Case study

Breaking bottlenecks with real-time data

The custom-built data dashboard in WorkfloPlus offered a data feed that can be analyzed in real-time, so managers are able to generate hourly data reports with a level of insight that was never previously possible, so individual assembler rates can be easily compared and contrasted to help improve efficiencies and crucially, prevent future production bottlenecks.

It also ensured a thorough audit trail that satisfied customer and regulatory needs, as an ISO manufacturer Carrio Cabling required a clear audit trail demonstrating who built the assembly; when it was built and on what assembly line and finally; what revision it was built to? The manufacturer quickly found all this data could be accessed quicker and with greater accuracy compared to when this data was manually logged.

This just left the problem of how to effectively measure success? “We justified WorkfloPlus on how much it would cost to hire someone to collate this same data; before an individual employee would gather, track the rate of production and what assemblers had built. By automating this work in real-time it simply pales in comparison to what we’d pay an operator to complete the same work, plus we wouldn’t get the information as quickly to act on it as required,” added Miles.

By digitizing its assembly processes with WorkfloPlus, Carrio Cabling is able to guarantee that its assemblers are always working off the correct documentation; it can automatically create audit trails on individual assembly lines and can quickly find out where production bottlenecks are for improved productivity and efficiency savings. v

Intoware

Intoware is a UK-based SaaS company at the forefront of workflow automation via the use of mobile and wearable technology. Its flagship product ‘WorkfloPlus’ is developed from a mobile first viewpoint, taking advantage of both mixed and augmented reality. It provides the connected worker with an easy-to-use experience, enabling businesses to digitally transform processes and gain rich data capture to make informed decisions.

www.intoware.com

Unlocking

the data

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Pick-by-Vision and the Human Digital Twin. By Larry Olson

No longer are manufacturers mutually exclusive from logistics. Finished goods have to go somewhere, whether to the end-user (direct to consumer, D2C) or to a warehouse, distribution center, or third-party logistics (3PL) facility. Digital twin technology in logistics is constantly and rapidly growing. Lead by pick-by-vision specialists, creating a human digital twin aims to perfect support for warehouse staff.

Critical factors for successful logistics operations will never exclude human beings. Workers offer unparalleled flexibility and logistics experts predict that this will remain the case, even with rapid adoption of automation. As part of continuous process improvement more companies are optimizing procedures and processes, with a humancentric approach. Many logistics professionals are currently relying on business intelligence and process data captured.

Logistics companies which use digital twins report these highly accurate, virtual representations of the entire supply chain must include the warehouse. Digital twins accompany many logistics systems throughout the entire service life. It provides support for planning, while simultaneously acting as a simulation model to secure test environment for potential process changes.

Incorporating movements

When using digital twins for testing process changes, human workers are a variable for which the technology cannot account. Until recently it was difficult to incorporate the specific activities and movements in the warehouse. Pick-by-vision changed this by implementing the human digital twin. It maps a warehouse worker going about day-to-day tasks which are reflected in the human digital twin from transport routes and times to movements and scans. These data are combined with the important operating parameters, especially tiny details which help identify areas for improvement, such as WLAN coverage or warehouse resources. This highly accurate virtual representation of the worker is continuously improved and updated. This is lean manufacturing in logistics.

Simple data collection

The human aspect of process analysis has been neglected in many businesses throughout logistics and manufacturing industries. Although concerns about data privacy are often given as a rationale for hesitancy, the true cause runs deeper. Collecting the data necessary for the human digital twin was next to impossible prior to pick-by-vision. With the right technology accessing these data it is now quite easy to model human behavior.

Gaining a virtual representation of the worker in the warehouse required logistics specialists to collect and use data generated in the relevant process. Wearables make this data collection axiomatic. The complication is making sense of these data. This requires an intelligent and intuitive tool.