Five years ago, MHI CEO George Prest and his team set out on a trip across the country to ask materials handling stakeholders a simple question: Where do you see the industry in 2025?
The answer, he found, was that the business community was on a rapid path to change. Everyone involved in the supply chain had begun to speak a different language, bringing up 3D printing, drones and autonomous vehicles as the future.
"That was all really foreign to us," Prest told Supply Chain Dive, reflecting on the experience. But now, he added, you can see all that technology and its benefits on trade show floors.
Ever since that trip, MHI surveyed more than 1,000 executives each year to get a pulse of the industry’s path to technology adoption. Below is an overview of the 11 technologies they identify as set to shape smart manufacturing:
Rates of adoption for emerging technologies
Source: The 2018 MHI Annual Industry Report
Cloud Computing & Storage
RATE OF ADOPTION
Big idea: Cloud computing and storage will help companies outsource the costs of managing the collected data and memory needed for emerging technology.
Use case: The benefits are not limited to manufactures. MHI has stopped using in-house servers for its data: “It’s much more economical,” said Prest.
Challenge: Migrating systems to a new operating procedure and budget, while ensuring the proper steps are taken to maintain cybersecurity.
Inventory & Network Optimization
RATE OF ADOPTION
Big idea: A platform to connect and optimize how companies use all the data gathered by emerging technology.
Use case: Inventory control drones rely closely on these systems, sending and receiving data from them to help executives manage product data in real-time.
Challenge: An effective system must closely interact with emerging technologies. It receives data from sensors, talks to autonomous vehicles and uses algorithms to sort and optimize that information.
Sensors & Automatic Identification
RATE OF ADOPTION
Big idea: Sensors like RFID tags can remove manual steps from the quality assurance process at each step of the supply chain and add SKU-level visibility.
Use case: Macy’s deployed RFID tags on all its inventory last year, and saw a 30% reduction in display shortages.
Challenge: Having the infrastructure – such as systems with high processing speeds and memory capacity – to store the massive quantity of new data sensors provide.
Predictive Analytics
RATE OF ADOPTION
Big idea: Use the mass amounts of data gained from new technology to anticipate problems and improve forecasting.
Use case: Imagine putting a crystal ball to your supply chain and asking it what would happen if an order is late. Predictive analytics would let it tell you what shortages will occur at a future date and how downstream customers would be affected.
Challenge: How can supply chains go from predictive, to prescriptive analytics? Prescriptive analytics allows software to not only analyze issues, but also suggest next steps for the supply chain manager.
Internet of Things
RATE OF ADOPTION
Big idea: A wider category for sensor-enabled equipment and systems that are in constant communication.
Use case: The smart factory is built on this technology, where sensors on production lines, pallets and forklifts may all be adapted to deliver data to a central system.
Challenge: How do you get all the equipment and existing systems to interact? “Every piece of equipment that’s out there was developed with a different code,” said Prest.
Robotics & Automation
RATE OF ADOPTION
Big idea: Turn ordinary equipment into self-operating machines.
Use case: At MODEX 2018, Brain Corp highlighted a robotics-enabled scrubber. The company partners its software with manufacturers’ equipment, to boost efficiency.
Challenge: Which machines are suited for automation? What talent is required to design and operate these systems?
Wearables & Mobile Technology
RATE OF ADOPTION
Big idea: Rugged technology like industrial smart glasses, gloves or other clothing items can help boost worker productivity at a time of labor shortages.
Use case: Logistics providers can use this to reduce the need for training temporary labor during peak retail fulfillment season. “It literally makes (new hires) efficient the first day they’re on the job,” said Prest.
Challenge: Not all emerging technology can work well within crowded or loud facilities. Workers must also be willing to adapt to a change in tools and processes.
Blockchain
RATE OF ADOPTION
Big idea: A decentralized ledger manufacturers can use to record transactions and assure their integrity.
Use case: "It's in its infancy," said Prest. "Everybody is still trying to really figure out what blockchain is all about."
Challenge: Adoption will require companies and networks to leap into the "bleeding edge" of technology – where pilots could fail – to prove the technology’s benefit.
Driverless Vehicles & Drones
RATE OF ADOPTION
Big idea: Raise productivity by reducing the resources needed for rote tasks, like moving a vehicle across a facility, or elevating a forklift to count top-shelf inventory.
Use case: Self-driving vehicles could move across a designated space within the warehouse, to expedite pick-and-pack, inventory control or forklift operations.
Challenge: Facilities acquiring these tools must often shift their design or map their systems to safely integrate driverless vehicles without disrupting labor operations.
3D Printing
RATE OF ADOPTION
Big idea: Reduce the steps and materials needed for small parts, by using a new manufacturing method to build a product in one step.
Use case: GE used the technology to design an engine with only 12 components, that is 5% lighter and one-third printed. The previous version had 855 components.
Challenge: Investing in the research to integrate it with current production processes and finding the talent that can implement it effectively.
Artificial Intelligence
RATE OF ADOPTION
Big idea: Unlock workers’ potential by using advanced computing to perform rote or human-like tasks.
Use case: Voice recognition within wearable devices for warehouses allow pickers to do their jobs and engage with a WMS without ever looking down.
Challenge: The U.S. will have to overcome regulatory obstacles and fear of change to unlock its full potential.
Correction: In a previous version of this article, a robot was misidentified as a driverless vehicle.
