The guide explores six common lab workflows in the chemical industry where weighing plays an integral role. more
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A few years ago the term “Industry 4.0” was used to describe a future vision of digitized production. This concept is now being implemented by industry. It is characterized by a high degree of automation and networked processes, without which global competitiveness would no longer be achievable. Now digitization is set to open up innovative opportunities for the laboratory environment as well, be it in R&D or QA/QC.
In highly regulated industries such as chemicals and pharmaceuticals, laboratories have a significant impact on productivity. Applying Industry 4.0 concepts  to the lab offers new opportunities to make processes more efficient whilst facilitating compliance. Similar to Industry 4.0, “Laboratory 4.0” has become an established term, but what does it stand for?
Industry 4.0 has become a term used worldwide for the digitization of industry. Emerging as recently as 2011 as a project of the same name within the framework of the German federal government’s high-tech strategy , it postulates for the first time a fourth industrial revolution. After the first, the second and the still ongoing third industrial revolution (mechanization, labor-based mass production and automation), the Internet of Things (IoT) has moved into focus as an innovation driver, with cyber-physical systems (CPS) as the technological basis for intelligent, networked manufacturing. For industry, the term IIoT (Industrial Internet of Things) was coined. Within a short period of time, industry has begun undergoing a fundamental change in production workflows and processes.
Fig. 1 The Reference Architecture Model Industry 4.0 (RAMI 4.0) is a three-dimensional construct to illustrate the most important aspects of Industry 4.0 and to provide guidance. (Source: Plattform Industrie 4.0) 
The Reference Architecture Model for Industry 4.0 (RAMI 4.0), which was developed jointly by the German Electrical and Electronic Manufacturers' Association (ZVEI) and partners, proposes a framework for the implementation of Industry 4.0 (Fig. 1) . Volume production can become much more flexible, highly individualized products can be manufactured and processes can take account of specific customer requirements. In production management, “lot size one” (product quantity of a production order) is the current buzzword. Based on the data that the connected machines and devices collect, data analysis (big data analytics) or artificial intelligence (AI) are used for continuous optimization. In addition, predictive maintenance opens up further scope for saving costs.
To assess the relevance of these developments for laboratories, it is important to keep in mind that they play an essential role for almost every manufacturer and are an important profitability factor. By generating, evaluating and documenting data as a result of their research and analyses, they create significant added value. Modern digital technologies have led to vast quantities of complex data, whose analysis and evaluation, in turn, requires state-of-the-art methods.
Especially in the regulated lab environment, processes are constantly being optimized and their efficiency increased while the main focus is kept on meeting the ever growing number of regulatory requirements. However, it's not only existing regulations that must be strictly adhered to, new regulations must too. Regulation has vastly expanded in recent years and been introduced at ever shorter intervals by the international authorities. Specialists are required to keep up-to-date with the latest developments so they can create systems and processes that comply with the current regulations and, where necessary, invest in new systems and equipment. A recent addition is the EU’s regulation on data protection, which came into force on 25 May 2018. The General Data Protection Regulation (GDPR) mandates significant changes in the handling of personal data. It applies to employees and, for example, to patients’ data in analytical labs or to clients’ customer data.
The following digitization issues are particularly relevant for ensuring compliant processes in the lab:
Manually transferring data, for example into a lab notebook, can be very time consuming because all the necessary data needs to be recorded and kept. These are so-called metadata, which convert a simple measurement (e.g. 10.235 mg) into a result with full traceability. Typical metadata to be documented are user ID, timestamp, instrument and state of the instrument (date of last calibration). In a highly regulated environment, it is common to have such records countersigned by a second person, in keeping with the four-eyes principle. Transferring data manually carries a potential for errors. A digital solution, whereby all required metadata are dealt with automatically, saves not only time but also prevents transcription errors. However, a digital solution does not necessarily have to be a fully integrated one1. Solutions using RFID tags2, which are placed on the product and contain all relevant data, are frequently used in industrial environments and can also be used in a lab.
Digitization in a regulated lab environment is directly connected to the compliance issue. At the core of this is data integrity, an essential part of quality assurance that has moved into the focus of the responsible national authorities. A number of draft directives have recently been published, including the US Food and Drug Administration’s “New FDA Draft Guidance Data Integrity and Compliance with CGMP”  and the PIC/S (Pharmaceutical Inspection Convention/Pharmaceutical Inspection Co-operation Scheme) “Good Practices for Data Management and Integrity in regulated GMP/GDP Environments” . Likewise, the EMA (European Medicines Agency) took up the issue and added the category “Data Integrity” to its set of questions and answers related to GMP and GDP . For implementing CGMP-compliant data integrity (for example, according to FDA 21 CFR Part 11 - Electronic Records, Electronic Signatures), the regulatory authorities do not provide detailed guidance. The somewhat vague statement “full traceability of process steps and results to all relevant data” might give us the best idea of the compliance level demanded.
