While many processes in drug development and manufacturing still involve paper and manual records, digital technologies are required to obtain the elevated visibility and transparency needed to minimize risk, react to crises, and face other challenges. Outsourcing-Pharma recently connected with Stephanie David, vice president of IT consulting firm Pariveda Solutions, to talk about advanced digital technologies (like artificial intelligence, machine learning, and automation) that are needed for pharmaceutical operations to gain a competitive advantage.
OSP: Could you please share your perspective on how paper and manual processes still play a part in drug development and life-science supply chains?
SD: It is no secret that the life sciences value chain (from drug discovery through commercialization) involves a series of manual and capital-intensive processes, which are more often than not supported by outdated legacy on-prem, paper-based systems and infrastructures.
Even in today's digital age, paper and manual-human intensive processes are still the backbones powering most life science drug development and supply chains.
This heavy and sometimes painful reliance on outdated drug development, manufacturing, and supply chain processes and systems puts the patient population at risk of drug supply disruptions. While this reliance on paper and manual processes is highly inefficient and can create more risk in the long run, the reality is that life science companies do love to rely on paper as a single source of truth for record-keeping, as a way to help minimize costly errors that are vulnerable to security and compliance risk, a fear for every life science organization of all sizes - errors that typically equates to rework, and rework typically implies costly impacts to speed to market (e.g., product launch delays) or worse – putting the safety of patients at risks.
Manual processes are still a daily reality in drug development and clinical research papers, from managing hardware in clean spaces to documenting experiment results. Life science organizations are still manually inputting case and study reports and forms data into multiple systems, leading to inconsistencies and errors and the need for rework, slowing down clinical trial execution.
During a clinical trial, there can be hundreds of different consent and results from data in forms that must be mailed and faxed to each of the sites every day. Waiting for documents and paper-based forms to reach these sites can be days and even weeks, depending on size and location. If something were to go wrong and the information goes missing, the process starts all over again, creating an ongoing lag in end-to-end clinical operations.
In manufacturing and supply chain operations, even in the largest pharma organization, we find areas or sites that are still going through digitizing and automating processes. Therefore, paper and manual processes are what are being used to run their operations.
A change in any of the following areas - API production, packaging (artwork/labeling), formulation, quality management/quality control, and distribution requires a change request to be completed (paper), an impact assessment to be completed (Xls based), documentation of the changes to process and systems, including approvals and at times new SOPs (more paper) all in the spirit of remaining compliant with regulatory authorities.
Regulatory groups are still using Excel and home-grown systems to track products across various geographies. Different suppliers use different systems and label data differently, which makes each of these scenarios hard to access and reconcile data.
OSP: What are some of the problems and challenges (obvious and less so) that this reliance on such outdated processes might lead to?
SD: Inability to effectively de-risk and advance molecules into development faster, facilitate secure research collaborations between global research teams and simplify data movement from lab equipment to integrate research environments, resulting in longer development cycles, higher R&D costs, slower regulatory submission process, and important therapeutics opportunities getting delayed or discarded.
In the supply chain, essentially, most of today's life sciences supply chains are like analog machines that try to solve problems in a digital world. The primary challenge in life sciences supply chains is the insufficient adoption of next-gen planning systems and capabilities, which forces organizations to rely on many spreadsheets to support unsustainable end-to-end supply chain processes. The lack of end-to-end, real-time inventory visibility in the supply chain to make timely decisions can, in fact, negatively impact or disrupt patient supply.
In manufacturing, the paper-based paradigm continues to dominate shop floors and critical business processes. Some of the challenges seen are stemming from a lack of agility and flexibility to adapt at a faster pace and scale due to complex product portfolios (from small molecules to biologics, solid oral dose to drug-device combinations, large volumes vs. small volumes).
Some form of paper-based record is created when raw material moves from the testing lab to the factory floor, resulting in an extremely long review and sign-off lead times. Making revisions could take longer than a month, and to access product information, a staff member typically would need to hunt down individual documents stored in filing cabinets. The lack of real-time visibility across content and quality management processes can impact tracking, deviations, and generating more meaningful and actionable insights.
Also challenging are the price and burden of remaining compliant with regulatory authorities when depending on outdated processes and systems, and the overhead required to maintain outdated processes and systems
Less so – The resistance or fear to change due to lack of understanding of new emerging technologies and the stigma of implementing new digital solutions that impact GxP processes can be a massive undertaking that takes years to implement to enable a fully compliant and validated solution. While regulatory authorities encourage increased use of information and digital technologies, they continue to accept antiquated paper-based processes.
