“Smart” pharmaceutical production: how it is being implemented right now?


			“Smart” pharmaceutical production: how it is being implemented right now?			“Smart” pharmaceutical production: how it is being implemented right now?

To rely only on the practical experience of specialists will not be enough, especially when a managerial decision has to be made in several hours time span and you don’t have several days to make it. After all, a large order which seems profitable in a short-term perspective (a month or two), may not yield the desired profit due to back charges, overhauls and downtime of equipment, poor calculation of the actual need for the necessary raw materials or logistics resources. To get precise calculations of how exactly the potential economic effect of each order will be achieved, it’s better to use the optimization (target) planning systems.

In the shortest possible time these IT systems form various forecast scenarios “what will happen if ...” based on a variety of input data, from physical limitations of production lines to the competitive and macroeconomic environment of the enterprise. At the end of this process the system will show which orders should be performed first and how to best distribute them on production sites and lines, taking into account the delivery time of materials, the need to use unique equipment, as well as the time for washing and disinfection of equipment between the batches release. Moreover, the production plans can be quickly revised which allows business leaders not to be attached to one scenario, but to consider the possibility of changing it, for example, in the case of an unexpected competitive tender win.


“Smart” pharmaceutical production among other things means using the approach related to integrated planning of the enterprise’s activities, which allows to synchronize the work of all its divisions: sales, production, equipment repair and maintenance, procurement. IT systems create a unique ecosystem within the organization through which all documents and communications pass.

Usually, each department has its own tasks to perform which may not be correlated with the goals of other departments. For example, production units are focused on a uniform, harmonious planned release of agreed batches of the product and do not like to change the production plan due to new contracts. On the other hand, getting the maximum number of orders is the task of the sales department. Also, in the pharmaceutical industry, there is a number of special standards in the field of certification and production execution (GMP): the amount of time for equipment disinfection has been increased, the availability of staff of certain qualifications “in the workplace” at the time of product release is regulated more rigidly than in other industries. If the sales department does not take these factors into account when formulating extensive production plans in terms of the output volume, then there is a risk that the detailed plan will not completely cover the sales needs.

Worth noting separately is the process of rescheduling in the event of a hazardous situation. For example, if the production line has stopped, how do you determine if it is worth resuming the release of the consignment immediately after it has been repaired? How long will it take to repair? And what losses will the organization incur? Any deviation from the planned targets will be optimized by the system in such a way as to achieve the final goal of the company with the least losses.


Finally, the last task that “smart production” solves is the creation of an integrated innovation infrastructure at the pharmaceutical enterprise.

It should be understood that any successful business today is built on data – competition in business is moving from characteristics of goods to the field of automatic information management. In just 5-10 years, a large number of management decisions will be made on the basis of the data analyzed by the algorithm and the recommendations issued by it. And in order to not to lose their competitive position and win new markets and regions, pharmaceutical companies master such technologies and various IT tools – predictive analytics, optimization planning and scenario modeling, as well as cognitive (“smart”) big data processing technologies that detect hidden patterns in terabytes of data almost instantly.

Pharmaceutical business has two ways of saving on routine activities. First, pharmaceutical companies introduce elements of artificial intelligence: for example, AI is already being used to monitor clinical trials, formulate optimal marketing and price strategies. Such industry’s giants as Johnson & Johnson or Sanofi use IBM Watson in their research work.

Secondly, pharmaceutical companies “hire” the bots. Robotizing business processes using RPA technology makes it possible to delegate boring, repetitive tasks that do not require making complex decisions to robots in order to free personnel from doing them. As a result, the company’s costs of processing the documents in standardized forms, gathering the data from multiple sources, searching and aggregation of information on various research and development tasks as well as registration of clinical trial results are substantially optimized.


Optimization and predictive modeling technologies, tools based on machine learning, artificial intelligence and robotics already help big pharma to make operational activities completely manageable and transparent at all stages, from research to marketing and product promotion. Nevertheless, the pharmaceutical business still has to solve a number of problems that arise when implementing the “smart” production.

First, it is necessary to provide a completely secure IT infrastructure for working with large data sets. Fortunately, in this direction, many developments are under way, from advanced antivirus programs to unique quantum encryption technologies that allow data to be transmitted over short distances through secure channels.

Secondly, the leadership of the pharmaceutical enterprises will have to accept the fact that a number of innovations are aimed at returning investments in the long term. Historically, pharmaceutical companies were not ready to innovate if this did not entail an immediate increase in profits. Pharma enterprises must learn to see value in innovative solutions that will allow them to optimally control business processes in enterprises, but will not increase substantially the revenue in the first months after implementation.

Once these problems are solved, the pharmaceutical manufacturers will be able to create a single information space at enterprises, where high-tech equipment, analytical and managerial IT systems exchange data in a non-stop mode. This will be the last step towards the intellectualization of business.

Статья опубликована в журнале CIS GMP news

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