Part 1 of this blogpost series introduced digitalization in pharma and medical device R&D, Part 2 discussed on the need for digitalization.

Part 3 mentioned on- How to go for digitalization in R&D, Part 4 discussed advantages of digitalization in pharma and medical device R&Ds, while Part 5 elaborated on digitalization in various aspects of drug research and development.
​
In this blogpost series I shall discuss on- Selecting the right R&D digitalization platform.

This totally depends on the company’s critical needs and its position on the transformation cycle. Another important factor is data pipeline. For example-

• Does your company conduct drug discovery research?
• Does it develop innovative products or generics?
• Which therapeutic areas are considered for product development?
• Does your product pipeline entail drugs for humans or veterinary?
• Does it use advance data science currently or is all work done only in analog mode?
• How much of information must be shared with other technical/ non-technical departments within the organization?

R&D digitalization sure involves a huge cost. But it is the need of business to stay relevant and stay competitive. Hence companies must strategically plan its digitalization journey. Continuous improvement in R&D via digitalization or any other pathway is a top-down initiative. Leaders must make continuous improvement in pharma R&D a priority and an integral part of corporate goals.
​
There are scores of R&D digitalization platforms out there. Selecting a platform which is right for your company is key. So, what are some of the salient features to note while choosing R&D digitalization platform? 

1. Position of the company in the overall digital transformation cycle.
​
2. Compliance to regulations:
Compliance to 21 CFR Part 11, ICH and all regulations impacting your product, R&D management and line of business. Also, compliance to testing, labelling and/or other product-specific regulations for drug- drug product.

3. Product pipeline:
Developing now and what is the company aiming for? What are the types of formulations a company wants to offer in their product mix? Is it a synthetic small molecule, herbal, peptide or some other biological product, vaccine, medical device, diagnostic etc.? Is it multiple types of dosage forms- because a single digitalization platform should be able to cater to all needs? Which markets are you developing the products for? Platform must build products to comply with all regulations (concerning development, testing, labeling and other product-specific guidelines) of each market.

4. Talent building:
Talent building via employee re-skilling and/or recruitment. A bottom-up approach to changing skills and mindsets. Make Agile work in a practical way. Build capabilities and team-member engagement.

5. Facilitates remote working and/or working in Cloud.

6. Compatibility and inter operability of the platform with existing digital ecosystem within the organization:
Compatibility and inter operability of the platform with existing digital ecosystem within the company such as LIMS, ERP supply chain software, BMS (building maintenance software), equipment process automation software etc. 

Compatibility and inter operability of the platform with customer engagement software channel with healthcare professionals: The med tech industry is shifting from a model that revolved around sales rep to and omni channel world in which HCPs (Health care professionals) are able to access information as and when they need. Although reps remain the main point of contact, companies are augmenting this relationship with new resources & tools and coordinating them across channels to address stakeholder’s needs.

Compatibility with digital marketing, portals and e-commerce platforms: According to a published statistic, 80% of med tech companies shifted more than 20% of their marketing expenditures to digital channels in 2020. Further, 2/3rds of med tech companies expect online channels to account for more than 20% of their revenue by 2025. 

Omni channel engagement is not about making tweaks to existing models. It is about shifting to a new model in which Agile teams bring design thinking, digital engagement and analytics to help sales rep deliver the right message at the right time through the right channel to the right stakeholder.

In the next part of this series, I shall discuss on- Six building blocks for digital transformation in pharmaceutical and medical devices R&D.

​Meanwhile, note that there are fifteen different Continuous Improvement pathways for pharmaceutical and medical device R&Ds. Are you planning a Continuous Improvement initiative for your R&D division?

Part 7:  Six building blocks for digital transformation in pharmaceutical and medical devices R&D.
Part 8:  How to transform existing R&D setup to a digitalized R&D.
Part 9:  Tips and Quick wins of digitalization in pharmaceutical and medical device R&D.

Realizing the digital opportunity is no simple task—it represents an innovation capability for the entire organization- as good as an organizational overhaul. 

