The B2B sales process is undergoing substantial transformations, fuelled by advances in information and communications technology and specifically by artificial intelligence (AI). The premise of AI is to turn vast amounts of data into information for superior knowledge creation and knowledge management in B2B sales. In doing so, AI can significantly alter the traditional human-centric sales process. In this article, we describe how AI impacts the B2B sales funnel. Specifically, for each stage of the funnel, we describe key sales tasks, explicate the specific contributions that AI can bring and clarify the role that human contributions play at each step of the AI-enabled sales funnel. We also outline managerial considerations to maximize the contributions from AI and people in the context of B2B sales.

Biological collective systems have been an important source of inspiration for the design of production systems, due to their intrinsic characteristics. In this sense, several high level engineering design principles have been distilled and proposed on a wide number of reference system architectures for production systems. However, the application of bio-inspired concepts is often lost due to design and implementation choices or are simply used as heuristic approaches that solve specific hard optimization problems. This paper proposes a bio-inspired reference architecture for production systems, focused on highly dynamic environments, denominated BIO-inspired Self-Organising Architecture for Manufacturing (BIOSOARM). BIOSOARM aims to strictly adhere to bio-inspired principles. For this purpose, both shopfloor components and product parts are individualized and extended into the virtual environment as fully decoupled autonomous entities, where they interact and cooperate towards the emergence of a self-organising behaviour that leads to the emergence of the necessary production flows. BIOSOARM therefore introduces a fundamentally novel approach to production that decouples the system’s operation from eventual changes, uncertainty or even critical failures, while simultaneously ensures the performance levels and simplifies the deployment and reconfiguration procedures. BIOSOARM was tested into both flow-line and “job shop”-like scenarios to prove its applicability, robustness and performance, both under normal and highly dynamic conditions.

The variability in the market conditions is growing in terms of its frequency of change and range of diversity. In response to this new industrial panorama, research on production systems is aiming to achieve highly reconfigurable shop floors. Frequent changes in such systems require also frequent re-planning with updated information. In this regard the Continuous Precise Workload Control method, is a recent approach aiming at precise control of workload in shop floor with the use of direct load graphs. Supported by a multi-agent platform, it generates dynamic non-periodic release decisions exploiting real time shop floor information. The study in this paper is two folded; (1) the presented workload approach is defined in terms of eight dimensions of the workload control concept in order to highlight its distinctive characteristics and (2) the impact of idleness penalty factor is analyzed by an experiment design in order to investigate its effect on the job release decision. The results show that the idleness penalty factor decreases the idleness of the resources up to a point where the adverse effect is initiated.

Modern machineries are often cyber-physical system-of-systems controlled by intelligent controllersfor collaborative operations on the productions of complex products. To assure theefficiency and effectiveness, a consolidation of concerns across managerial levels, product lifecyclestages, and product lines or families becomes necessary. This calls for a common informationinfrastructure in terms of ontology, models, methods and tools. For industrial manufacturerssubjected to increased cost pressure and market volatility, the availability of such an informationinfrastructure would promote their abilities of making optimized and proactive decisions andthereby their competitiveness and survivability. This paper presents a virtual environment thatconstitutes an information infrastructure for the management and development of evolvableproduction systems (EPS) in manufacturing. It adopts mature modeling frameworks throughEAST-ADL for an effective model-based approach. The contribution is centered on a meta-modelthat offers a common data specification and semantic basis for information management acrossproduct lifecycle, models and tools, both for resource planning and for anomaly treatment. Aprototype tool implementation of this virtual environment for validation is also presented.

This article describes the first version of a tool designed to infer the network characteristics of JADE-based multiagent systems. The rationale behind the tool is that systems in general and multiagent system in particular, often have some hidden dynamics that contribute to the emergence of desired and undesired characteristics. Traditional sniffing tools simply display the message exchange. The presented tool goes therefore beyond simple message sniffing and infers the agents' network based on the ongoing interactions and codifies it a format suitable for further processing in specialized network analysis tools. In particular the prosped tool identifies the most frequently used communication links and the messages associated with them. To demonstrate the behavior of the tool an exploratory system based on the Evolvable Production System paradigm is discussed and analyzed.

The increasing market fluctuations and customized products demand have dramatically changed the focus of industry towards organizational sustainability and supply chain agility. Such critical changes inevitably have a direct impact on the shop-floor operational requirements. In this sense, a number of innovative production paradigms emerged, providing the necessary theoretical background to such systems. Due to similarities between innovative modular production floors and natural complex systems, modern paradigms theoretically rely on bio-inspired concepts to attain the characteristics of biological systems. Nevertheless, during the implementation phase, bio-inspired principles tend to be left behind in favor of more traditional approaches, resulting in simple distributed systems with considerable limitations regarding scalability, reconfigurable ability and distributed problem resolution.

