The introduction of new digital transformative technologies and processes into everyday workspaces means both technical and non-technical personnel must have some understanding of its terminologies. These terminologies include both new keywords and more importantly, keywords that digital transformation solutions providers such as Simio use to simplify the request for proposals of industrial enterprises.
This post will highlight some of the traditional keywords used within the industrial sector and their corresponding terminologies in the digital transformation age. These keywords are focused on requests for simulation and digital twin modeling solutions for optimizing facility of process performances.
9 Keywords and their Digital Transformation Terminologies
- Optimization Engine – Within the industrial sector, the need to optimize processes and facility performances is crucial to improving productivity, throughput quality, and achieving maximum ROI. Hence, industrial enterprises routinely search for optimization engines that support the evaluation of the production system to develop optimized plans and schedules. For Simio this is called – A Process Digital Twin.
Factory Digital Twins are virtual representations of the assets, resources, and processes within your facilities. The digital twin is a replica of your physical facilities modeled using the data generated from your MES, RFP, or other data storage platforms.
- Optimizer – An optimizer is a verb explaining the actions of an optimization engine. Technicians in the industrial sector who require optimizers to evaluate and improve facility processes can leverage digital transformation tools to accomplish the task. Simulation and digital twin solutions offer Process Simulation that optimizes industrial processes to improve output or gain insight into operations.
Hence, we call the optimizer you request for a process simulation solution or process simulator that you can apply to develop analytical and process planning initiatives. Process simulation also relies on industrial data sets to develop functional models and for simulation.
- Objective Function – Minimizing resource use to achieve productivity or maximizing an asset capabilities to improve throughput are important factors to achieving profit within the industrial sector. Traditionally, the term objective function is used to refer to the means used to achieve the minimization or maximization of an operational process within the factory floor to meet specified goals. To achieve the objective function using digital transformative means, a virtual factory model is required.
The Virtual Factory Model serves as the means and platform for developing plans, strategies, and schedules in achieving the objective function or goals that maximize ROI. Hence, we use the term the Virtual Factory Model as an upgraded replacement for “Objective Functions”.
- Algorithm – In the industrial sector, the term algorithm represents a set of rules that should be followed to solve operational problems. These set of rules are generally schedules or manufacturing master plans developed using digital transformation tools such as simulation modeling or a manufacturing enterprise system. Although algorithms are used in the industrial sector, it is more or less a mathematical and computing term that does not capture the intricacies of the average factory floor. Hence, the term Feasible Schedules better encapsulates the set of rules required to solve operational problems.
We refer to Feasible Schedules as actionable plans developed for immediate implementation to solve production-related problems and eliminate operational bottlenecks. These actionable schedules are generated from the virtual factory model built with a digital twin solution.
- Self-Learning AI – Automating work processes have become a key consideration for industrial enterprises interested in improving operational conditions, maximizing resources, and automating decision-making. Artificial intelligence has always been synonymous with automation which is why the term self-learning AI is used to describe autonomous processes.
For planning and scheduling we use the term Autonomous Scheduling to represent self-learning schedules that react to real-time changes. Autonomous scheduling involves developing actionable schedules from simulation and digital twin models that utilize AI or neural networks to streamline the scheduling process.
- SaaS Model – Software as a Service refers to the provision of software and its corresponding licensing agreements online or through a cloud-enabled platform. With SaaS, enterprises do not have to purchase software and install it on hard drives before accessing these applications. In the industrial sector, many enterprises refer to online solutions for developing simulation and digital twin models as SaaS models. Here, we refer to these solutions as digital transformation solutions to highlight the diverse online tools and ways that can be used to develop virtual models.
- Python – The widespread use of Python programming language as a means of handling data capturing and science tasks has made the word ‘python’ synonymous with the process of capturing data or knowledge in the industrial sector. Using python can be a source of confusion for technical personnel offering digital transformation solutions. Hence, we believe the term “knowledge capture” better describes the process of accumulating data and turning it into actionable insights.
- Asset Utilization Optimization – The term asset utilization optimization has been used alongside machine utilization optimization and other variants to refer to processes that improve productivity. In many industrial sectors asset utilization optimization is used to describe process optimization thus limiting the complex task of evaluating complex processes to the evaluation of individual assets.
The term Process Improvement highlights the application of process that optimizes the entire industrial system instead of individual assets. Using the process improvement informs technical professionals that you intend to improve every aspect of your facility’s processes not individual equipment.
- Solver-based Solution – The solver-based approach to developing optimized solutions involves the use of an optimization solver to analyze available data and constraints to solve operational problems. In the solver-based solution process, black box modeling is used. Black box models are designed based on an input-output approach to problem solving. The black box model takes a predetermined input and works towards a provided output.
Using advanced digital transformation solutions, the term model-based solution that involves the use of a glass box approach is the better option. The model-based solution involves the use of simulation to understand the behavioral patterns of industrial systems. In this case, a predefined output isn’t required and the use of neural networks drives the experimentation process.
Using the Right Terminologies
The updated terminologies outline here simplifies the creation of a request for proposals and communicating with digital transformation solutions providers. You can learn more about these terms and their applications from these resources: