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This paper investigates factors affecting business analytics (BA) in software and systems development projects. This is the first study to examine business analytics continuance in projects from Pakistani software professional's perspective. The data was collected from 186 Pakistani software professionals working in software and systems development projects. The data was analyzed using partial least squares structural equation modelling techniques. Our structural model is able to explain 40% variance of BA continuance intention, 62% variance of satisfaction, 69% variance of technological compatibility, and 59% variance of perceived usefulness. Technological compatibility and perceived usefulness are the significant factors that can affect BA continuance intention in software and systems projects. Surprisingly the results show that satisfaction does not affect BA continuance intention.

Digitalization and Industry 4.0 promote a fundamental technological disruption that requires industry, research and government institutions to revisit their roles within the innovation ecosystems. Actors in this environment need to understand value co-creation during interaction and collaboration. The purpose of this study is to investigate the triple helix collaborative capabilities in an Industry 4.0 ecosystem context. The case under study is a Finnish national publicly funded research project involving five global manufacturers, three research institutions, and several small-and medium-size enterprises (SMEs). The results demonstrate that practices related to adaptivity, experience sharing, SME co-innovation and scale up can enable the ecosystem to be managed in a dynamic way. Yet, this type of operation requires the adoption of the ecosystem approach with mutual trust, intensive collaboration and the identification of common aims among the project participants. The presented co-innovation model can be used to design innovation ecosystem projects in the future.

The need for automated production plans has evolved over the years due to internal and external drivers like developed products, new enhanced processes and machinery. Reconfigurable manufacturing systems focus on such needs at both production and process planning level. The age of Industry 4.0 focused on mass customization requires computer aided planning techniques that are able to cope with custom changes in products and explores intelligent algorithms for efficient scheduling solutions to reduce lead time. This problem has been categorized as NP-Hard in literature and is addressed by providing intelligent heuristics that focus on reducing machining time of the products at hand. However, as 70% of the lead time is consumed in non-value added tasks, it is fundamental to provide modular solutions that can reduce this time and handle part variety. To address the subject, this paper focuses on the generation of automated process plans for a single machine problem while focusing on reducing time lead time. Two evolutionary algorithms (EAs) have been proposed and compared to answer complex problem of process planning. A modified genetic algorithm (GA) has been proposed in addition to cuckoo search (CS) heuristic for this discrete problem. On testing with selected benchmark part ANC101, significant improvement was seen in terms of convergence with proposed EAs. Moreover, a novel Precedence Group Algorithm (PGA) is proposed to generate quality input for heuristics. The algorithm produces a set of initial population which significantly effects the performance of proposed heuristics. For the discrete constrained process planning problem, GA outperforms CS providing 10% more feasible scheduling options and three times lesser run time as compared to CS. The proposed technique is flexible and responsive in order to accommodate part variety, a necessary requirement for reconfigurable systems.