A three-step plan for digital transformation

Digitalisation and automation have rapidly accelerated the efficiency and quality of laboratories in recent years, driving them forward on their digital transformation journey. Advanced software and hardware technologies are being embraced across many sectors, and laboratories are continually adopting new instruments and methods for data acquisition, and implementing digital systems to streamline workflows.

自动化科学的优势有很多，包括提高吞吐量水平、简化工作流程、最小化人为错误、提高数据质量、完整性和可重复性。自动化可以发生在任何层面，从简单的任务，如校准硬件系统，到复杂的多部分实验室生态系统的编制。先进的数字解决方案，如数据管理和分析软件，以及自动化硬件，如机器人，只是现代实验室用于加速科学进步的工具的一些例子，作为其持续发展的一部分。
The importance of having a digital transformation strategy has been confirmed by a recent Accenture survey, which discovered that while 93% of chief scientific officers know transformation is imminent, only 20% feel prepared for this innovation [1]. So, even with a high awareness of the expected industry disruption, why do such a large proportion of senior scientists experience barriers to entry for digital transformation?
One of the biggest perceived issues is the possibility of disruption to workflows during implementation. While it is true that changing workplace practices can be challenging, there are proven step-by-step processes that support laboratories with this digital transition. These workplans hinge on solutions like laboratory information management systems (LIMS). LIMS play a pivotal role in digital transformation by integrating instruments for connected end-to-end workflows, automatically storing and tracking data, and helping to drive productivity through features such as maintenance planning. In essence, LIMS work as a digital science workbench around which automated hardware and intelligence systems can function.
在本文中，我们讨论了数字化解决方案在驾驶自动化中的重要性，以及实验室可以采取的步骤来推进其数字化转型之旅。This includes the importance of connectivity, and how this can facilitate automated, end-to-end workflows and advanced analytics, allowing scientists to analyse data in ways that wouldn’t otherwise be possible.

Digital systems: the spearhead of automation

Automated science is often regarded as being built on three foundational pillars:
1. Physical automation. This involves all equipment and hardware with capacities for automation, including tools such as automated high-throughput screening devices and robotic sample handlers.
2.数据基础设施。数字解决方案创造了一个基础设施，将仪器与基于云的技术和集中的数据库连接起来，使它们能够从多个计算系统、智能手机和平板电脑访问。LIMS和电子实验室笔记本(ELNs)是专用的软件连接工具，能够生成标准化、可共享的数据，并最终为自动化基础设施提供数字骨干网。此外，数字系统还可以通过自动订购试剂和耗材等功能进一步提高自动化程度。
3. Artificial intelligence (AI). Advanced software uses data science approaches like deep learning to analyse large volumes of data and generate insights that inform predictive science and intelligent workflows. Once fed enough data, AI can drive discoveries and push science to new levels.
While digital infrastructure is itself one of the pillars, digital solutions also play a pivotal role in connecting all three pillars together, helping to automate science by integrating instruments for connected end-to-end workflows. They act as the essential links between physically automated devices, the user and advanced analytical approaches, automatically storing and tracking data, and helping to drive productivity.
In particular, systems, such as Thermo Scientific SampleManager LIMS, help organisations to increase automation, and ultimately achieve digital transformation, by supporting laboratories to better connect instruments and data, and organise samples and associated information. LIMS software can manage standard operating processes for analytical instruments and collect large amounts of high-quality experimental and operational data automatically, securely storing it so it can be analysed manually or by deep learning algorithms. By improving connectivity and making data more easily accessible and shareable, LIMS software provides increased visibility and control over processes and improves capacity for automation, collaboration and intelligent data analytics.
Built-in data settings also ensure both experimental and operational data is compiled and stored in an organised manner, making it traceable, auditable and compliant with regulations. Furthermore, the gathering of operational data means LIMS can monitor laboratory systems in real-time and automate more processes, such as reagent stock monitoring and ordering, instrument calibration, and flagging when instruments need maintenance.
总体而言，数字解决方案使科学家们能在琐碎的任务上花更少的时间，更多的时间利用他们的专业技能。当这些系统实现时，就为构建智能工作流创建了一个平台。

The three stages of digital transformation

But how do laboratories reach this stage of game-changing digital transformation? Every laboratory’s transition process will be different, and projects aimed to integrate software and hardware systems often encounter challenges. For example, projects can see delays, high costs, and often result in disconnected user experiences and inconsistent audit trails. To avoid this, laboratory changes can be approached systematically based on their goals, resources and budget. While specifics may vary between organisations and laboratories, there are three key steps to implement a successful transformation:
1. Connect everything
这是数字化转型的关键第一步。它从连接实验室中的一切开始，包括仪器、消耗品、人员、工作流程和数据。利用信息学工具，如LIMS和ELN，实验室可以建立一个数字科学工作站，数据是可找到的，可访问的，互操作和可重复使用(FAIR)[2]。除了在所有过程中保持数据完整性外，LIMS还可用于连接分析仪器和其他系统，如全球制造业务。通过整合一个组织的所有方面，从研究到制造和其他，组织可以得到一个更全面的观点，他们的运作和实现增加的过程控制。
2.的端到端自动化工作流
The next step is implementing automated instruments and workflows, and connecting these systems into the digital laboratory. Laboratory automation is designed to maximise throughput, standardisation and reproducibility. By reducing manual tasks, automated systems decrease hands-on time and minimise potential human errors. When implemented well, interconnected automated instruments also enable laboratories to flexibly scale-up. Laboratories can start with standard journeys, like automating simple instruments and interconnecting benchtop instruments in a workstation. This can then be scaled-up to a larger transformative process that integrates and automates multiple instrument platforms and experiments to form an end-to-end connected workflow.
3. Utilise advanced analytics
Finally, integrating automated physical and digital platforms with tools and services like e-commerce and data analytics builds one intelligent ecosystem that provides predictive support for scientists. For advanced data analytics, this relies on artificial intelligence (AI) to analyse all the data created by the various automated solutions. For example, machine learning algorithms can create meaningful insight from data, while deep learning algorithms go further to generate more abstract information from that data. The more data that is inputted, the more trends are identified, meaning AI can be used to streamline operational processes and drive discovery. However, you need to be putting the right data in to get accurate results out, which is why effective data management and data integrity are so important.

A pathway to digital transformation

Many laboratories see digital transformation as a vital way to drive innovation and enable faster and more secure production routes, but also feel unprepared for this transformation, and find it challenging to overcome the barriers for successful implementation.
虽然每个实验室的数字化转换过程是不同的，但使用这三个阶段的共同元素可以根据它们的目标、范围和预算对任何实验室进行转换。通过连接实验室管道提高生产力可以加快科学产出。一个强大的信息学基础设施，包括LIMS和ELN等工具，可以成为单个或更大的仪器网络自动化的基础。In the end, after the steps to connect workflows and increase automation are implemented, laboratories can begin to consider implementing intelligent analytical systems, enabling scientists to utilise their data to its full potential and reveal insights that wouldn’t otherwise be uncovered.

References

1. Disruption need not be an enigma, Accenture, February, 2018 [https://www.accenture.com/us-en/insight-leading-new-disruptability-index]
2.FAIR Principles, GO FAIR, [https://www.go-fair.org/fair-principles/]