The principles and methodology of control science and engineering are relevant beyond engineered systems to all dynamical systems. In this paper we discuss how control terminology and concepts can be interpreted in the context of decision-making by human managers, and the benefits that can accrue from the analogy. Examples of managerial decision making are presented in a control systems context. A selected review of earlier literature in the area is included. The connections of some popular business practices with control principles are reviewed. Points of differentiation, especially human-in-the-loop aspects of managerial control, are highlighted. Two examples, on return-on-investment dynamics and short interval control in process plant operation, are discussed. A number of control concepts are mapped to the management domain, revealing what we hope are useful insights for decision makers. This is the main paper for a tutorial session at the 2022 American Control Conference. An appendix is included that contains abstracts of the other papers and presentations in the session.

Many categories of manager decision-making (human organisations, environmental systems, supply chains) are in some way or another related to systems. However, system dynamics do not always lend themselves well to superficial, or intuitive, interpretation. This can inadvertently result in suboptimal managerial decision-making. The application of control science concepts for guiding managerial decision-making has the potential to improve results. The contemporary manager is typically resource-constrained and time-stressed. Control-science-based decision-making guidance that does not accommodate the reality of the manager’s time constraints may have limited affect in practice. A manager’s attention-scheduling behaviour is more analogous to that of a networked control system (NCS) than to that of a singularly focused control loop.There is an opportunity to apply NCS aspects of control science to identify the minimum attention/frequency requirements of key decision-making realms. This paper acknowledges the time-poor reality of the contemporary manager. It considers how learnings from NCS theory can be applied to add resilience and efficiency to control-science-inspired improvements to manager decision-making. Just as regulatory control systems don’t perform well when subjected to unexpected network or input/output delays, the application of control theory concepts to manager decision-making will be challenged if the time-poor aspects of the manager are not catered for.

Over the last few years, the fourth industrial revolution has captured attention both in the academic and industrial realms. The world is still developing an understanding of this new industrial revolution and how the digitisation of the manufacturing process enables a paradigm shift from mass production to customized production.This paper provides a concise introduction to some of the more popular components of Industry 4.0. It also provides an insight into the current support infrastructure for accelerating Industry 4.0 adoption in Australia, along with a perspective of the progress of the Australian manufacturing industry.The Australian manufacturing industry has been in decline for decades and currently represents around 6% of GDP. This is a concern as manufacturing helps raise the standard of living more than any other sector and is often considered to have the largest multiplier effect of all sectors in an economy. Digitisation (and Industry 4.0 technologies) are key components of the Australian Government’s modern manufacturing strategy.

The provision of a safe and healthy workplace is one of the highest responsibilities of both employers and employees. Alongside the ethical aspects, a safe workplace also has a strong correlation to the financial aspects of business performance. In general the journey towards safer workplaces has been neither rapid nor without challenges. However improvements in standards, processes, education, regulations and technology have collectively resulted in strong progress over time. Industry 4.0 promises a significant step-change in industrial operations including improvements to productivity, efficiency, quality and cost. This imminent industrial revolution also has the potential to automate and simplify many aspects of safety and risk management. To date the safety related research and work within Industry 4.0 has largely been developed/proposed in isolated functions and from a 'technical solution' perspective with a heavy emphasis on automated monitoring. However safety performance is related to things like human behaviour, psychology and safety culture. This paper highlights the potential of this trajectory to undermine existing safety culture. Key recommendations around a more coherent anthropocentric approach are included. Contemporary estimates for projected market spend on Industry 4.0 vary but are typically in excess of US$150 billion by around 2026. This is all in pursuit of improved performance. If the safety components are not implemented in a manner that retains and supports safety culture then a consequential impact on safety performance could likely impact on other performance measures, which would be the opposite intent of Industry 4.0. Copyright (C) 2021 The Authors.

Contemporary business (including those with integrated AI capabilities) often encompasses or aspires towards the automated, networked production of industrial goods across transnational supply chains that have many digitalized interfaces. This allows competitive operations in time, costs, and quality, which have been widely discussed. On the downside, it entails cyber threats with significant risks for society in areas including business, environment, and health. Hence, to adequately manage these risks in the emerging digital world, there is a vital necessity to raise awareness, establish, maintain, and further develop cyber-security measures to ensure an appropriate level of protection along the entire value chain and supply chain. Blockchain capabilities are introduced to improve the technical and organizational basis for secured operations in industrial networks. Its advantages are explained by a simple USB-device use case, that has often been the root cause to subsequent security incidents, especially in the Stuxnet incident.

Data typically requires context or meaning in order to be of value. In an applied sense the context is often implemented in the form of a data model. Contemporary manufacturing environments rely on the use of data models both throughout their automation landscape as well as within most layers of business operations. Industry 4.0 (with a projected market spend of over US$150 billion by around 2026) relies heavily on the use of data models. The scale, complexity and level of integration of data models is set to increase markedly over the next phases of migration towards Industry 4.0.However the nature, location(s) and significance of data models are not always understood by many of the stakeholders within the enterprise. This can lead to decisions around system architecture, ownership and accountability that result in sub-optimal outcomes for the enterprise.This paper clarifies the nature and characteristics of data models in the manufacturing enterprise, providing a context and understanding for stakeholders and decision makers.