The so-called ALCOA principles define the key criteria which both paper-based and electronic data must fulfill to ensure data integrity in accordance with regulations. The acronym ALCOA stands for attributable, legible, contemporaneous, original and accurate. ALCOA-plus (ALCOA+) extends the criteria to include complete, consistent, enduring and available (see Fig. 2). The ALCOA principles, which were developed by the FDA, provide a framework for achieving GMP/GAMP-compliant data integrity and have been included in most guidelines.
Fig. 2 ALCOA+ based criteria for GMP/GAMP-compliant data integrity and explanations (according to FDA Draft Guidance on Data Integrity )
Implementing these principles is challenging because there are no clear instructions to follow. Detailed specifications of the data needed for a complete dataset cannot be found in the literature. The assumption that manually acquired data would be sufficient for a future digitized process has to be put into question because the data sets would need to be more detailed in order to achieve full traceability. Access rights to data, long-term storage (archiving, not backups) and audit trails also have to be dealt with upon implementation. The conventional IT approach is to create a data architecture model [7, 8 chapter 4.4]. This would have to include regulatory aspects to enable it to go into further detailing and implementation. A data model can only be created if the IT specialists, the affected departments overseeing the labs and the compliance officers cooperate. To be able to use the data at later points in time for other purposes (data lakes, big data), the department responsible for data analysis  would need to be involved.
Digitization increases lab efficiency by automating many processes that were previously performed manually. In a regulatory compliant lab, it supports meeting data integrity requirements and eliminates transcription errors. At the same time, digitization can have a positive effect on the job satisfaction of the lab staff by relieving them of cumbersome tasks. Key factors for the lab digitization process are a present-state analysis, the data structure (taking the regulations into account) as well as identifying future strategic opportunities arising from, for example, data lakes. To tackle such a complex project, a step-by-step implementation approach may make sense, with interim solutions, such as the use of RFID technologies, possibly achieving short-term qualitative improvements.
1 All devices are connected to the network and integrated at back end level (e.g. LIMS).
2 RFID tags are small transponders (an amalgamation of “transmit” and “response”) which contain the RFID information as stored data and transmit it wirelessly to RFID readers via radiocommunication. They basically consist of a microchip, a capacitor and an antenna which, together, enable a continuous flow of information.
 BMWi, Plattform Industrie 4.0, https://www.plattform-i40.de/I40/Navigation/DE/Industrie40/WasIndustrie40/was-ist-industrie-40.html, accessed on 2018 August 27
 BMBF, https://www.bmbf.de/de/zukunftsprojekt-industrie-4-0-848.html, accessed on 2018 August 27
 BMWi, Plattform Industrie 4.0, https://www.plattform-i40.de/I40/Redaktion/EN/Downloads/Publikation/rami40-an-introduction.pdf?__blob=publicationFile&v=7, accessed on 2018 August 27
 U.S. Department of Health and Human Services, Food and Drug Administration, https://www.fda.gov/downloads/drugs/guidances/ucm495891.pdf, 2016 April (accessed on 2018 August 27)
 PIC/S, PI 041-1 (Draft 2): Good Practices for Data Management and Integrity in regulated GMP/GDP Environments, 2016 August 10 (accessed on 2018 August 27)
 EMA, http://www.ema.europa.eu/ema/index.jsp?curl=pages/regulation/q_and_a/q_and_a_detail_000027.jsp&mid=WC0b01ac05800296ca, accessed on 2018 August 27
 Wikipedia, Data architecture, https://en.wikipedia.org/wiki/Data_architecture, accessed on 2018 August 27
 Chircu, A. M., Sultanow, E. & Sözer, L. D., (2017) A Reference Architecture for Digitalization in the Pharmaceutical Industry. In: Eibl, M. & Gaedke, M. (Hrsg.), INFORMATIK 2017, Gesellschaft für Informatik, Bonn, S. 2043-2057, DOI: 10.18420/in2017_205
 Smith, J. (2016) How Big Data Is Transforming Pharmaceutical Manufacturing, https://www.pharmaceuticalonline.com/doc/how-big-data-is-transforming-pharmaceutical-manufacturing-0001, 2016 August 12 (accessed on 2018 August 27)
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