​Part 1 of this blogpost series introduced digitalization in pharma and medical device R&D, Part 2 discussed on the need for digitalization, Part 3 mentioned on- How to go for digitalization in R&D, while Part 4 discussed advantages of digitalization.

In this blogpost, I shall elaborate on- Digitalization in various phases of drug research and development.

Experiments and clinical trials carry a huge cost for drug industries, both financially and in terms of human and scientific resources. Advanced simulation, modelling, AI-based analytics, and quantum computing help identify the strongest candidate for new therapies by allowing only the most promising moieties to proceed to the costly experimental phase. 
​
Let’s see how does digitalization impact various phases of drug development…

​Traditionally new drug discovery used mouse models with a hope that findings would translate to humans, leading to high failure rates. Now and going forward new drug moieties are discovered using human genome sequencing technology. Rapid growth in genome sequencing technology allows researchers to not only design therapies based on human biology, but also at reduced costs.

Using digitalization at in silico discovery for target identification maximizes the value of data. The efficient data interoperability implies a clear data strategy, data governance and data management which shrinks drug discovery timelines significantly by 3-5 years! Also, this reduces cost of drug discovery by several million USD.

Further, besides researchers, peers and colleagues from project decision-making to portfolio management & risk optimization functions work more closely together and feel more empowered through increased data visibility and decision transparency.

​Digitalization uses Big Data for formulation prediction. Using digital mode of drug product development shrinks both development cost as well as time enormously.

Just as drug discovery, digitalization impacts how drug products are developed such as-
​
1.      (Drug) Salt- finding study.
2.      Dose-finding study.
3.      Identifying ADME (Absorption Distribution Metabolism Elimination) of the drug.
4.      Route-finding study-

Route finding study identifies the best way to administer the drug or whether the drug can be administered via multiple routes? Should the drug be administered only by per oral route or can it be administered via other mucosal routes such as nasal, inhalation, ophthalmic, parenteral, transdermal etc.

5.      Proof of concept study/ Formulation development & analytical testing-

Digitalization allows machine learning algorithms to review raw material properties such as- drug molecule chemistry, excipient chemistry, powder dynamics, rheology, ADME , patent and regulatory data etc. to build formulation recipes and predict results. Digitalization uses Big Data to suggest formulation compositions.

For instance- in case of targeted drug delivery systems, the choice of carrier is vital. And researchers conduct studies to identify if the carrier-mediated drug transport additive should be- Niosomes, liposomes, methacrylates, ion-exchange resins, cyclodextrins, fullerenes or nanotech-buckyballs, RBC (red blood cells) or something else.

Another customary study is to find out if the formulation requires a therapeutic platform for delivery.

Needless to say, huge number of experiments get done to arrive at the ‘best’ carrier for drug delivery. Obviously, this involves huge R&D cost and time which can be saved by digitalization in product development.

Moreover, data visualization helps researchers to identify the product with best profile. Both in vitro and in vivo data can be visualized. Researchers can skip pilot BE studies if a product shows superimposable data graphics with RLD (reference listed drug). Imagine the costs saved!

Additionally, quantum computing also immensely helps product development via 505 (b) (2) pathway.

Hence, I would say if a company aims to develop products via 505 (b)(2) pathway then digitalization is the way to go. For such companies, digitalization shall save millions of dollars of R&D budget and in addition provides first-mover advantage.

​Digitalization makes clinical research more efficient. Even though electronic data records are standard since long and digital regulatory submissions are being accepted for over two decades, clinical development still has significant untapped efficiency reserves. Discovery and clinical trials making up the largest share of costs to get a drug to commercialization, efficacy gains in these areas therefore have a direct measurable impact not only with time to market but also in real savings.

AI has many potential applications in clinical trials both near- and long-term. These range from automating routine study data entry functions, to analyzing electronic health record data to find suitable candidates & clinical trial sites, to monitoring and encouraging patient compliance with study protocols, to adaptive dose finding, to discovering and modelling potential new molecules and therapies. 

Big Data and real-world data (RWD) are becoming increasingly prevalent as digital devices become more adept at capturing myriad signals from patients in a variety of settings.

