Parth Chandak discusses how manufacturing systems are evolving from basic tracking tools to comprehensive digital platforms that improve efficiency and quality in autonomous technology production. With experience in both manufacturing engineering and creative technology, he shares insights on building more intuitive interfaces for complex production environments.
The autonomous technology industry has seen rapid growth, which has created new challenges for manufacturing systems. We've witnessed a significant transformation from basic tracking to comprehensive digital platforms that integrate multiple aspects of the production process. This evolution goes beyond automation. It represents a shift toward more intuitive, user-friendly interfaces that help optimize manufacturing processes while ensuring high-quality production. These systems need to handle complex configurations and frequent design changes while maintaining accuracy and efficiency.
Traditional manufacturing systems often relied on manual entries and disconnected documentation, which created several problems. Data entry errors were common, and information was frequently siloed across different teams and platforms. Work instructions might be stored in various Google Drive documents, making it difficult for production teams to find consistent information.
Inventory management was another challenge, with manual counting processes leading to discrepancies and inefficiencies. Without integrated systems, production scheduling often relied on individual knowledge rather than data-driven planning, which limited scalability and made it difficult to optimize resources.
My approach focused on creating tools that bridge the gap between engineering requirements and production needs. I started by developing web-based tracking systems to minimize data entry errors before implementing more comprehensive digital platforms.
For complex vehicle configurations, I helped establish structured change management processes and version control for Bills of Materials (BOMs). This ensured that production teams always worked with the correct specifications, which is critical when dealing with components that evolve rapidly during development.
I also worked on digitizing work instructions, bringing scattered documentation into structured digital platforms that improved consistency and accessibility. This made it much easier for production teams to access the information they needed without hunting through various systems.
I focused on several key areas to automate workflows and reduce manual effort. For inventory management, I implemented automated material deduction systems that improved accuracy and reduced counting errors. By integrating work orders with inventory software, we achieved real-time material tracking that helped optimize production cycles.
Quality control was another important focus. I developed an automated quality management system using JavaScript and integrated it with user friendly systems like Slack, which provided real-time notifications and comprehensive dashboards. This helped teams quickly identify and address quality issues before they affected downstream processes.
For planning and scheduling, I created data-driven tools that enabled more effective management of production timelines and resource allocation. These tools analyzed historical data to help predict material needs and production times more accurately.
The improvements led to several measurable outcomes. BOM processing time was significantly reduced, making it possible to implement changes more quickly and with fewer errors. Work order creation became more efficient, which was particularly important for complex components like wireless control devices.
The time required for inventory reconciliation decreased substantially, resulting in better stock management and fewer production delays due to missing components. Quality issues were identified and addressed more quickly, improving first-pass yield rates.
Perhaps most importantly, these systems made the manufacturing process more transparent and predictable, which helped align expectations across engineering, quality, and production teams.
Resource limitations were a significant challenge. Manufacturing systems often compete with other high-priority departments, so I had to be strategic about demonstrating value.
System integration was another major challenge. We advocated for middleware solutions and API implementations that could connect various systems like inventory management, work instructions, and BOMs. This required careful planning to ensure data could flow seamlessly between systems without creating new bottlenecks.
Helping teams transition from familiar spreadsheet tools to new digital solutions required substantial training and change management. I developed documentation and training programs to ensure smooth adoption, along with validation tools to verify that the new systems were working correctly.
My research on rapid prototyping technologies and user-centered design has significantly influenced my approach to manufacturing systems. In my paper "Rapid Prototyping Technologies and Design Frameworks: Transforming Traditional Manufacturing into Smart Additive Solutions" I explored how prototyping methodologies can improve manufacturing processes by enabling faster iterations and validation.
My work on "A Comprehensive Review of User-Centric Design in IoT Prototyping for Smart Agriculture: Integrating User Feedback in Hardware and Software Development" examined how user feedback can inform the development of more intuitive interfaces for technical systems. These principles apply directly to manufacturing platforms, where operator experience significantly impacts efficiency and error rates.
My paper on "Advances in Human-Robot Interaction: A Systematic Review of Intuitive Interfaces and Communication Modalities" explored interface design principles that help humans work effectively with automated systems. These insights have been valuable in designing manufacturing interfaces that provide the right information at the right time to support decision-making.
Manufacturing has systems that might turn out to be more proactive than reactive. Intelligent automated solutions will improve production scheduling, predict needs of materials, and enhance quality assurance during the early detection of imagined issues.
As the industry grows, it becomes increasingly clear that scalability would become very critical. Systems optimized for small production runs must be adaptable for large operations without having to completely redesign the system. This implies flexible architectures that have the ability to grow with the organization.
There will continue to be importance attached to user-centered design principles. Modernization of interfaces in manufacturing systems will be more intelligent because new technologies will not stop such modernization. Commands should be given to most 'powerful' automation combined with human processes of making decisions quickly.
Integration will also become synonymous across the entire life cycle of the product; the manufacturing data will help be fed back into the design processes to enhance future iterations. An organization would thus have a continuous loop of improving its products as well as its production systems.