In the industrial sector, a growing number of companies have an ongoing smart factory initiative. In such initiative, previously disparate systems and equipment become connected so the data streams they generate can be turned into actionable insights. Industrial IoT (IIoT) data originate from various sensors and Internet of Things devices deployed in industrial equipment and facilities. The vast volume of generated data need to be leveraged to improve robots’ operation, optimize processes, and help industry stakeholders and applications make faster and more informed decisions. Many existing industrial applications use the power of the cloud for data processing. However, time-sensitive industrial applications cannot tolerate sending IIoT data to the cloud for processing due to unacceptable network bandwidth requirements and high latency. The operation and maintenance staff of industrial facilities need the ability to efficiently stream data and process data in real-time at the edge. Smart factory operations are typically executed as workflows of dependent tasks. This paper investigates the performance of some scheduling stategies for the exection of workflow tasks in a smarty factory fog environment. Simulation results show that the MinMin, GA, and PSO scheduling algorithms offered the best results in terms of execution time.