For example, combining historical information from EHRs (electronic data records) with imaging, genetic and molecular test data is driving the development of highly targeted oncology treatments, such as CAR-T and other cell therapies, giving hope to patients resistant to more conventional treatment approaches .
 
If Big Data is the raw material of digital transformation, then AI is the engine that sponsors rely on.

AI powered capabilities viz pattern recognition, evolutionary modelling etc. are essential to gather, normalize, analyze and harness large data sets that are fundamental to modern therapeutic development. Also, these models are especially useful for conducting smaller studies that are gaining importance in targeted smaller patient populations.

Additionally, applying advanced statistical models to the new data also improves pharma R&D productivity.

One of the main hurdles in clinical operations is the disconnect between clinical development plans and clinical trial protocols. In other words, between strategy and the operations.

Digitalization defines the ideal trial avatar and guides researchers on patient-centric clinical development,  feasibility of the study to help them better plan the duration of recruitment and trial length, collaborate with patients in the research process, ease patient access, reduce the burden of trial participation and transform patientcare during clinical trials.

Such gained efficiencies, optimize time and cost, expedite patient enrolments, improve retention & increase study diversity, improve investigator productivity, reduce manual effort on repetitive tasks and transform patientcare during a clinical trial by paper-less tracking.

A published case study (Reference- https://www2.deloitte.com/us/en/blog/health-care-blog/2018/how-a-digital-rd-strategy-can-improve-clinical-trial-innovation-and-execution.html) indicated how digital transformation of a standard neurological test reduced clinician burden- in neurological assessment, a commonly used symbol-digit modalities test asks a participant to match basic numerals to geometric shapes according to a reference key. The lower the number of correct connections within the allotted time, the higher the level of cognitive impairment. A biopharma company made this test available to patients on an iPad at the point of care and fed test results directly into the patient’s EHRs. This eliminated the need for clinicians to spend time on conducting the test and entering data and also enabled more standardized test administration.
 
From understanding disease onset and progression to identifying early safety signals or food and drug interactions to engaging with patients for clinical trial participation and awareness to virtual augmented reality to conduct virtual clinical trials, digitalization enables researchers to better predict the properties of a material, the performance of an active molecule and/ or its toxicological effect.
 
In addition, digitalization improves efficiency of post-marketing pharmacovigilance and patient safety.
 
In fact, from now on in the coming decade, adopting digital technology at scale viz patient reported outcomes captured using mobile devices, digital assistants and voice recognition to virtual augmented reality (AR) to conduct virtual clinical trials and digital biomarkers as primary endpoints might just be the norm!

Digitalization will be a necessity in pharma R&Ds in years to come regardless of whether it is an innovator company or a generic player. The extent and magnitude of digitalization needed of course shall be different for the two.

Pharma, medical device R&D digitalization surely involves a huge cost. But it is the need of business to stay relevant and stay competitive. Hence companies must strategically plan its digitalization journey. One of the critical aspects of R&D digitalization is selecting the right digitalization platform.

Coming up in next part of this series- Selecting the right R&D digitalization platform!

​Meanwhile, note that there are fifteen different Continuous Improvement pathways for pharmaceutical and medical device R&Ds. Are you planning a Continuous Improvement initiative for your R&D division?

​Part 6:  Selecting the right R&D digitalization platform.
Part 7:  Six building blocks for digital transformation in pharmaceutical and medical devices R&Ds.
Part 8:  How to transform existing R&D setup to a digitalized R&D.
Part 9:  Tips and Quick wins of digitalization in pharmaceutical and medical device R&D.

​Biopharmaceutical R&D involves a series of high-risk and high-investment decisions. In addition, the industry faces considerable productivity challenge in terms of identifying, testing, and bringing new drugs to market, especially in the context of the highly innovative therapies we seek today.

In previous parts of this series, I discussed Introduction to digitalization in pharma and medical device R&D (Part 1), the need for digitalization (Part 2) and how to go for digitalization in pharma and medical device R&D (Part 3).
​
In this blogpost, I shall highlight some of the salient advantages of digitalization in pharma and medical device R&Ds.

