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The purpose of this paper is twofold. First, it is to introduce a knowledge‐based system for planning processes for families of final products, instead of component items, be they parts or assemblies. Second, it is to demonstrate the feasibility and potential of a prototypical system developed for planning processes families for truck families from a multinational company.

The authors first identify the challenges in planning process families, including data and knowledge representation and constraint handling. To accommodate these challenges, the paper adopts the integrated product and process structure (IP²S) and colored timed Petri nets (CTPNs) in the proposed knowledge‐based process family planning system. On top of the IP²S and CTPNs, XML‐based knowledge representation is employed to alleviate the difficulties in modelling complex product and process family data and planning knowledge while enabling information exchange across different operating platforms. In addition, in accordance with the correspondence between PNs and knowledge‐based systems, a mechanism is designed to cope with the generation of production rules, which model constraints.

The proposed system is able to automatically generate production processes for customized products. At a higher level, such production processes provide input (e.g. operations, machines) to downstream activities for planning process details to manufacture component parts or component assemblies.

Traditional trial and error approaches to planning processes limit production performance improvement when companies need to timely produce diverse customized products. Knowledge‐based systems should be developed to help companies better plan production processes based on the available manufacturing resources.

Unlike most reported studies addressing either detailed process planning or assembly planning for component parts or component assemblies, this study tackles process planning for final products, in attempting to maintain production efficiency from a holistic view.

Motivated by recent research indicating that the operational performance of an enterprise can be enhanced by building a supporting data-driven environment in which to operate, this paper presents a simulation framework that enables an examination of the effects of applying smart manufacturing principles to conventional production systems, intending to transition to digital platforms.

To investigate the extent to which conventional production systems can be transformed into novel data-driven environments, the well-known constant work-in-process (CONWIP) production systems and considered production sequencing assignments in flowshops were studied. As a result, a novel data-driven priority heuristic, Net-CONWIP was designed and studied, based on the ability to collect real-time information about customer demand and work-in-process inventory, which was applied as part of a distributed and decentralised production sequencing analysis. Application of heuristics like the Net-CONWIP is only possible through the ability to collect and use real-time data offered by a data-driven system. A four-stage application framework to assist practitioners in applying the proposed model was created.

To assess the robustness of the Net-CONWIP heuristic under the simultaneous effects of different levels of demand, its different levels of variability and the presence of bottlenecks, the performance of Net-CONWIP with conventional CONWIP systems that use first come, first served priority rule was compared. The results show that the Net-CONWIP priority rule significantly reduced customer wait time in all cases relative to FCFS.

Previous research suggests there is considerable value in creating data-driven environments. This study provides a simulation framework that guides the construction of a digital transformation environment. The suggested framework facilitates the inclusion and analysis of relevant smart manufacturing principles in production systems and enables the design and testing of new heuristics that employ real-time data to improve operational performance. An approach that can guide the structuring of data-driven environments in production systems is currently lacking. This paper bridges this gap by proposing a framework to facilitate the design of digital transformation activities, explore their impact on production systems and improve their operational performance.

This chapter provides an extensive review of literature on the interaction between and interdependence of informal and formal working practices in various workplace settings. The aim of the chapter is to elucidate the organisational, managerial, human relations and social factors that give rise to informal work practices and strategies, on the shop-floor not only at workers and work group levels but also at supervisory and managerial levels. This chapter helps the reader to understand the informal work practice of making a plan (planisa) in a deep-level mining workplace.

Artificial Intelligence, Cybernetics, Production Describes the ELEKTRA reflective production rule interpreter, which differs from other interpreters by providing considerable support for meta‐level inference and reflection. Also describes the representations employed in the system, in which the control problem in production systems can be solved by increasing use of rules. Introduces the reflective properties of the system and gives examples. Shows that the interpreter on which the entire ELEKTRA system runs can be implemented as ELEKTRA rules.

Any kind of production flow is obtained not from individual production organizations but from a more or less widespread Production Network of interconnected production modules located in different places and times. All of these modules are, consciously or not, necessarily connected, interacting and cooperating in a coordinated way to combine and arrange, step by step, the factors, materials, components, manpower, machines and equipment to obtain flows of products’ final goods, in particular’ and to sell these where there is a demand for them. The purpose of this paper is to determine, in logical and formal terms, the minimum conditions that bring about the formation of production networks and to discover the laws that explain their dynamics over time.

At the global level, the continuous and accelerated economic progress of mankind is witnessed. There is an increase in the quantity and quality of satisfied and yet to be satisfied needs, of attained and yet to be attained aspirations. The increase in productivity and in quality has become unstoppable and appears to guide the other variables in the system. It is natural to ask who produces and governs these phenomena. It does indeed seem there is a Ghost in the “Production” machine whose invisible hand produces growing levels of productivity and quality, increases the quality and quantity of satisfied needs and aspirations and reduces the burden of work, thus producing increasingly higher levels of progress in the entire economic system. This conceptual framework gives a simple answer: there is nothing metaphysical about this evolution towards unstoppable and irreversible progress, and it is produced by the spontaneous genesis and activity of selfish nodes and governed by the rules and laws of the production networks.