This paper analyzes and presents a brief critical review on how bio-inspired concepts are currently being explored in the manufacturing environment, in an attempt to formulate a number of challenges and properties that need to be considered in order to implement manufacturing systems that closely follow the biological principles and consequently present overall characteristics of complex natural systems.

This work introduces a phenomenographic analysis of the concept of flexibility in the domain of production science. Flexibility is a cornerstone in the education of industrial and production engineers; however, it still appears as a broadly and even inconsistently defined construct. In order to clarify what is or should be learnt, this work analyses first the established literature to extract a working characterisation of the flexibility concept. The resulting understanding is then used to represent the experts' perception of the topic, which in turn is used as the ideal level of understanding that a student should achieve herself/himself when studying such a concept. The second phase of the work aims at disclosing and classifying the multifaceted perceptions of flexibility that two classes of industrial engineering students have after two courses in which the focal concept of manufacturing flexibility has been presented using two different approaches. The research is based on a survey completed by students. The data collected have consequently been structured into a finite set of clusters according to: a) the level of understanding of the key concept; and b) the nature of the shown knowledge. The classification is, then, the basis for defining an epistemologically sound approach to develop suitable teaching and learning activities to ensure optimal acquisition of the concept of flexibility.

Sustainability is currently one of the biggest challenges and drivers of manufacturing industry. With traditional automation approaches becoming evermore inadequate to support sustainable mass customized production, the research focus is moving towards agile systems that enact companies with the ability to quickly reconfigure their shop-floors by seamlessly deploying or removing modules. Such systems are envisioned as key for attaining a profitable and sustainable industrial development. In this sense, this paper attempts to characterize an innovative approach that relies on bio-inspired concepts as the main control mechanism, in order to foster sustainability by attaining the necessary shop-floor agility. Furthermore an experimental setup is presented and the results are analysed, in order to understand the influence and impact of the main properties of the approach towards the system performance.

The variability in the market conditions is growing in terms of its frequency of change and range of diversity. In response to this new industrial panorama, research on production systems is aiming to achieve truly reconfigurable shop floors. Frequent changes in such systems require also frequent re-planning with updated information. In this regard the Continuous Precise Workload Control method, is a recent approach aiming at precise control of workload in the shop floor with the use of direct load graphs. Supported by a multi-agent platform, it generates dynamic non-periodic release decisions exploiting real time shop floor information. The study in this paper is two folded; (1) in order to highlight its distinctive characteristics, the presented workload approach is defined in terms of eight dimensions of the workload control concept and (2) the penalty of idleness which affects the decision of release is analyzed by an experiment design in order to investigate its correlation with two critical parameters, norm value and assessment range. The results show that the idleness penalty factor decreases the idleness of the resources up to a point where the adverse effect is initiated. Besides there are strong indications towards the correlation of idleness penalty factor with the norm value.

The word “flexibility” is often abused and not univocally understood within the manufacturing science domain and in particular in the context of industrial automation. Since the raise of industrial robots in the 1960’, different researchers and practitioners have been using such a common word with different meanings. This has generated a very articulated concept, spanning from capability of a system to increase the production volumes to ability to handle product mix variation. Several authors have tried to count the current meanings of such a word in manufacturing and someone arrived to more than 50!. In spite of this fuzziness in both the definition and scope, the concept of flexibility remain one of the cornerstones in the curriculum of industrial and production engineers, and it appears in many courses along the bachelor and master studies. The apparent paradox that higher education institutions have to teach things that are not even well-defined and agreed in the scientific world is, in fact, quite a usual practice. In order to clarify what is, or should be, learnt this work analyzes first the established literature to extract a “working” characterization of the flexibility concept. The resulting understanding is then used to represent the experts’ perception of the topic which in turn is used as ideal level of understanding that a student should achieve her/himself when studying such a concept.   

The second phase of the work aims at disclosing and classifying the multifaceted perceptions of flexibility that two different classes of industrial engineering students have after two courses in which the focal concept of manufacturing flexibility has been presented using two different approaches. The research is based on a phenomenographic analysis of a series of well-designed interviews to the students. The collected data have consequently been structured in a finite set of clusters according of: (1) the level of understanding of the key concept (as expressed in the Bloom’s taxonomy) and (2) the nature of the shown knowledge (as presented in the SOLO taxonomy). The classification is then the basis for defining an epistemological sound approach to develop suitable teaching and learning activities to ensure optimal acquisition of the concept of flexibility.