​Digital transformation via digitization and digitalization in R&D allows researchers to automate time-consuming manual processes and open new exploration skylines in prickly issues that have failed to evoke breakthroughs.

Artificial intelligence (AI), advanced analytics and automation are changing ways of working in scientific research. The umbrella category of AI and advanced analytics of digital technology has the maximum potential to improve R&D productivity.

Close behind are identification of biomarkers, use of EHRs (Electronic Health Records) and Clinical Registries.

A significant advantage of digitalization in drug development is creation of targeting biomarkers as the therapeutic approach, followed closely by gene therapies and customized therapies targeting specific disease stages & comorbidities.

Digitalization opens pharma R&D horizons in areas such as genomics which further leads to breakthroughs in precision medicine. It also creates opportunities for decentralized clinical trials, unleashing future innovation in digi-ceuticals and healthcare wearables.

In the allied chemicals sector too, R&D digitalization facilitates designing and modeling new materials and composites. Digitalized chemical innovation is the foundation for everyday products from smartphones and fiberoptic network cables to autonomous vehicles. Digitalization enables researchers to better predict the properties of a material, the performance of an active molecule or its toxicological effect.

As quantum computing- an element of digitalization, allows deeper molecular modeling, medical device research and development specialists expect quantum computing to open new material prospects in specialties such as batteries, semiconductors, superconductors, composites and opto-electronic gadgets.

Further, quantum computing-based molecular modelling helps researchers to identify the most effective molecular designs or structures before having to synthesize molecules in laboratory settings. AI helps glean the best compound in the early stage, so researchers can skip over the very long discovery processes. For example- using AI to select or optimize the antibodies and evaluate efficacy or toxicity in the preclinical stage.

Digitalization explores the space of interest for discovery, guides researchers to do more focused experiments and generate data to refine the experimentation process further.

As therapies are designed as targeted dosage forms, the efforts to identify those patients who may benefit most from a drug takes considerable amount of time and effort. Digitalization helps these efforts by leveraging digital technology to help find the right people set for the right trials.

For example- AI improves patient-trial matching by crunching large datasets like electronic health records, genomic data, medical literature, trial databases, inadequate enrollment of patients in trials—too small a sample, too high a drop-off rate, or suboptimal cohort selection. Failing to exclude individuals using another medicine that could interfere with the tested one—is a major cause of trial failure.

Once underway, advanced analytics can enable adaptive clinical trials, where researchers modify a live trial, such as by refining the sample size, changing the allocation of patients to ‘control’ versus ‘trial’ arms, and stopping a trial early based on likely success or failure.

A significant value driver within clinical R&D is reinventing how companies engage physicians, patients, and investigators at a granular level. Through digitalization, the medical affairs teams can understand the requirements of individual physicians (as well as other stakeholders) and to deliver precise information on-demand.

Lowering the costs of trials and improving experimental data are worthy ends in themselves, but companies can also use digital technology to identify new lines of therapy.

For instance, in health care, the quality of data available to researchers helps in the development of therapeutic areas like immunotherapy, which alters the human immune system to better fight diseases like cancer. This is a far cry from current therapies like radiotherapy and chemotherapy which bring debilitating side effects and are often palliative rather than curative. Digital technologies like wearables, implants, and digestible can show how a therapy is working in richer detail via digital biomarkers.

An overarching benefit of digitalization in pharma R&D is improving the quality of new products before they enter the experiment and clinical trial process. Digital innovation can improve the quality of experiments from optimizing design and protocols to generating better data about the drug candidate’s effectiveness.

Big data and AI enabled modelling processes allow researchers to move from static processes to more fluid and adaptable working procedures for drug discovery and R&D pipeline portfolio management.

Moreover, analytics, simulation, prediction, and modeling show researchers more about how an innovation might fare in the real world. This eliminates drug candidates that might once have incurred costs in a failed pilot or pivotal trial due to shortcomings that researchers were unable to spot in an earlier phase.

