The Strategic Automation of the Process Industries
In recent decades, factory automation has been recognized as a competitive difference. It will be a need with the arrival of Industry 4.0 and IIoT-enabled workplaces. Numerous recommendations have been made to determine the optimal amount of automation for a facility. According to Frost & Sullivan’s ‘Impact Assessment of Automation in the Indian Manufacturing Sector,’ the worldwide automation industry is predicted to rise as an investment in automation increases.
It suggests that the sector is leaning toward automation to boost quality and efficiency. Yet there are no hard rules regarding how much automation is optimal, each production unit’s requirements differ from one another and even over time. There are a few guiding concepts that might assist with automation deployment.
ISA-95 may be used as a benchmark.
The International Society of Automation established the ISA-95 standard. This standard establishes a concept for the automated interface between factory control functions and enterprise business operations technology-neutral
The model has developed over the last two decades to satisfy various sectors’ demands and facilitate the adoption of Industry 4.0 ideas. There are four stages in the ISA-95 automation pyramid (Exhibit 1). Process control levels 0 through 2 are used. Level 3 is for MES (manufacturing execution system), and Level 4 is for ERP (enterprise resource planning) (enterprise resource planning).
0 is the lowest level.
Data gathering and data authentication are dealt with at Level 0. Sensors collect process factors like temperature, pressure, and flow, which are then regulated by actuators. Field instruments are sensors and actuators that determine the precision of process control.
The first level is for data consolidation and correlation with pre-defined logic. The reaction speed, scalability, redundancy, complexity, and modifications factor into the controller choices (PLC & DCS).
The second level is concerned with decision support systems that allow process control. Over and beyond the pre-defined control logics, monitoring, acquiring, and real-time processing of data from field instruments through controllers helps directly control the operation.
Integrating control systems with MES at Level 3 allows for data-driven decision making, real-time performance monitoring, and improved operational efficiency. Level 4 achieves the ISA-95’s main goal of integrating MES and ERP for real-time visibility throughout the value chain.
The Different Types of Automation
When it comes to industrial automation, it’s important to remember that the needs of various sectors differ, especially when it comes to time and place. We may define particular criteria around automation needs by classifying manufacturing businesses into process types and discrete types. As a general rule, the equipment determines the product’s capacity, quality, and productivity in all process industries. The scope of automation in process industries may be divided into two categories: process automation and information automation.
Many automation options for the production process are available thanks to modern technology. On the other hand, the proper one is chosen based on the necessity and added value to the company. The following key characteristics may aid in prioritizing automation needs.
Implementing sophisticated auto controls for critical-to-process parameters at Levels 0 and 1 improves process capability, yield, repeatability, and other factors. The nature of the process determines the extent of automation.
Over the traditional cost of investment-based judgments on automation, safety is becoming a primary issue. The first goal is to automate the potentially dangerous or deadly operations at Levels 0 and 1.
Traditional RoI (return on investment) calculations are losing way to aspects like business continuity and risk mitigation for automation initiatives.
Data analytics, particularly in the process sector, gives insight into minimizing processing time, reducing changeovers, optimizing resources, and so on. At Levels 2 and 3, choosing the correct field systems offers many opportunities for productivity gains.
Automation of information
Data is raw; nevertheless, it becomes the perfect fuel for revving up the growth engine when processed. In supply chain management, production management, asset dependability, and quality, automation of data collecting and analytics will aid in the exact identification of improvement possibilities.
Analytics of Big Data
The transition from logical predictions to empirical decision-making is made easier with Big Data analytics. For a successful analytical choice, accurate and trustworthy data with high velocity, volume, and the mix is required. Big Data analytics tools’ increased data requirements need IoT (Internet of Things)-enabled devices in the field. Big Data analytics, for example, can foresee demand and supply concerns and eradicate chronic faults.
The interface between MES and ERP
For improved visibility, an MES & ERP interface unites the complete value chain in a single link. Tools like an intelligent dashboard use various data to assess and monitor the operation’s real-time effect. Data analytics and visualization assist the executive team in maintaining complete control and command of the company.
Artificial Intelligence (AI),Machine Learning (ML) and Deep Learning
Machine-learning algorithms are being effectively used by manufacturing businesses, particularly in numerous process sectors. To prevent malfunctions, machine learning aids in prescriptive/predictive maintenance. Deep learning automates the complicated planning process and replicates multiple safety scenarios for risk assessments, and AI plays a critical role in replacing human potential and skill evaluation.
It’s time to strategize.
The future of automation and the shifting landscape of industrial dynamics are being shaped by a lack of trained personnel, fast technological advancements, and ever-demanding client requirements. Factory automation has evolved from being reviewed based on return on investment (ROI) based on cost savings to being on the CEO’s priority list of things to examine to be future-ready.
Appropriate maturity in automation system deployment and alignment at various levels within the organization would increase overall efficiency and provide a competitive edge. When routines are disturbed, as they are today, we must consider automation and its advantages more holistically.