IndexProduction Management OverviewImplementationConclusionsThe manufacturing sector has conventionally been considered the backbone of economic development and impacts the social, environmental and political well-being of any nation. Over the last two centuries, this sector has evolved by leaps and bounds, thanks to scientific advances, the effect of globalization and the growth of the world population. A multitude of factors such as the availability of raw materials, labor costs, automation, logistics and government policies contribute to the dynamics of a production system and its profitability. However, with technological and management innovations, players such as Toyota have successfully transformed traditional manufacturing principles and have also stimulated further research and exploration in this area. With their integrated socio-technical production system called "Toyota Production Systems" (TPS), Japan has achieved higher productivity and lower operating costs despite the lack of natural resources and geographical disadvantage compared to its American and European counterparts. Say no to plagiarism. Get a tailor-made essay on "Why Violent Video Games Shouldn't Be Banned"? Get an Original Essay Overview of Production Management “Toyota Production Systems” includes techniques such as KANBAN (inventory planning), JIDAKA (quality control), ISHAKAWA (root cause analysis), and MUDA (waste reduction). In all these methods various parameters such as cycle times, waste, inventory counts, demand, forecasts and machine failures are recorded and used for optimization analysis. These are crucial indicators that guide management towards improvement and diagnosis objectives. For example, to identify MUDA (non-value added waste) the study of the temporal movement of a process is carried out. Likewise, ratings of weight, time, temperature, noise, light, quality, etc. are mandatory for the application of these improvement methods. These measurements were generally expensive and data-intensive before the digital age. To remain profitable and relevant in today's competitive market, manufacturers must maintain high quality standards; low production costs and optimize their processes by adapting intelligent manufacturing (SM) methods. The current wave of digital transformations offers game-changing opportunities with deep insights that were previously untapped and invisible. What distinguishes the buzzword “big data analytics” from other analytical methods are the 5 Vs of data, namely “volume, variety, velocity, veracity, and value.” Large manufacturing companies sit on huge piles of data generated all the time by their 5Ms (labor, materials, machines, methods and money). The latest expeditions in the field of Big Data, artificial intelligence, availability of high-end computing power and low sensor costs have enabled the development of “data, information and knowledge (KS) systems”. The synergy of knowledge systems between different business domains (production, maintenance, finance, human resources, supply chain) to achieve goals, business objectives or corporate social responsibility is known as "Manufacturing Intelligence" (MI). This article explores the MI architecture, various types of manufacturing KPIs, methods for capturing them, and their applications in SMs. Key Performance Indicators: Before proceeding further, it is important to outline the difference between 'indicators', 'performance indicators' and 'key point indicators'. An indicator can be any measure of a quantity in the company such as thetotal number of shift hours in a month (shift hours). Indicators are generally meaningless until used in a context or contrast known as "benchmarking". A performance indicator is a relative term that tracks the value of an indicator against a set value or target value. The number of “shift hours in a month times total hours in a month” is a performance indicator of facility utilization. (Shift hours/hours in the month) There may be innumerable performance indicators that may or may not have any meaning to management and which may be carefully ignored. Key performance indicators are those critical performance indicators that measure "how much" has been achieved towards the objective of "adding business value". KPIs are at the heart of any performance measurement and goal-setting system. When used correctly, they are one of the most powerful management tools a business has. KPIs provide a definitive measure of production and resource performance. So, continuing from the previous set of examples, the KPI would be the number of “productive” shift hours times the total hours in a month. KPIs must have certain characteristics, as explained in: Responsibilities: Each KPI should have a team or manager responsible for measuring and working on its results. Assimilation: They should be quantifiable, accurate and understandable by the shop floor and higher up. Timely: KPIs should be measured frequently, reflecting current priorities. In smart manufacturing KPIs are a real-time measurement. Relevant: Measures should support strategic organizational objectives and add value. Consistent: KPIs should not conflict with other performance measures. KPIs are expensive to acquire and analyze, so only the most relevant ones should be selected. As mentioned above, it is management's prerogative to choose the most relevant performance measures. Figure 4 presents the most commonly used performance measures by number. We searched 251 peer-reviewed publications between the years 1979-2009 to present this PI data related to maintenance operations representing 20% ​​of total manufacturing costs. It can be observed that "cost" and "OEE (overall equipment effectiveness) are the most frequent performance indicators. MI architecture: from now on we will use the term MI (Manufacturing Intelligence) to describe any knowledge system that collects and analyzes big data from the 5Ms to optimize and achieve business objectives. Improve “supervisory control and data acquisition” (SCADA) by adding middleware or “intelligent substations”. of equipment via wired and wireless networks. It may have components such as a microcontroller or it may be a pure software application. Depending on the requirements, it is designed to provide a flexible interface with different control systems and data collection capabilities The MTconnect agent for computer numerical control (CNC) machines is a good example of such a system. It is a protocol that collects data from the NC machine via "adapters" and transmits it to an "agent" in XML format. It has been widely adopted by manufacturers. The key functions of MI: Aggregation: ability to transmit data across multiple sources. Contextualization: A data model structured and organized like the ISA-95 standard. Analytics: Enables data analytics across various domains. Visualization: Having tools like dashboards to make data understandable to decision makers. Propagation: automate the.