warehouse Management System (WMS) using a cloud-based microservices architecture. 

 

Barcode scanning was integrated to streamline the picking and packing process, while API-driven communication ensured seamless coordination between warehouse, order, and delivery systems.

 

3. Testing & Deployment:

 

Before full deployment, rigorous testing was conducted to validate system accuracy and performance. Warehouse staff and delivery personnel received comprehensive training on the new workflows. The new system was then deployed in phases to minimize disruption and ensure a smooth transition.

 

4. Optimization & Scaling:

 

Post-deployment, system performance was continuously monitored, with optimizations made to enhance order routing efficiency.

Results
The integration of the Order Management System (OMS) and Warehouse Management System (WMS) significantly enhanced operational efficiency and customer experience by:
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    Reducing order processing costs through automation

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    Improving demand forecasting with real-time data insights

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    Increasing fulfillment capacity without additional warehouse staff

Tools and Technologies Used
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    Caching: Redis

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    Directory: LDAP

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    Frontend: React.js

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    Version Control: GitHub

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    Big Data: Apache Spark

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    Code Quality: SonarQube

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    Project Management: JIRA

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    Unit Testing: JUnit, Mockito

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    API Documentation: Swagger

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    Data Visualization: Apache Superset

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    CI/CD: Jenkins, Docker, AppDynamics

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    Mobile Apps: iOS (Swift), Android (Kotlin)

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    Testing & Automation: JMeter, Selenium, Cucumber

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    Databases: Apache Cassandra, MongoDB, IBM DB2

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    Logs Monitoring: ELK (Elasticsearch, Logstash, Kibana)

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    Monitoring & Alerts: Prometheus, Grafana, OPManager

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    Backend: Java 8, Spring Boot, Maven, Node.js, Python 3

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    Infrastructure: Google Cloud Platform (GCP), On-Premise

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    Architecture: Microservices, SOA, Event-Driven, Domain-Driven

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    Messaging Systems: Apache Kafka, IBM MQ, Google Cloud Pub/Sub

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    Other Cloud Technologies: Google Maps, Google Direction API, Google Geocoding

Driving Business Transformation with Tailored Digital Solutions

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