Scott Mullins is the EVP of operations and COO at Lucas Systems, where he leads several cross-functional teams, including engineering, operations, and support. Mullins has more than 30 years of experience with supply chain and health-care organizations, having previously served as chief technology officer at Annexus Health, vice president of technology for health-care supply chain company Pensiamo, and as vice president of product development and senior director of software development at Intalere. Mullins has a Bachelor of Science degree in information science from the University of Pittsburgh.
Q: How would you describe the current state of the voice and software markets?
A: The industry is thriving and growing consistently, bringing substantial value along with it. With labor shortages expected to continue, there’s a focus on developing products and features aimed at minimizing the need for labor. A significant portion of the market’s innovation revolves around enterprise software, particularly modular solutions and applications like voice technology—things like highly intuitive voice recognition, which requires minimal training, and the integration of AI and machine learning to cut down on-floor worker travel.
Uncertainties in the supply chain, market fluctuations, and inflationary pressures make investing in large-scale, capital-intensive solutions risky. Being able to implement cost-effective software solutions within a shorter time frame is a crucial advantage. The demand lies in software that’s both user-friendly and flexible, capable of seamlessly exchanging data with various WMS, ERP, or warehouse execution systems. The key is to have a solution that doesn’t heavily rely on IT resources and can be quickly put into action, bringing a critical edge in optimizing operations and enabling scalability as operational needs change.
Q: Voice technology is most often associated with picking, but it can be used in many other applications as well. Where else are customers currently looking to deploy voice?
A: Picking is really just the tip of the iceberg. You can use voice on its own or together with scanning, and possibly even vision, to improve the execution of tasks throughout the warehouse.
For example, with inventory checks, speech recognition can be used for verbal confirmation of inventory levels during regular checks, ensuring real-time accuracy in the system without needing manual data entry. During cycle counting, speech recognition can guide workers by providing audible counting instructions, minimizing errors and accelerating the counting process.
For task assignment and allocation, warehouse supervisors can use voice to assign tasks, providing real-time instructions and updates for quicker response times and efficient task execution. In cross-docking situations, where products are moving from inbound to outbound shipments, voice can verify that the correct items are being moved without manual scanning. The list is really extensive, spanning almost all of your warehouse functions.
Q: Do you have any particular projects under development that you wish to share?
A: Over the past 25+ years, the Lucas team has consistently broken new ground in process and technology when it comes to simplifying complex logistics challenges. While we’re always driving the evolution of our core voice-recognition capabilities, we’re also looking for areas to empower warehouse workers and managers through the continued progression and enhancement of solutions like our Dynamic Work Optimization software, which helps customers reduce on-floor travel 30 to 50% by optimizing work assignments and by defining optimal pick sequences or paths.
One new project we’re really excited about and proud of is our partnership with Carnegie Mellon University, which is aimed at solving packaging and sustainability challenges in the warehouse. The research is focused on developing ways to reduce waste by optimizing the way warehouses pack and package multiple items in a single order.
Q: How are the recent advances in artificial intelligence affecting research and product development?
A: AI has the potential to significantly transform activities like picking, product slotting, developing worker travel routes, and the coordination of workers and robots. Advanced analytics, machine learning algorithms, and ideas like digital twins are pivotal for helping us explore ongoing optimization, increased adaptability, and greater flexibility.
For example, AI has the capability to understand the typical time required to complete tasks by analyzing performance data collected from various operational aspects. It considers factors like user, task type, work environment, starting and ending locations, the product being handled, quantity, and more. This learning process empowers machine learning to establish standards that go way beyond the accuracy of standards developed through traditional labor standards engineering.
One big advantage of machine learning models is the ability to continuously improve. As operational modifications are introduced, the machine learning approach automatically adapts and adjusts. This dynamic capability ensures that solutions can stay in sync and continuously optimize operations with evolving circumstances. It can really be a game-changer.
