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Industry 4.0 envisions a future of networked production where interconnected machines and business processes running in the cloud will communicate with one another to optimize production and enable more efficient and sustainable individualized/mass manufacturing. However, the openness and process transparency of networked production in hyperconnected manufacturing enterprises pose severe cyber-security threats and information security challenges that need to be dealt with. The paper aims to discuss these issues.

This paper presents a distributed trust model and middleware for collaborative and decentralized access control to guarantee data transparency, integrity, authenticity and authorization of dataflow-oriented Industry 4.0 processes.

The results of a performance study indicate that private blockchains are capable of securing IoT-enabled dataflow-oriented networked production processes across the trust boundaries of the Industry 4.0 manufacturing enterprise.

This paper contributes a decentralized identity and relationship management for users, sensors, actuators, gateways and cloud services to support processes that cross the trust boundaries of the manufacturing enterprise, while offering protection against malicious adversaries gaining unauthorized access to systems, services and information.

The purpose of this paper is to introduce an agent‐based model for partners' selection in inter‐organizational supply‐chains. Screening activities undertaken by the main contracting organization for the case of large project deliveries and parameters to be considered are identified, a multi‐level quantitative framework to support the decision‐making process in effectively selecting an efficient and a compatible set of partners is proposed. Different agent behaviours are introduced and modelled; their impact on the results of the bidding processes is analysed through a simulation model specifically developed for this purpose.

Among the biggest challenges, companies confront with is the finding of appropriate action lines, methods or IT tools to overcome problems introduced by the saturation of the market, fierce competition and downfall of entry‐barriers. The paper uses a paradigm that is seen as one of the most promising in responding to the needs of modern companies: the agent‐based paradigm.

Designing an efficient supply network greatly influences the level of uncertainty that a company is encountering. An agent‐based model may efficiently help the inter‐organizational networks partners' selection process. To operate effectively in open environments such as supply chains, agents must be adaptive. System/agents performance can be improved by doing what‐if simulation runs.

A further possible work direction, considering that nowadays a society must find a compromise between being internationally competitive and having a healthy environment, would be the introduction in the model of a special type of agent, namely the project delivery waste management agent.

In addition to contributing to the scientific literature, this research also aims at supporting practical business situations in the industry by creating a normative decision‐making model based on an agent‐based approach. The simulation model developed supports the tuning of parameters used in the evaluation process and makes possible analyzing the effect of different agent behaviours.

Research that identifies means to constrain the pre‐contracting selection problem for specific delivery chains as bridge constructions is needed. The paper details the particularities of such chains, and makes a presentment of roles of actors with impact on the assessment process. One specific of the approach is that in addition to participants as suppliers and main contracting organization also issues (goals) relevant in the supplier's appraisal process are represented by independent agents.

The purpose of this paper is to examine challenges and potential of big data in heterogeneous business networks and relate these to an implemented logistics solution.

The paper establishes an overview of challenges and opportunities of current significance in the area of big data, specifically in the context of transparency and processes in heterogeneous enterprise networks. Within this context, the paper presents how existing components and purpose-driven research were combined for a solution implemented in a nationwide network for less-than-truckload consignments.

Aside from providing an extended overview of today’s big data situation, the findings have shown that technical means and methods available today can comprise a feasible process transparency solution in a large heterogeneous network where legacy practices, reporting lags and incomplete data exist, yet processes are sensitive to inadequate policy changes.

The means introduced in the paper were found to be of utility value in improving process efficiency, transparency and planning in logistics networks. The particular system design choices in the presented solution allow an incremental introduction or evolution of resource handling practices, incorporating existing fragmentary, unstructured or tacit knowledge of experienced personnel into the theoretically founded overall concept.

The paper extends previous high-level view on the potential of big data, and presents new applied research and development results in a logistics application.