OSP: How has COVID-19 complicated supply chain issues?
SD: I believe COVID-19, plus many other macroeconomic forces, are driving a new era in life sciences. A new era that in turn is driving the unavoidable and terminal decline of traditional (linear) life science supply chains and forcing supply chains to be reimagined to be more resilient, data-driven, and intelligent.
The disruptions felt by COVID-19 exposed the vulnerabilities and weaknesses of an already fragile supply chain ecosystem that left supply chain executives scrambling to respond and in constant crisis management.
It exposed the following:
- Severe fragmentation along the value chain created a lack of shared information and data across the value chain and a lack of supplier visibility.
- An outdated supply chain operating model that relied on foreign operations with tax advantages, dependent IP, inventory buffers as a response to manage supply chain risk and long lead times.
- Insufficient adoption of next-generation planning systems and capabilities forces organizations to rely on spreadsheets to support unsustainable end-to-end supply chain processes.
- Heavy reliance on outdated supply chain management systems (e.g., control tower capabilities, lack of digital lighthouses, or providing real-time, end to end visibility into global supply chains)
- The dependency on foreign drug manufacturers for generics, APIs, and finished drug products.
It impacted life science supply chains in the following ways:
- Organizations were forced to change and restructure, pushing executives to identify the critical roles required based on the outbreak progression's stage.
- Predicting future demand patterns with little knowledge or insights in record time will hinder appropriate response plans to avert potential disruptions.
- Financial stability of suppliers and the lack of supplier visibility beyond the first tier to understand potential risks to their ability to meet contractual obligations on time
- Concerns over depleting (or idling) inventory and threats to product security - increased risk of counterfeit products reaching patients
- Increased risk of insolvency if supply chains were not adjusted to manage cash and inventory consumption effectively.
- Production challenges due to increased lack of raw materials and API availability
- Lead times are becoming increasingly unpredictable, and product shortages are compounded by the shortage of air and ocean freight options to move products to the US.
- Regulatory agencies halted inspections at overseas drug plants during lockdowns, and changes to legislation were introduced to incentivize innovation and advanced pharmaceutical manufacturing.
OSP: Please share some information on the evolution of digital tools in supply chain management in recent years. What are some of the key advances, and do you see any areas for opportunity/improvement?
SD: I believe we have come a long way and, more importantly, seen an unprecedented acceleration of the adoption of digital tools in supply chain management.
Back in 2017, we talked about enabling digital supply networks, or technologies such as predictive analytics, robotics, and better visibility over the movement of goods that would help warehouses and distribution centers keep pace. These would all play a key role in enabling a digital supply chain. It was clear that digitalization would change supply chains, but it was a bit unknown and an early work in progress. From enabling warehouse management systems with radio frequency guns to leveraging electronic data interchange between suppliers, you could say that some organizations were in the early stages of digitizing certain supply chain functions.
The next step was to enable network-based, end-to-end visibility solutions and the ability to reflect and react to what's going on in the real-world supply chain—to track the condition or location of goods or real-time traffic data to spot trends trigger appropriate changes.
Fast-forwarding to today, we describe the future of life science supply chains as a dynamic ecosystem or a patient-centric, purpose-driven supply chain. A resilient, data-driven, and intelligent supply chain that breaks down the linear and functional silos to dynamically connect and integrate your full supply network in a collaborative and optimized way. It is responsive, agile, and digitally driven to enable:
- End to end visibility
- Ability to quickly predict customer/patient demands
- Sense potential changes in supply and demand
The future of supply chains will also benefit from a new operational mindset:
- Revisiting globalization strategies to include further diversification of international supply chains and sourcing more regionally
- Enabling or revamping a Supply Chain Risk function that focuses on ongoing contingency planning as part of the new normal and formalizes crisis management processes into a playbook
COVID-19 increased the need for transparency, connectivity, agility, experimentation, and empowerment. It also accelerated the adoption of the building blocks of a resilient, data-driven, and intelligent supply chain. Important components like AI/ML, automation, data lake platforms, and other emerging technologies such as RPA, Digital Twins, and Blockchain will shape the future pharma supply chains to survive and gain competitive advantage.
OSP: Specifically, what risks have been minimized, and which still might pose concerns?
