An introduction to Building a Smart Laboratory 2015
Tis introduction sets out the changing demands being put upon laboratories both by their customers and by technological developments. Only by becoming smart – as the rest of this guide outlines – can laboratories respond effectively.
Unlike some industries, going digital in the laboratory has proceeded at a relatively leisurely pace. Aſter four decades or so, a growing number of laboratories can now consider themselves to be ‘electronic’ or ‘paperless’. Te journey started with data capture, data processing, and laboratory automation. It went on to storing and managing digital data from disparate sources, with the evolution of systems such as LIMS and ELNs, which serve to collate and add context to laboratory data. Troughout this period, the underlying business drivers were process efficiency, laboratory productivity, and error reduction. All purchases had to be justified against these criteria, as industries sought competitive advantage by reducing their costs and time-to-market. Informatics tools therefore focused primarily on eliminating waste (time, effort, errors) while providing management with the added bonus of a perspective on laboratory performance.
Cost-reduction no longer enough
Different business issues are now having an impact on laboratory operations, and hence
4
putting new demands on the ‘smart laboratory’. In recent years, the distribution of laboratory processes across geographic boundaries and 3rd parties (externalisation) has become common. Businesses can take advantage of the cheapest source of commodity laboratory functions and, in some cases, tap into external sources of expertise to provide research and development. Laboratory informatics systems can meet this business need by providing capabilities for collaboration and the sharing of laboratory data and information, as long as there are infrastructures and levels of access control to ensure protection of IP. In addition to externalisation, two other
major business issues now challenge the laboratory informatics market. Firstly, there is a growing demand for more and better innovation, and secondly, the systems need to be adaptable and agile so they can cope with relentless market, business, and process changes. Historically, a considerable amount of
scientific innovation came about through serendipity and the investigation of unexpected outcomes of planned experiments – where the primary objective was to advance scientific knowledge and understanding. Nowadays, innovation is a systematic, industrial, and time- pressured process, dependent to a large extent on making sense of existing data, prior knowledge, and evidence-based decision-making. At the same time as they cope with the demand for and changing nature of innovation, organisations have to accommodate the changes driven by
externalisation, mergers and acquisitions, as well as other market forces. Frequently, such business changes entail the need to consolidate systems, processes, and workforces. Tese are the challenges that the laboratory
informatics industry now faces. Increased throughput, cost and error reduction isn’t enough anymore. Long-term, managing laboratory data and information in an integrated way needs to provide not only the ability to acquire and store data, but also to support and advance science by extracting information and knowledge from the repositories of data. Te needs now are not just to store but to make sense of the data, to uncover correlations, and to support evidence-based decision making. Moreover, informatics systems need to be able to adapt to changing business and operational needs.
How to quantify the benefit of supporting science?
Here lies a paradox: the concept of ROI (i.e. productivity) is used to quantify the benefits and thus justify the purchase and deployment of informatics systems, but the long-term benefits may arise through non-quantifiable factors such as better understanding, better decisions, and better science. In other words, the emphasis needs to change from the elimination of waste (time, effort, errors) to providing greater capability, greater flexibility, and more predictive approaches to supporting science.
www.scientific-computing.com/BASL2015
Zurijeta/
Shutterstock.com
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40 |
Page 41 |
Page 42 |
Page 43 |
Page 44