Scheduling a software update on an industrial machine

A computing system for scheduling a software update installation on industrial machines in an Internet of Things (IOT) environment, comprising:

• a processor;
• a memory device coupled to the processor;
• and
• a computer readable storage device coupled to the processor, wherein the storage device contains program code executable by the processor via the memory device to implement a method, the method comprising:
• receiving, by a processor, a plurality of log files and IoT data feeds related to a set of industrial machines;
• calculating, by the processor, using the plurality of log files, an installation time estimate to perform the software installation;
• analyzing, by the processor, using the plurality of log files and IoT data feeds, a set of activities and an activity sequence related to a workflow execution using a machine learning technique to identify a planned time and sequence of a software update installation on an industrial machine among the set of industrial machines based on the installation time estimate, wherein the set of industrial machines are part of an integrated workflow system;
• performing, by the processor, a digital twin simulation to identify a potential negative impact in the workflow execution based on a downtime of the industrial machine during the planned time of the software update installation;
• and
• proactively reconfiguring, by the processor, a resource allocation to minimize the potential negative impact.

A computer program product for scheduling a software update installation on industrial machines in an Internet of Things (IOT) environment, the computer program product comprising a computer readable storage device, and program instructions stored on the computer readable storage device, to:

• receive, by a processor, a plurality of log files and IoT data feeds related to a set of industrial machines;
• calculate, by the processor, using the plurality of log files, an installation time estimate to perform the software installation;
• analyze, by the processor, using the plurality of log files and IoT data feeds, a set of activities and an activity sequence related to a workflow execution using a machine learning technique to identify a planned time and sequence of a software update installation on an industrial machine among the set of industrial machines based on the installation time estimate, wherein the set of industrial machines are part of an integrated workflow system;
• perform, by the processor, a digital twin simulation to identify a potential negative impact in the workflow execution based on a downtime of the industrial machine during the planned time of the software update installation;
• and
• proactively reconfigure, by the processor, a resource allocation to minimize the potential negative impact.