Enterprises operating industrial control and automation systems, in a bid to increase profitability, are demanding ‘smarter’ shop floor operations, and are becoming ever more ‘data-driven’ in their decision making. IIoT and Industry 4.0 come with the promise of unlocking vast amounts of previously unavailable data from shop-floor devices and systems. In meeting this thirst for data, there is a significant engineering burden to correctly configure and connect devices and software systems.This paper presents two general approaches that allow connections between data sources and sinks in automation systems to be rapidly configured en masse. These techniques, scripting and model-based, can automate the manual, repetitive, and error-prone data point configuration task. A case study applying implementations of these techniques in a modern brewery’s process control and automation systems is presented. It demonstrates the significant level of reduction in configuration burden that has been achieved, especially in the case of the model-based approach.By utilitising these techniques, the cost, time, and error-rate involved in the configuration of industrial control and automation software systems can be greatly reduced. These improvements in engineering efficiency can lead to previously infeasible projects becoming achievable, and the extension of the lifetimes and capabilities of existing plants and equipment.

The digital domain, or cyberspace, has developed into the fifth domain for military operations, along with air, sea, land and space. Malware presents a significant and growing threat, not just to industry but to digital environments in general. Although, no complete protection against cyber threats will ever be realistic, a resilience against a certain level of threat should be achieved to safeguard industrial corporations. The existing cyber resilience of multinational corporations is arguably typically inadequate and, in the context of digital supply chain integration, the potential consequences are larger. This situation could be significantly improved through the decision on senior management level for effective adoption of existing standards, processes and resources. Cyber resilience requires conscious planning and relentless action from both the security provider and the multinational corporation. Effective internal procedures along with appropriate architecture of plant automation infrastructure, including response and recovery planning, can have a substantial impact on the resilience of a plants Operational Technology (OT) as well as its ability to withstand or recover from a cyber incident. This paper provides a practical perspective of cybersecurity resilience and defence in a multinational industrial environment. Some example recommendations that are both practical and effective are included. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

Contemporary environmental sustainability challenges coupled with rising energy costs compel the manufacturing industry to tightly monitor energy consumption. User-pays accountability systems are an appealing pathway to evoke genuine motivation to monitor and reduce energy consumption. This paper outlines the numerous issues and challenges associated with implementing such a system in existing manufacturing plants. A case study of a functioning user-pays system in a modern brewery is presented.

Sustainability and environmental impact have emerged as important priorities for industry. The manufacturing industry often use maturity models to guide their pursuit and progress towards manufacturing excellence. Control & Automation (C&A) has a significant and increasing impact on the environmental footprint of manufacturing plants. However there are no maturity models that specifically address this impact and provide a pathway for minimisation.This paper both clarifies the need for a focused maturity model of this type and presents a proposed maturity model structure. The proposed maturity model can help accelerate the reduction of C&A's environmental impact across manufacturing industries. It also provides a context for those undertaking research in the associated areas to help them identify both their target market and the potential impact of their research focus.

The manufacturing industry has continued to evolve since its inception. There have been many different areas of focus and paradigms during this journey of improvement. Globalisation, particularly in the form of acquisition and integration of disparate manufacturing plants, has presented an interesting set of challenges and opportunities for plant and process control. This paper looks at the impact of this type of globalisation on process & plant control. The examples and insights that are presented are based on observations and experiences within the global brewing industry. Process control is a popular field of research and is relevant to a significant portion of the manufacturing industries. The insights into the context and basis for emerging control requirements is of use to researchers interested in industry-relevant aspects of this field.

This paper investigates the optimization and control of a beer filtration process via closed-loop PID control. The authors' previous work has found that the beer filtration process changes in both dynamics and steady-state gain, resulting in different linear models obtained at different operational times. This paper addresses the issue of multiple linear models for a plant by taking the average of their frequency responses to obtain a single set of frequency response data. It also proposes to use a PID controller design technique based on two frequency response data points. The PID controller is then validated using Nyquist loci for measuring gain and phase margins. Closed-loop control simulations that mimic the filtration process have been used to demonstrate the feasibility of maintaining the desired operating conditions of the filtration process by manipulating the system input.

A novel architecture for a real-time Utilities Consumption Model (UCM) has been developed. The online UCM is capable of estimating the contributions from individual items of equipment towards the total instantaneous load of key utilities in a manufacturing plant. It also has the capability to forecast future consumption for areas of a plant that are scheduled. The UCM is a useful addition to the industrial control tool-set as it provides an effective means of minimising the energy impact of the timing and scheduling aspects of plant operations. A case study, demonstrating the application of the UCM at the Carlton & United Breweries (CUB) plant at Yatala, Australia, is included.

