EVENT

Justifying a Paperless Solution – Building a Compelling Business Case

25 April 2017
Webinar

Lonza is hosting a free 60-minute webinar on how to create a compelling business case for the implementation of a paperless QC testing solution. The selection of a paperless solution is only step of a longer capital expenditure (CAPEX) process, which can be difficult to navigate without a strong business case.   

Jeremy Tanner, account and business development representative – Lonza Bioscience Informatics, will discuss the following topics:

  • How Value Stream Mapping (VSM) provides a useful template for collecting and visualising the data you will need
  • Utilising the data collected to create a Return on Investment (ROI) summary
  • Building a presentation/document for upper management to approve the project
Company: 
Feature

Building a Smart Laboratory 2018 highlights the importance of adopting smart laboratory technology, as well as pointing out the challenges and pitfalls of the process

Feature

Informatics experts share their experiences on the implementing new technologies and manging change in the modern laboratory

Feature

This chapter will consider the different classes of instruments and computerised instrument systems to be found in laboratories and the role they play in computerised experiments and sample processing – and the steady progress towards all-electronic laboratories.

Feature

This chapter considers how the smart laboratory contributes to the requirements of a knowledge eco-system, and the practical consequences of joined-up science. Knowledge management describes the processes that bring people and information together to address the acquisition, processing, storage, use, and re-use of knowledge to develop understanding and to create value

Feature

This chapter takes the theme of knowledge management beyond document handling into the analysis and mining of data. Technology by itself is not enough – laboratory staff need to understand the output from the data analysis tools – and so data analytics must be considered holistically, starting with the design of the experiment