New Article: Data Analytics Excellence in MES
Posted on September 18, 2019 by Helena Graham
Pharmaceutical manufacturing is becoming ever more complex and data analytics more important. The requirement for small batches for niche, personalised medicines is increasing, creating the need for more flexible facilities, while drug products are also becoming more complex, often containing highly potent active pharmaceutical ingredients (APIs).
In a bid to navigate through the multiple manufacturing challenges, there is a greater need for data analytics than ever before to help deliver smooth operations and reduce the waste of any resource. With data analytics coming to the forefront of many pharmaceutical companies’ strategies, Manufacturing Execution Systems (MES) are expected to be key for data scientists to gather the necessary insights. Ryan McInerney, MES project manager and technical consultant at Zenith Technologies is this week presenting at Pharma MES 2019 in Berlin, exploring the benefits of data analytics excellence and how MES can help companies to improve their manufacturing related data analytics programs.
What is data analytics excellence?
MES contextualises data for data scientists so that they can reduce cycle times and turn previously limited or inaccurate analytics into meaningful, valuable data. When data scientists spend less time interpreting the data, it gives them more time to develop and validate complex algorithms that are required to fully utilise more valuable data analytics methods such as predictive and prescriptive analytics.
Having a data-oriented mindset when using automation and MES platforms will pay dividends when extracting data in process optimisation decisions. Industry experts estimate that data scientists spend around 60-80% of their time scanning and preparing data before any insight is gained.
Optimising the design of data sources will help to speed up the analysis of data, whilst also reducing collection and preparation time and subsequently resulting in more meaningful and timelier insights.
Bringing different perspectives together
While data scientists and MES designers don’t typically interact until the data has been presented, a simple introduction and familiarisation with each other’s systems can go a long way in helping both sides decide upon a solution that is sustainable in the long term. An optimal solution would be encouraging data scientists to review the data as the systems undergo development, providing feedback on the interface and data that is missing – as well as any challenges they’ve faced during the organisation of data.
Manufacturing-related data analytics
Companies can reduce project delivery time by making an effort in advance of any digital initiatives to standardise documents, terminology, platforms and technology transfers, as well as simplify the maintenance of those systems post go-live. For example, evaluating and leaning the company’s paper master batch records prior to an MES project helps to ensure that only the required information is recorded.
Digital and analytics maturity
While different regions impact the interest and execution of digital projects, the bigger differences tend to be linked to the digital maturity of a company. Companies that are committed to digital initiatives, such as implementing a full MES and an automation strategy, will have more potential to move into advanced data analytics compared to an organisation that hasn’t yet fully adopted electronic ways of working; the more open a business is to projects that will make their data readily available, the better position they are in data-wise.
Although there are several challenges impacting the manufacture of drug products, employing MES platforms can improve manufacturing related data analytics programs. It is vital that data analytics are used to gather the insight needed to successfully overcome multiple manufacturing challenges.
Data integrity initiatives will improve current systems by ensuring that data is captured real time and is more accurate as increasingly information will be captured through automation interfaces, enabling existing data to be better used in data analytics.
You can find out more about Pharma MES 2019 here and if you attending the event please ensure you catch the presentation and come and meet us at Booth No. 12.