Process-related industries (many of which are inherently batch-related) account for a significant portion of the manufacturing sector. Many advances have been made in the field of process control, some of which are related to performance management of control loops. However, much of this work assumes continuous operation of the control loops. Many of the challenges presented by batch environments relate to a requirement to cater for both interruptions to control loops as well as varying plant dynamics. This paper provides both an overview of the general issues that are likely to be encountered in a batch environment as well as some deeper insights into a number of specific challenges. Some examples are provided based on experience within one of Australia's largest breweries. Despite this being a topic of emerging importance to manufacturing industries there is very little objective (non-commercial) information on these challenges available in the literature. This material will be of benefit to researchers who have an interest in realistic industrial control applications. It is also intended to be of use for those in industry by providing a realistic set of issues and challenges that are specific to batch environments.

A significant portion of industrial automatic control is powered by PID controllers. However there is little published literature on the details and quantity of controller types used in industry. Food and beverage represents an important segment of manufacturing in Australia. This paper presents a review of the automatic/regulatory control topology in one of Australia's largest breweries. Details of the site's 505 PID loops as well as the industrial networked control systems are included. As there is very little published material of this type it makes an important contribution to the existing literature. It is intended that this material can assist further research by providing evidence-based information on the quantity of controller types used in this segment of industry and details on some of the current issues and opportunities.

A novel information infrastructure designed to measure key components of the greenhouse gas emission 'opportunity cost' in a production environment is proposed. The data-driven metrics facilitate drill-down to provide visibility of constituent parts. Contemporary metrics typically focus on direct energy consumption and are often normalised against units of product produced. Such metrics have proven to be useful for monitoring trends in relative performance and for benchmarking against other plants. However, they typically do not provide visibility of energy consumption (carbon emissions) that did not directly contribute to the production of saleable product. With the community's ever increasing focus on sustainability and climate change, the environmental impact of industry has come under greater scrutiny. The proposed information infrastructure provides a new level of transparency enabling stakeholders to see the portion of utilities consumption (or greenhouse gas impact) that did not directly contribute to saleable product; the nonproductive greenhouse gas emission impact in a manufacturing environment. A case study is included depicting results from a pilot implementation in a packaging line in a brewery.

There is an increasing focus on sustainability in manufacturing industries. Operations management and plant/process control have a significant impact on production efficiency and hence environmental footprint. Information systems are an increasingly important tool for monitoring, managing and optimising production efficiency and resource consumption. An advanced Utilities Management System (UMS), that operates on the G2® real-time intelligent systems platform, has been developed at the Yatala brewery, Australia. An important characteristic of the UMS is its strong integration with the existing information and automation systems at the plant. The tight integration was required to maximise effectiveness and ease of use as well as to minimise development effort and cost.

A distributed fuzzy system is a real-time fuzzy system in which the input, output and computation may be located on different networked computing nodes. The ability for a distributed software application, such as a distributed fuzzy system, to adapt to changes in the computing network at runtime can provide real-time performance improvement and fault-tolerance. This paper introduces an Adaptable Mobile Component Framework (AMCF) that provides a distributed dataflow-based platform with a fine-grained level of runtime-reconfigurability. The execution location of small fragments (possibly as little as few machine-code instructions) of an AMCF application can be moved between different computing nodes at runtime. A case study is included that demonstrates the applicability of the AMCF to a distributed fuzzy system scenario involving multiple physical agents (such as autonomous robots). Using the AMCF, fuzzy systems can now be developed such that they can be distributed automatically across multiple computing nodes and are adaptable to runtime changes in the networked computing environment. This provides the opportunity to improve the performance of fuzzy systems deployed in scenarios where the computing environment is resource-constrained and volatile, such as multiple autonomous robots, smart environments and sensor networks.

Many manufacturing environments are only semi-automated and still require operator input for key aspects of operations. Decision Support Systems (DSS) are a useful means of improving the performance of human-in-the-loop control aspects of operations. Passive DSS systems can empower operators to make optimal decisions while still allowing them to retain control of the plant. However in practice DSS systems are not always utilised to their full potential and 'operator discretion' can result in sub-optimal plant performance. An on-line real-time DSS has been implemented to help operators minimise redundant cleaning cycles in a brewery. Autonomous monitoring functionality has been developed to provide visibility and transparency of the actual use and performance of the DSS.

Real-time intelligent system technology can be an effective means of optimising industrial operations. A large, intelligent Utilities Management System (UMS) has been developed at Foster's brewery in Yatala, Australia. The system is based on the G2 real-time intelligent system platform and it contains a number of modules that have been designed to improve the energy and utilities consumption of brewery operations. Of particular interest was the management and optimisation of the brewery's extensive Clean-In-Place (CIP) systems. The UMS monitors every step of every cycle of each CIP set and its real-time mass balance estimates chemical use and loss. An operator decision support system has also been developed that helps to minimise any redundant cleaning cycles. This system has been instrumental in lowering the bulk caustic soda consumption (per hectolitre of beer) of Yatala brewery by over 57% between the financial years F05 and F08.