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Purpose -The purpose of this study is to explore the required changes, outline business potential and envisage the key steps that a networked manufacturing industry needs to take to reach more sustainably performing manufacturing in the future. Design/methodology/approach -The paper utilises a visionary road-mapping approach to study the required changes and the business potential related to sustainable development in the manufacturing industry. Findings -The results were summarised in three sub-roadmaps empowerment of stakeholders, increase efficiency and creation of new performance criteria. On the basis of the summary of the sub-roadmaps, the framework was configured to describe the opportunities and challenges of sustainable business development in the European manufacturing industry.Research limitations/implications -A clear implication of this study is that a more system-oriented approach, new models for collaboration between network actors and transparently shared network-level KPIs are required before further steps towards a sustainable manufacturing industry can be taken. In addition, sustainability-driven business models are required to specify these changes concretely. Practical implications -The presented sub-roadmaps and framework summarising them could provide new insights to business practitioners exploring business potential of sustainability. Social implications -Understanding about the road-mapping process as tool that enables interaction and envisioning between different stakeholders could also have social implications supporting shared industry-level learning processes. Originality/value -Studies of sustainability within the manufacturing industry have focused mainly on green issues in supply-chain management or corporation-level governance models and reporting practices. The paper presents a broader view of sustainable development and recognises networked business as part of the solution.

Large amounts of data are increasingly gathered in order to support decision making processes in asset management. The challenge is how best to utilise the large amounts of fragmented and unorganised data sets to benefit decision making, also at fleet level. It is therefore important to be able to utilize and combine all the relevant data, both technical and economic, to create new business knowledge to support effective decision making especially within diverse situations. It is also important to acknowledge that different types of data are required in different decision making context. A review of the literature has shown that decision making situations are usually categorized according to the decision making levels, namely strategic, tactical and operational. In addition, they can be classified according to the amount of time used in decision making. For example, two situations can be compared: 1) optimization decision where a large amount of time and consideration is used to determine an optimum solution, and 2) decisions that need to be made instantly. Fleet management of industrial assets suffers from a lack of asset management strategies in order to ensure the correct data is collected, analysed and used to inform critical business decisions with regard to fleet management. In this paper we categorize the decision making process within certain situation and propose a new framework to identify fleet decision making situations.

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The paper provides on empirical example of co-innovation process within an Industry4.0 ecosystem between Finnish IT sector, service designers, researchers and the forest industry companies. Based on empirical evidence the paper summarizes some of key challenges in building business impact from digitalization.