Further, in a digitalized formulation R&D, with super imposable information from recipe builder on bio-chemical properties of active(s) and additives, enforced patent and regulatory data, with a click of a mouse key, formulators can checkout series of formulation suggestions. These formulation suggestions can be further evaluated by digital experimentation.
​
On an average, post digitalization in drug development, the time from drug discovery thru product commercialization can be decreased by as much as 6 years! 

​A Japanese pharma company (Reference- https://www.bbc.com/news/technology-51315462) put a new drug molecule invented entirely by artificial intelligence into human clinical trials. This new compound is designed to help patients with obsessive-compulsive disorder (OCD). It reached human clinical trials in just 12 months – now, compare that with the four to six years that it traditionally takes for drug candidates to reach that stage! Cool, isn’t it?
​
In the next part of this series, I shall elaborate on- Digitalization in various phases of drug research and development.

​Meanwhile, note that there are fifteen different Continuous Improvement pathways for pharmaceutical and medical device R&Ds. Are you planning a Continuous Improvement initiative for your R&D division?

​Part 5:  Digitalization in various phases of drug research and development.
Part 6:  Selecting the right R&D digitalization platform.
Part 7:  Six building blocks for digital transformation in pharmaceutical and medical device R&Ds.
Part 8:  How to transform existing R&D setup to a digitalized R&D.
Part 9:  Tips and Quick wins of digitalization in pharmaceutical and medical device R&D.

​Keywords and Tags:

#businessprocessimprovement #continuousimprovement #digitalization #digitization #digitaltransformation #digitaltransformationinR&D  #digitalizationinR&D #continuousimprovementinR&D #advantagesofdigitalization

The journey of digital transformation begins with building an enterprise-wide digital strategy that is tied to the organization’s R&D strategic priorities, followed by its flawless execution.

After a company makes it a strategic objective, then laying out the roadmap for the ‘how-to go for digital transformation’ is the next step. And that involves not just considering the organization’s goals, but also the current data and technology landscape, workflows, processes, product mix along with the appetite for change and investment.

The successful initiative would be to focus on a degree of R&D-wide buy-in first. But it doesn’t stop there, you need to make it a team sport to make change happen. By encouraging cross-functional collaboration, data strategies and data sharing platforms, not only do you encourage company-wide thinking about digital transformation, but also reinforce the support for the idea that- digitalization is a priority.

Next step is to create a team and a team champion who will drive the digital transformation wagon effectively. The champion could be an in-house expert or an external consultant. I shall elaborate on this point in Part 8 of this series, where I shall discuss on- How to transform existing R&D set up to a digitalized R&D.

Another important criterion to define at the outset are- metrics of measuring success with digital transformation. Identify how you will measure success and use those metrics to build a success story of sustainable digital investment. Further, build a communication plan and communicate those objectives to all stake holders. Be meticulous in tracking both leading indicators and the actual impact delivered.

Remember! Via a journey with a right digital vision, strategy, roadmap, persistent leadership support, effective change management and a culture of continuous improvement, an organization can fast realize as well as sustain the benefits of digitalization.

In the next part of this blogpost series, I shall highlight- Advantages of digitalization in pharmaceutical and medical device R&Ds.

​Meanwhile, note that there are fifteen different Continuous Improvement pathways for pharmaceutical and medical device R&Ds. Are you planning a Continuous Improvement initiative for your R&D division?

Part 4:  Advantages of digitalization in pharma and medical device R&Ds.
Part 5:  Digitalization in various aspects of drug research and development.
Part 6:  Selecting the right R&D digitalization platform.
Part 7:  Six building blocks for digital transformation in pharmaceutical and medical devices R&D.
Part 8:  How to transform existing R&D set up to a digitalized R&D.
Part 9:  Tips and Quick wins of digitalization in pharmaceutical and medical device R&D.

​Keywords and Tags:

#businessprocessimprovement #continuousimprovement #digitalization #digitization #digitaltransformation #digitaltransformationinR&D  #digitalizationinR&D #continuousimprovementinR&D

Part 1 of this blogpost series introduced Digitalization as one of the important continuous improvement pathways.