SD: The ways of working are changing significantly – data needs to be readily available in a consolidated and structured manner to enable business insights, discover new opportunities, and better identify vulnerabilities in the supply chain and manufacturing operations.
Data and digital skills need to be developed at all levels of the organization – shop floor to C-suite. Leaders need to become comfortable with discomfort. being vulnerable by opening up to learning from the new generation of the tech-savvy workforce early in their careers…" reverse or mutual mentorship." Digital transformation and innovation take a village – culture is critical to success!
OSP: Please talk about some of the advanced digital tools and platforms (such as AI, ML, automation, and robotics) and how their use and understanding are increasing.
SD: Research in the space is pointing us to appreciate how life science drug development and clinical research organizations will get to apply data with advanced AI/ML to innovate further and accelerate drug discovery and launch of new therapies, but perhaps predict when/how a patient may develop a life-altering condition and having the therapies to prevent it in the first place and with precision…or to enable digital agents trained to determine patient eligibility and recruitment of patients for any given trial in an accelerated way…or to enable "site-less" trials that will enable the tracking of patient stats remotely.
More efficiently, which can help optimize R&D spending and hopefully, in the not-so-distant future, deliver on the potential of patient-free trials. Through the evolution of advanced digital tools and platforms, organizations are improving data/digital awareness, understanding, and education – asking the "why" and "how."
In addition, organizations are investing in data accessibility – creating a data backbone and platforms that bring all data (structured and unstructured) together in a consolidated fashion, making it available to the business. On top of that, the creation of data visualization tools – accurate, connected, timely data, providing actionable business insight and performance transparency – for real-time visibility and help address the questions around "what is happening now" and "why did it happen"? and then add on top of that the advanced analytics layer to reduce repetitive tasks and increase robustness; move towards a more predictive and prescriptive state – to answer the questions: "what will happen"? "How can we make it happen"?
The use of other digital technologies such as IoT, sensors, etc. to help reduce or eliminate manual data manipulations to increase data integrity and automation of processes – required to have confidence in your data-informed analyses – moving away from the sea of spreadsheets
Combining these things should allow organizations to shepherd the industry and bring others along the journey and benefit from speed to market, precision in processes for efficiency and compliance, de-risking future product launches, etc.
OSP: Do you have any advice or comments you'd like to add?
SD: We have witnessed how the life sciences sector has risen to the challenge over the past 18+ months because there was no other option. We have seen over 3-4 years' worth of digital transformation in just a few months.
Almost overnight, life science organizations had to quickly mobilize and transform their ways of working to address two fundamental challenges –
- Accelerating the complex discovery and development process - which normally takes up to 10 years - w/out sacrificing patient safety, and at the same time preparing for the rapid large-scale production of vaccines and treatments to support the global health crisis.
- Continuing to deliver existing and new innovative life-saving therapies to the healthcare ecosystem—even as R&D, manufacturing, and supply chains were struggling to maintain business as usual.
So, I'd like to leave you with a few thoughts:
- The realization: It should not come as a surprise that those life science companies that started their digital journey to reimagine their business before the pandemic will be able to capitalize on those investments and will be better positioned to face and survive future disruptions/disruptor those that haven't or are still struggling to catch up will be on their path to face extinction. If unable to get their act together in the next 12 - 18 months.
- The recognition: Acceleration can be possible across the end-to-end pharma value chain, but to deliver breakthroughs and tangible business outcomes at pace and scale - this needs to be a C-Suite priority as it will result in companies having to transform multiple parts of their business all at once rather than sequentially.
- The acceptance: What was considered groundbreaking yesterday is deemed table stakes today. Here are a few reasons why:
- The demands stemming from patient service/patient experience will continue to be greater than ever, driving the need for personalized and digitized patient and customer engagement.
- R&D organizations will now be expected to accelerate the drug discovery, development, and launch process to bring new therapies to market faster.
- Supply chain and manufacturing operations will become less linear and less reliant on outdated infrastructure and humanly intensive processes to become a dynamic, connected, and autonomous ecosystem with the customer/patient at the center of it instead of the end of the value chain.
- Courageous leadership will be required to effectively drive the re-skilling of the workforce, making space for the rise of the "Digital Enterprise Solution Architect."
As we continue to witness life science organizations embrace new science trends and emerging technologies to accelerate digital shift and adoption further, the question is will organizations be able to drive this level of acceleration at scale over time to deliver the next generation of patient care? Will your organization be able to be part of the pack that gets to shape the future of healthcare?