July 2, 2018
|by John Hayes, VP Marketing and Sales|
The increasing popularity of Fork Truck Free environments is justified for safety benefits. Now, with the integration of artificial intelligence (AI) and Industrial Internet of Things (IIoT) technologies in Vecna Robotics’ automated guided vehicles (AGVs), companies considering an OEE (Overall Equipment Effectiveness) program or machine utilization tracking system have a much simpler decision.
Bolstered by affordable leasing programs and the elimination of large upfront fees, many manufacturers can implement an enterprise-wide OEE or machine utilization tracking solution. Pay-as-you-go monthly usage models are driving distribution center managers and manufacturing plant managers to try this technology.
Collecting data is one thing, making data useful is another. There is clearly value in historical data but having 24/7 access from the IIoT provides continuous meaningful data.
The Vecna Robotics advanced analytics and IIoT interface guarantees delivery of visual data to stakeholders throughout the organization with user interfaces uniquely configured to suit the needs of specific users or function areas. Manufacturing data must be accessible to more people to enable them to make faster decisions and accelerate workflows. Ultimately this leads to faster, better understanding and communication of information.
Personal Dashboards for Tracking Utilization
Visual dashboards for tracking utilization can be configured by each user for their own unique use, or for use by functional areas such as machining cells. Personal dashboards allow users to customize part of the interface to suit their individual needs. Plus, multiple personal dashboards can be created to provide quick access to a variety of different information. This data is also available 24/7 for remote viewing.
Tracking Multiple Types of Cycle Time
Cycle times can be tracked from a remote site, providing the ability to report metrics such as utilization and efficiency.
Ethernet-based protocols greatly reduce or eliminate any hardware needed at each piece of equipment. While collecting OEE data from equipment with the latest AI and IIoT sensor technology, the expense of many OEE solutions is eliminated. Software-as-a-Service (SaaS) cloud design greatly reduces the implementation and maintenance costs.
The inclusion of IIoT monitoring ensures increases in operational efficiency. Eliminating the ripple effect caused by stopped vehicles is a metric of overall equipment effectiveness (OEE). OEE evaluates how effectively a manufacturing operation is utilized. The results are best used to identify scope for process performance improvement and determine how to achieve the improvement. When the cycle time is reduced, the OEE will increase (more product is produced for less resource). More changeovers (setups) will lower the OEE, which also includes down-time from tuggers and AGVs not operating during production times. OEE measurement is used as a key performance indicator (KPI) in conjunction with lean manufacturing efforts to provide a quantifiable measurement of success.
Without IIoT monitoring, plant managers, operations managers, and logistics coordinators throughout manufacturing and distribution sectors lack the necessary real-time data for continuous vehicle status and health information. This, in turn, leads to a higher chance of productivity waste, and a missed opportunity for process optimization. Manufacturing is in the midst of transformation driven by technology, and those who lag behind will quickly lose their competitive advantage.
John Hayes, Vice President of Marketing and Sales for Vecna Robotics, is a widely respected thought leader for the manufacturing, distribution, and material handling industries and a Supply & Demand Chain Executive “Pros to Know” recipient. For more than twenty years he has been evaluating, designing, developing, and implementing innovative software and hardware solutions with a particular focus in the AGV (automated guided vehicle) space. Contact John at firstname.lastname@example.org.