Digitization is conversion of data and processes from analog to digital whereas, digitalization embraces the ability of digital technology to collect data, establish trends, make better business decisions, in toto transform ‘how work happens’.

In this blogpost series, I shall discuss on- Why the need for digitalization in pharma R&D labs?

Need for digitalization in pharma & medical device R&D:

Historically R&Ds regardless of industry sector, were run in an unstructured way. The degree or extent of ‘unstructured-ness’ is different for different industry sectors.

For example, R&D in a metallurgy firm is less sophisticated than in an engineering firm, while R&D labs in the electronic industry may be top class. Similarly, R&D labs in chemical industry, oil & gas industry may be more ‘unstructured’ compared to pharmaceutical or medical device R&Ds.

Then came an era of EVOP (Evolutionary Operations) and Experimental designs or Design of Experiments (DOE). This brought some structure to R&D’s workflow, yet not the desired finesse.

Scientists tend to record results in notebooks, spreadsheets, even on post-it (sticky) notes. Each scientist has his/her unique way of recording experiments, observations and results.

Data is usually decentralized and often buried under a pile of ad hoc experimentation, and this happens even in the most state-of-the-art laboratories. For example, pharma R&D labs have one of the most superior quality assurance norms to comply with. Yet when it comes to pharma R&D’s efficient data capture and data utilization, it has a long way to go!

This is one of the main reasons why pharma R&Ds run on high budget. I shall not take up hi-fi examples but simply put if a product can be developed with 20 experiments instead of 80, cost (of experimentation) will slash to around 1/4th. Now imagine if a product can be developed by mere few experimentations and no clinical trials? Wondering how’s that possible?

Yes, that’s possible- by digitalization!

R&D costs are skyrocketing, while ROI is steadily falling over the last decade. In 2010 the mean cost of bringing a new drug to market was US$1.1 billion ... in 2018 it was US$2.1 billion with clinical trials making up an increasingly large share of this cost. Meanwhile, the mean projected return on R&D investments fell from 10.1% in 2010 to 1.8% in 2019.

Digital transformation could reverse this trend of declining ROI in pharma R&D and improve R&D productivity over the next decade... What will it take to realize this potential?

A recent survey reported opinion of 250 global pharmaceutical and biotechnology leaders on the state of digitalization in biopharmaceutical R&Ds. It was found that some progress is being made, but that there was a greater-than-anticipated gap between the expected value of digital in pharma R&D and actions being taken to realize that value.

In the next part of this blogpost series, I shall discuss on- How to go for digitalization in pharmaceutical and medical device R&Ds.

​Meanwhile, note that there are fifteen different Continuous Improvement pathways for pharmaceutical and medical device R&Ds. Are you planning a Continuous Improvement initiative for your R&D division?

Part 3:  How to go for digitalization in R&D.
Part 4:  Advantages of digitalization in pharmaceutical and medical device R&Ds.
Part 5:  Digitalization in various phases of drug research and development.
Part 6:  Selecting the right R&D digitalization platform.
Part 7:  Six building blocks for digital transformation in pharmaceutical and medical devices R&D.
Part 8:  How to transform existing R&D setup to a digitalized R&D.
Part 9:  Tips and Quick wins of digitalization in pharmaceutical and medical device R&D.

Continuous improvement in pharmaceutical companies is in practice for the past fifteen odd years. It is vigorously practiced in few organizations especially large multinationals, while in most small and medium organizations, continuous improvement culture is in the making ...

One of the obvious reasons for this is- pharma space is highly regulated. As regulations change companies make necessary changes to comply. But this is not continuous improvement. This is simply compliance to regulations. Continuous improvement is a much broader topic.

Continuous improvement can transform businesses from being good to great or turnaround a sick business to become a profit-making enterprise. If practiced correctly continuous improvement brings-in exponential benefits to the company, its employees and customers ( i.e. patients).

When I say continuous improvement for pharmaceutical companies, improvement of R&Ds is a major aspect- be it- R&D design, functioning or output.

An effective productive R&D can develop products fast and give first-to-file benefit. If first-to-file is your objective then make sure your R&D labs have installed a culture of continuous improvement to build-in capable infrastructure, efficient & effective business processes and a talent pool having relevant skill sets.

