The adoption of semantic data in manufacturing operations has been significant. Not so long ago, using semantic data in manufacturing was not commonplace. For early adopters who chose to implement shop floor data collection systems utilizing semantic data, there were costs in both networking equipment and implementation time to deploy the tools needed to transform shop floor data from its historical proprietary form into a semantic form. Based on the ever-increasing adoption of semantic data for shop floor software systems, it demonstrates that the benefits have far outweighed any additional costs.
Today, a majority of the equipment being installed in manufacturing operations either comes standard with the ability to provide data in a semantic form or offers a relatively low-cost option to provide such data. Most of the initial cost and effort experienced by those early adopters has disappeared.
Many major equipment manufacturers no longer promote semantic data as a unique feature of their products. Upon digging deeper, it became apparent that these companies have moved on from promoting their support for semantic data (now a standard feature of their equipment) to promoting how they deploy software tools utilizing the semantic data to enhance the capabilities of their equipment.
There is no question that plenty of equipment installed in most manufacturing operations does not natively support semantic data. There is a cost for installing the tools required to convert data from these systems into semantic data. However, many tools are available today to make these conversions easier and more cost-effective than were available to the early adopters. The benefit of converting data from these pieces of equipment into a semantic form is that this equipment can then be more easily integrated into the same production monitoring and maintenance systems available for new equipment.
As industry experts described, semantic data enables software applications to be deployed more quickly and at lower costs. As companies deploy more software systems and technologies like the integration of semantic data models with OPC-UA, we are now seeing a secondary benefit 鈥� software companies are accelerating the rate of development of new products specifically focused on shop operations. As availability expands of products focused on shop operations, costs will decline. At the same time, the types of analysis tools will continue to increase, delivering even more valuable information to support manufacturing operations.
The trend for software tools that leverage semantic data is to provide auto-configuring software applications, nearly eliminating the time and effort required to deploy new software solutions. While this may seem like wishful thinking, commercial products have already begun delivering these capabilities. An industry-leading software supplier of industrial automation software products and integration services describes their strategic vision:
鈥淲e embrace semantic data models and are designing future releases of our products leveraging semantics to significantly reduce manual configuration with a longer-term vision of achieving self-configuring software applications.鈥�
Upon installation, these software systems query the information being published by each machine, dynamically self-configure its database based upon that information, and automatically begin collecting and displaying analytics to support shop operations. This is possible since semantics provides all the necessary information to describe the data published from each piece of equipment fully.
The impact of semantic data on the future of digital manufacturing is significant:
Reduction in the level of expertise required to deploy software solutions.
Reduction in the time and expense to deploy a software tool.
More analytic tools focused specifically on manufacturing operations.
Cooperation between standards groups creates standardized solutions for exchanging data between different environments 鈥� shop floor, quality, planning and scheduling, maintenance, etc.
Flexibility to scale a digital manufacturing solution to the unique requirements of every manufacturing operation.
So, to the initial question, is semantic data worth the effort? The resounding answer is 鈥測es.鈥� It is the basis for much of the future innovation for manufacturing software and productivity solutions.
