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Productivity is often cited as a key barrier to the adoption of metal laser-based powder bed fusion (ML-PBF) technology for mass production. Newer generations of this technology work to overcome this by introducing more lasers or dramatically different processing techniques. Current generation ML-PBF machines are typically not capable of taking on additional hardware to maximise productivity due to inherent design limitations. Thus, any increases to be found in this generation of machines need to be implemented through design or adjusting how the machine currently processes the material. The purpose of this paper is to identify the most beneficial existing methodologies for the optimisation of productivity in existing ML-PBF equipment so that current users have a framework upon which they can improve their processes.

The review method used here is the preferred reporting items for systematic review and meta-analysis (PRISMA). This is complemented by using an artificial intelligence-assisted literature review tool known as Elicit. Scopus, WEEE, Web of Science and Semantic Scholar databases were searched for articles using specific keywords and Boolean operators.

The PRIMSA and Elicit processes resulted in 51 papers that met the criteria. Of these, 24 indicated that by using a design of experiment approach, processing parameters could be created that would increase productivity. The other themes identified include scan strategy (11), surface alteration (11), changing of layer heights (17), artificial neural networks (3) and altering of the material (5). Due to the nature of the studies, quantifying the effect of these themes on productivity was not always possible. However, studies citing altering layer heights and processing parameters indicated the greatest quantifiable increase in productivity with values between 10% and 252% cited. The literature, though not always explicit, depicts several avenues for the improvement of productivity for current-generation ML-PBF machines.

This systematic literature review provides trends and themes that aim to influence and support future research directions for maximising the productivity of the ML-PBF machines.

The objective of the study is to investigate the factors that differentiate long-term shareholder value (LTSV) creating firms from LTSV destroying firms.

Through the review of literature, the hypothesis for the study is developed. To test the hypothesis, the study collects data from S&P BSE 500 companies listed in Bombay Stock Exchange (BSE). Based on the average overall return to shareholders for the period from year 1991 to 2019, the study identifies top 25 LTSV creating and LTSV destroying firms. The top 50 firms form the basis of this study. The study uses descriptive statistics and independent sample t-test to test the hypothesis of the study.

Among the variables investigated such as capital management policy and effective capital management practices, business and financial strategy, intellectual capital strategy, relational capital strategy and human capital strategy, the study found effective capital management and governance as a long-term source of value for shareholders.

The study highlights the importance of inclusion of value-relevant information in the annual report of the company. The study also supports the proposition that discretionary disclosure of intangible assets is relevant for the market to enable market participants to reasonably comprehend the fair value of the firm.

Adoption of a reporting framework that ensures the availability of all value-relevant information including off-balance-sheet resources is in the interest of the investors and policymakers alike.

This is a first such study exploring the value-relevant information and the source of long-term value for listed firms.