There are 15 different continuous improvement pathways for pharmaceutical and medical device R&Ds; I shall discuss one such pathway- Digital transformation in Pharmaceutical and Medical Device R&Ds.

Digital transformation involves- Digitization and digitalization. Both digitization and digitalization are two significant tools for improving R&D functioning and output.

Digitization is conversion of data and processes from analog to digital whereas, digitalization embraces the ability of digital technology to collect data, establish trends, make better business decisions, in toto transform ‘how work happens’.

In this nine-part blogpost series, I shall touch upon digitalization in pharmaceutical and medical device R&D, benefits of digitalization & challenges to overcome, how to select the right digitalization platform, building blocks for digital transformation, success metrics and how to transit from existing R&D setup to a digitalized R&D.

In Part 2 of this series, I shall discuss on- Why the need for digitalization in pharma and medical device R&D labs? 

Meanwhile, note that there are fifteen different Continuous Improvement pathways for pharmaceutical and medical device R&Ds. Are you planning a Continuous Improvement initiative for your R&D division?

​Part 2:  Need for digitalization.
Part 3:  How to go for digitalization in R&D.
Part 4:  Advantages of digitalization in pharma and medical device R&Ds.
Part 5:  Digitalization in various phases of drug research and development.
Part 6:  Selecting the right R&D digitalization platform.
Part 7:  Six building blocks for digital transformation in pharmaceutical and medical device R&Ds.
Part 8:  How to transform existing R&D setup to a digitalized R&D.
Part 9:  Tips and Quick wins of digitalization in pharmaceutical and medical device R&D.

‘Go- digital’ is the new magic mantra in today’s time of the ‘new normal’. The new normal induced by the pandemic compels us to work differently as we successfully wade thru the un-lockdown phase …

Clear and inspiring communication is central to making returning to work a success. In addition to moving decisively on strategic changes, leaders need to help unnerved workforce believe in the future.

Traditional ways of working are ebbing fast. Most companies have installed sophisticated IT infrastructure such as ERP (enterprise resource planning) systems, payroll programs etc. More companies are moving towards automation and digitization. Industry 4.0 involves automation, robotics, artificial intelligence, IoT (internet—of-things), cloud-based working, big data etc. Needless to say, this involves big money.

A well -defined post-pandemic IT strategy should guide cuts and investments. Consider following three points while building your tech modernization strategy-
​

1. Define the target product portfolio based on current and new business with strongest prospects and most feasible funding models.
2. Invest efficiently and stick to the plan, operational and commercial excellence will deliver the strategy is full potential.
3. Create clear structure and processes for executing the new strategy across the organization.

Companies will also need to greatly improve their IT productivity to lower their cost base and fund rapid, flexible digital solution development.

Firstly, this requires quickly reducing IT costs and making them variable whenever possible to match demand. This means figuring out what costs are flexible in the near-to-medium terms. For example- by evaluating nonessential costs related to projects or maintenance and reallocating resources. Using process design methodologies such as CMMI (Capability Maturity Model Integration) can give first-class improvement in IT productivity while lowering costs to minimal.

Secondly, this involves defining a future IT product platform, establishing the skills and roles needed to sustain it, mapping these skills onto the new organization model and developing leaders who can train people to fill the new or adopted roles. Using process improvement methodologies such as- Design Thinking, TRIZ or Hoshin during new product platform development ensures a customer-centric product.

Thirdly, the adoption of cloud and automation technologies will need to be speeded-up, including bringing cloud operations on the premise and decommissioning legacy infrastructure.

Increasing the speed and productivity of digital solutions is key. To deal with the crisis and its aftermath, companies not only need to develop digital solutions quickly, but also to adapt their organizations to new operating models and deliver these solutions to customers and employees at scale.

Solving this challenge requires integrating business processes, incorporating data-driven decision-making and implementing proven change management models to manage the change. There are different ways to do this …

Keywords and Tags:

​#ContinuosImprovement  #NewNormal  #ITStrategy  #DigitalSolutions