The author has identified ten “rules of selfish behavior” on the part of the nodes, whose application necessarily and inevitably produces three evolutionary dynamic processes “which refer to the network as an entity” which the author has called the “rules of the production networks” to emphasize their cogency: continual expansion, elasticity-resiliency and continual improvement in performance. The cognitive and creative processes that characterize the nodes do not allow us to predict the actual evolution of production networks; nevertheless, if it is assumed that nodes “consciously or not” follow the 10 “rules of selfish behavior”, then several typical trends, or behavioural schema, can be deduced which the author has called as the “laws of networks”, to highlight their apparent inevitability and cogency.

More than any other structure, Production Networks display Holland’s features and Arthur’s properties as their modules, viewed as autonomous entities with cognitive functions, represent a collectivity of agents that interact and exchange information with their environment to maintain over time their internal processes through adaptation, self-preservation, evolution and cognition, making individual and collective decisions as part of a network of micro behaviours.

This new conception of production through production networks, which takes into account the “rules” and “laws” regulating their behaviour, also sheds new light on the development of networks and their natural tendency to become globalized.

Although the concept of a network is becoming more popular in economic and business studies, it is yet to see an interpretation of production as deriving exclusively from the actions of increasingly larger networks. This paper presents an integrated view of production that does not discard the notion that production is carried out by organizations and companies but introduces the broader concept of the integration among organizations, which must be interpreted as nodes of a broader network that produces the flows of all the components needed to obtain the flow of a specific product. This represents an innovative view that will help us in understanding the difficulties policymakers encounter in governing production and controlling the basic variables that characterize it, specifically productivity, quality, quantity, prices and value. This perspective also allows to derive rules and laws for the behaviour of production networks that appear to be cogent and unvarying over time.

Generative Production Systems are generative design computation that is linked to optimal physical production. They can improve the design and production of products which have unique geometries. The purpose of this paper is to introduce a preliminary methodology for Generative Production Systems.

The paper takes the form of a literature review investigating the structures and formats of successful methodologies. The literature review also investigates Generative Production Systems' theoretical foundations, development and implementations.

The potential of Generative Production Systems is restricted because enabling information is fragmented and difficult to access. In other fields, such barriers have been overcome through the introduction of methodologies that provide guiding principles, rules and strategies (P‐R‐S). The P‐R‐S type of methodology structure is suitable for Generative Production Systems.

The use of Generative Production Systems requires more upfront investment than the use of CAD/CAM software packages. However, they require little, or no, human input after they are set‐up. The preliminary methodology provides structured guidance about how to set up a Generative Production System. Further, the explanatory text enables wider understanding of Generative Production Systems, and how they can improve design and production.

An analysis is provided for the structure and format of successful methodologies in general. A preliminary methodology for Generative Production Systems is introduced. Further, the paper provides an overview of the disadvantages and advantages of Generative Production Systems compared to typical CAD/CAM software packages.

The purpose of this paper is to demonstrate the value of adopting an organizational ecological perspective to explore behavioural barriers in a UK operations & production management (OPM) setting.

An ethnographic case study approach was adopted with a narrative ecological stance to deconstruct the perceived realities and the origins of the inter‐departmental barriers applying Scott‐Morgan's unwritten rules methodology.

Despite an improvement in the physical proximity of the production and quality control departments, the qualitative approach revealed that latent, socially constructed drivers around management, interaction and communication reinforced inter‐departmental barriers. Conflicting enablers were ultimately responsible derived from the organizational structure, which impacted the firm's production resources.

As a case study approach, the specificity of the findings to this OPM setting should be explored further.

The paper demonstrates the use of theoretical frameworks in a production and manufacturing organization to provide insights for maximising process effectiveness. Using the organizational ecological perspective to uncover the socially constructed unwritten rules of the OPM setting beneficially impacted on operational effectiveness.

The paper contributes to organization ethnography literature by providing a detailed empirical analysis of manufacturing and services behaviour using an organizational ecology perspective. The example demonstrates that “qualitative” research can have real world impact in an advanced operational context. It also contributes to an ecological or complex adaptive systems view of organizations and, inter alia, their supply chains.

An analysis of the productions and rules in the way they are used in artificial intelligence systems is presented. The proposed new definition for productions refers to a large number of types of productions which may be found in the literature on AI systems. This definition emphasizes in the most general way those production components which are important both for theory and for practice and which for some reasons remained unnoticed by many researchers. These components are implemented in a theoretical formalism which concludes the paper.

In the current competitive environment, each company faces a number of challenges: quick response to customers’ demands, high quality of products or services, customers’ satisfaction, reliable delivery dates, high efficiency, and others. As a result, during the last five years many firms have proceeded to the adoption of enterprise resource planning (ERP) solutions. ERP is a packaged software system, which enables the integration of operations, business processes and functions, through common data‐processing and communications protocols. However, the majority, if not all, of these systems do not support the production scheduling process that is of crucial importance in today’s manufacturing and service industries. In this paper, the authors propose a knowledge‐based system for production‐scheduling that could be incorporated as a custom module in an ERP system. This system uses the prevailing conditions in the industrial environment in order to select dynamically and propose the most appropriate scheduling algorithm from a library of many candidate algorithms.

This article describes how the n‐Cube expert system development tool can assist in item classification using the Universal Decimal Classification (UDC) standard. The n‐Cube makes use of a tree classification structure with associated rules and default inheritance features. Any information known about a particular classification is a combination of the defaults known about that class, as well as the defaults associated with any of its parent classes. As a result, many of the problems associated with simple rule‐based systems are overcome.