RFID
Assembly Line
Ducati chooses TESISQUARE® for the end-to-end digitalisation of its Supply Chain
Ducati Motor Holding, the Italian motorcycle manufacturer based in Borgo Panigale, Bologna, and TESISQUARE®, the leading Italian company in the creation of digital supply chain solutions, have launched an innovative long-term project designed to deliver end-to-end visibility of the supply chain through the implementation of the TESISQUARE Platform.
The partnership is part of Ducati’s digital innovation programme to make its procurement and logistics processes more efficient and innovative, working from the principle that technology and digitalisation can improve the transport ecosystem. In the initial phase of the project, Ducati and TESISQUARE® implemented the Delivery Schedule and Inbound Management modules for publishing delivery plans with direct suppliers, currently numbering around 300, and managing shipment advice notes. The subsequent phase involved the launch of a pilot based on RFID and IoT technologies to monitor the 2,500 vehicles in the company’s fleet and track motorcycles in real time. This offers enormous benefits for control process automation on operations, from planning of deliveries and despatches to management of anomalies and delays through pro-active alerts.
Walmart’s Earnings Call Is A Delight For Supply Chain Professionals
After having too much inventory for several quarters, driven by supply/demand mismatches caused by COVID, they returned to inventory efficiency. The world’s largest retailer had lower markdowns, improved in-stock service levels, and lower inventory. Walmart’s U.S. inventory was down 4.5%, Sam’s was down over 8%. In the US, the company is investing in automation that they predict will lead to a higher level of inventory accuracy over the next few years. Walmart reports that investments in artificial intelligence have also improved inventory management.
Sam’s Club performed 35 million fewer tasks in the store last year. A lot of that was artificial intelligence that helped associates manage inventory better. They also report using RFID and computer vision, as well as digital displays and labels, to improve operations. New digital tools that automate repetitive tasks or eliminate heavy lifting have increased associate productivity and customers are benefiting from improved in-stock rates and associate accessibility, leading to customer experience scores up over 140 basis points in fiscal year 2024.
On the logistics front, the retail behemoth reports that they have retrofitted 13 regional distribution centers with varying levels of automated storage and retrieval systems. They are implementing technology from Symbotic in a phased deployment. The level of automation in a Symbotic solution is hard to grasp; it really has to be seen to be believed.
How Off-the-Shelf Tech Can Make Factories More Profitable
Many smart factory proponents, bedazzled by technology, overlook the biggest potential sources of value creation. We’ve seen this in visits to hundreds of manufacturing sites over the last five years: that most manufacturers make far too little use of readily available data. Labor hours, for instance; those numbers are collected for payroll, but rarely extracted from clock-in/out systems and analyzed to discover ways to operate more efficiently. Similarly, off-the-shelf RFID technology can trace scrap origins, and simple Internet of Things (IoT) sensors can deliver real-time insights into utility usage. The ability to see, analyze, and act on information immediately can have as much impact on factory profitability as advanced robotics, at much lower cost.
The best leaders for smart factory projects are experienced executives who combine manufacturing pragmatism with digital savvy. Too many balance sheets show write-offs for factory modernizations that didn’t deliver the goods — sometimes literally. In this respect, many middle market companies have an advantage over larger rivals, in that their senior leaders are closer to operations both experientially and physically.
🚜 Inside John Deere Harvester Works: Think your iPhone is cutting-edge? Try driving an X9 combine.
”Supply chain was massively disrupted last year,” said Jim Leach, the factory manager in East Moline. “We had hundreds of machines that were partially complete. We still haven’t seen a return to normal yet.” One way to minimize the wait for parts is to make them yourself. The Harvester Works has eight industrial Trumpf fiber-optic laser stations turning sheet metal into combine parts, chassis components and grain tank sides, then molding them on 10 press brakes — large industrial presses — in a process that is almost totally automated. The only need for human hands is to transfer the components from the lasers to the presses. The plant turns 60,000 tons of sheet steel a year into combine parts.
As big a challenge as making the parts is then keeping track of where they go, spread across Harvest Works’ 71 acres of floor space. Two years ago, employees were manually conducting daily inventory of which parts and aborning combines were where. Now, a large, white refrigerator-sized autonomous mobile robot purrs its way through the facility, scanning the RFID chips in various components to map the inventory down to each bin of bolts.
Assembling and checking are often done simultaneously. Michael Churchill uses an impact wrench gun containing an RFID chip that talks to Deere’s central production computer system, which knows when Churchill has tightened any given bolt enough and tells him to stop.
🚗 Using RFID Databolts in an Engine Assembly Plant
There are many types of RFID processors and network protocols to keep in mind as you’re installing your RFID system in your automotive plant manufacturing line. This blog post focuses on RFID databolts. I’ll discuss best practices for installing them, how to use RFID technology to track engine parts and components throughout the production process and how to use RFID databolts to provide instructions and to document the finished process.
The RFID databolt is a threaded device that can be embedded into a blank engine block or other component prior to production. It includes a radio-frequency identification (RFID) tag, a microprocessor, RFID antenna, and a power source, such as a battery or a connection to a power supply.
Automotive works on its mojo
Top of the list here is reducing transportation costs. In fact, transportation is the largest single cost in the supply chain for automotive, says Matt Bush, vice president of engineering and innovation at KPI Solutions. The challenge, he says, is to increase the density of parts and components inside the trailer. But as Freeberg points out, LIB components can easily weigh out a truck faster than it can be cubed out. The other challenge is to maximize the return ratio of collapsed containers on their trip back to the manufacturing plant, wherever that might be, says Freeberg. The standard ratio today is 3:1, reducing the number of trucks needed to return sustainable containers by two for every three shipments.
As Bush of KPI explains, it’s a continuing battle for automakers to manage the flow and relative state of assembly completion of parts and components lineside, where space is at a premium. For instance, a key question continues to be: Is it better to send kits of parts to the line or stage all inventory there for on-the-spot assembly? “The kitting process takes space but reduces the number of steps people must take along the line,” adds Bush.
Plant tour: Middle River Aerostructure Systems, Baltimore, Md., U.S.
Current production programs at MRAS include the LEAP-1A engine for the Airbus A320neo, LEAP-1C for the Comac C919, the CF-6 engine for multiple civil and military widebody aircraft, the Passport 20 engine for Bombardier’s Global 7500 business jet, the CF34-10A engine for the Comac ARJ21 and the GE9X engine for the Boeing 777X.
“For us, it was the integration with engineering, ERP and MRP that was key,” says Diederich. “Plataine integrates into all of this. It manages the raw materials coming in, generates cut plans per our engineering and marks the labels on the kit plies. We can dynamically nest up to 10 parts. The Plataine software uses AI to recommend which rolls of raw material should be cut next.” What is dynamic nesting? “Optimizing the nests on the fly as the software receives new inputs or when we query it,” says Diederich. “It can also send us alarms to change materials or operations. The sorted ply information is output to the Eastman systems, which have “cut and collect” software that identifies plies for kits using different colored lights. These match stacking tables at the conveyor’s end. ”