Monitor and improve the efficiency of production processes.
Challenge:
Inventory management is one of my main challenges even though my purchasing department is connected to my accounting department and the main warehouse. The stock is checked manually and this procedure creates shortages of certain items and overstock of other items.
Main Requirements:
- Optimize supply chain based on performance;
- Optimize flow of material;
- Automate checking of loading and unloading materials.
Industry Sector:
Manufacturing
Challenge classification:
Limiting manual checking in order to minimizing human errors;Real-time process monitoring and optimization., Warehouse management based on real-time tracking of product locations, transportation conditions, the integrity of packaging., Supply chain transparency and reliability improvement.
Time for Project Completion:
Not specified.
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Preliminary Analysis:
Using the taxonomy, we identified various technological solutions for Inventory and Stock Management of manufacturing companies. The company requires to create IT Solution for accountability, inventory and stock management, work scheduling, on-demand management. Therefore the basic technologies of all possible solutions are Cloud Computing, Cloud Data Storage, Relational Database, IoT Connectivity and IoT Device Management which allows us to create a Dashboard where we can have real-time data ingestion from IoT infrastructure , from partners and other actors on the Inventory Management. Furthermore, all solutions use the Cloud Computing , RFID, Dashboard Analytics and IoT Device Management tool for collecting inputs.
The differences between these solutions are the Flexibility of tools to add tracking packages with delivery partners , the capacity and scalability of solutions to massive production , and the analytics feature available on the Real-Time Dashboard.
To add more features to the solution we can use GPS technology for monitoring and tracking the stocks and the delivery. It depends on whether the delivery is outsourced or handled by an internal department. The analytics and optimization of logistics is a key feature that can reduce costs and grow productivity . It is recommended to implement custom Machine Learning solutions to optimize the scheduling and the stock management. Also Scheduling Optimization algorithms can be created from a custom Data Analytics partner.
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Solution Summary:
The proposed solution for inventory management involves implementing BarCode labels on boxes and RFID tags on pallets for accurate detection and recognition of inventory objects. The system combines BarCodes and RFID technology to capture data such as delivery dates and customer information. MQTT protocol is chosen for IoT device communication due to its energy efficiency and suitability for remote connectivity. AWS IoT Core is utilized to securely connect and collect data from devices, with AWS EC2 hosting the application for inventory management, data analysis, and insight generation. AWS RDS ensures secure storage of IoT data, providing data integrity and high performance. The cloud-based components enable the development of a user-friendly dashboard offering real-time visibility into inventory, key metrics, and delivery information. Comprehensive reports and analytics are generated to derive insights into supply chain performance. AWS Sagemaker, a cloud-based ML platform, is employed to develop AI models that continuously adapt and optimize over time, providing predictive insights for work scheduling, material flow, and decision-making processes.
Additional Benefits:
- Enhanced Operational Efficiency: Automation of inventory management processes and real-time visibility result in improved efficiency, optimized work scheduling, and reduced operational costs.
- Predictive Analytics and Optimization: AI models enable predictive insights, optimized inventory levels, and proactive decision-making, driving operational excellence and gaining a competitive edge.
Possible Difficulties:
- Data Integration Complexity: Integrating data from diverse sources, such as RFID and BarCode devices, poses challenges due to varying formats and standards, requiring careful mapping and synchronization.
- Scalability and Performance: Processing real-time data from numerous devices requires a scalable infrastructure to handle increasing data loads and ensure real-time insights without performance issues.
- Security and Privacy Concerns: Protecting data security and privacy while integrating IoT devices and cloud services requires robust measures like encryption, access control, and compliance with regulations.
Sources:
[1] Affia, I. and Aamer, A. (2022), “An internet of things-based smart warehouse infrastructure: design and application”, Journal of Science and Technology Policy Management, Vol. 13 No. 1, pp. 90-109.