Inefficiencies in Pharmaceutical Supply Chain Cost. Leveraging AI in Drug Supply Chain Management

Supply chain companies are adopting artificial intelligence at a great pace. Many companies are already on their way towards digital transformation across various industries that face supply chain management challenges. The AI applications in supply chain management include, among a host of other uses, process robotization, forecast demand, real-time visibility on shipments, and improved efficiencies in last-mile deliveries. According to Gartner’s report, the use of artificial intelligence (AI) and machine learning was one of the top 2020 trends in supply chain businesses and one of the top technologies giving companies a leg up over the competition.

The pharmaceutical industry is no different. Companies in various ways are applying AI in pharma to gain a competitive advantage. Slowly but steadily, the pharma sector is also becoming the next testing ground for companies looking to use AI to help digitalize and boost their global supply chains.

How is the pharma supply chain different from the classic model? What specific challenges do pharma companies face within supply chain management? And finally, how can AI be used in the supply chain to bring value and return on investment? This article gives an overview on the subject together with dedicated advice on how to implement AI process-wise.

How does the pharma supply chain work?

The pharmaceutical supply chain involves a complex network of steps required to produce a drug, from sourcing and supply of materials, warehousing, through manufacturing and distribution, to the drugs’ end delivery to the pharmacy and patient.

Major challenges in the pharmaceutical supply chain

For Big Data transformation in the pharma supply chain to succeed, organizations must transform to overcome the difficulties they face in an ever-changing technological environment.

Pharma’s biggest data challenge is its historical siloed structure resulting from decades-old legacy systems. These disparate data silos prevent access to drug manufacturers’ and distributors’ data in real-time. It is essential going forward that organizations strive for a data-centric and user-centric approach allowing visibility of the complete data across all channels - the right information at the right time in the right place.

As the operations are struggling to move forward, the drug discovery phase embraces new technologies like no other. Especially AI can have colossal impact for discover and development drug.The spectacular discoveries and a new wave of therapies based on complex biologic medicines and gene and cell therapies are becoming increasingly popular. Nevertheless, they throw up huge challenges for manufacturing and distribution networks due to their sensitivity, the need for temperature control and special handling, and short life cycle.

The biggest challenges pharmaceutical within the supply chain management include:

• lack of end-to-end visibility
• malfunctions at the manufacturing level
• tight deadlines and costly expedites
• fragmented multimodal networks
• cold chain - temperature control and strict handling along the whole process
• issues related to drug counterfeiting
• keeping highest quality and repeatability of drug manufacturing
• personalized treatment production for individual patients (e.g., T-cell therapies)

How AI in the drug supply chain can make a difference?

Artificial Intelligence presents the pharmaceutical industry with the opportunity to solve problems previously unsolvable with simple data analysis. Looking at the pharmaceutical supply chain specifically, we’re seeing a growing number of AI-based solutions offering answers to many of the industry’s current challenges. As organizations look to improve their operational performance, productivity, efficiency, and cost-effectiveness leveraging artificial intelligence across the entire pharmaceutical supply chain seems to be the next move. What are the major benefits AI can bring to the drug supply chain?

Process visibility

To compressively and accurately assess today’s complex supply chains, a 360-degree approach must be taken. Point-to-point visibility across the whole supply chain will enable companies to become more efficient by rapidly responding to and mitigating disruptions. AI solutions applied to the supply chain can provide an insightful and comparable benchmarking of pharmaceutical supply chains. As artificial intelligence relies heavily on data, a crucial first step must be considered to even think about AI solutions - digital transformation. Information flowing along the supply chain needs to be collected and normalized in real-time.

Digitalization is crucial for visibility, and it should be organized with the future AI implementation already in mind. Full visibility means coupled with AI for insights will reveal supply chain performance gaps, e.g., caused by low inventory enabled by low utilization and high unit cost, with a full profile of contextual information. Once this is in place, AI-augmented solutions for the pharma supply chain will provide advanced decision-making systems by efficiently collecting and managing data in real-time and generating actionable insights. Beyond these benefits, AI is capable of seeing patterns that even trained professionals might miss. Further, it supplies real-time actionable insights filling the potential knowledge gap between suppliers, drug manufacturers, and logistics providers.

Related case study: Developing a logistics platform offering real-time visibility and integrations with different carriersOne of our clients was seeking to improve the global supply chain management optimization product Our challenge? Providing visibility and data transmission for maximum efficiency and control. We supported solution development for end-to-end execution of logistics activities in Supply Chain Management at the PO/SKU level, including PO creation, stock management, suppliers and distributors management, consolidation and load planning, carrier allocation, documentation, and final delivery. Read more about this case study.

Optimizing pharma inventory management

Once you have an end-to-end view, you can optimize how the supply chain works, especially when it comes to inventory management. AI aids the decision-making process, like warehouses to stock with inventory according to proximity to their biggest customer bases, tracking warehouse capacity, and required transportation capabilities. Smooth warehousing also means tracking inventory to carry and how to distribute it. When and what to reorder for optimal inventory carry costs and supplier bulk order discounts to keep stock keeping units (SKU) profile and quantities at an optimal level.

Optimize for predictive maintenance

With full process visibility, one step further can be made on the path towards optimization with predictive analytics and machine learning technology. Using AI to process supply chain data is critical to support real-time decision-making, orchestrating operational efficiency, ultimately creating a cost-effective, near-autonomous, and thriving supply chain. The intelligent supply chain identifies and understands the patterns influencing it rather than projecting past demand. Solutions based on AI are generally better at demand anticipation (SKU, quantities) and characterization (localization, service levels). AI technologies can find patterns and interdependencies between variables that traditional methods would otherwise miss. Leveraging AI through real-time production and distribution monitoring can produce actionable predictions that will optimize maintenance, minimize shortages and bottlenecks and, ultimately, maximize pharmaceutical supply chain efficiency.

Medicine demand forecasting, logistics, and stock management

Digitalization and automation of operations can help businesses from the pharma industry establish cost-effective, reliable, and robust processes coordinated across the pharmaceutical supply chain. Adopting AI tools, such as image recognition and analysis, into distribution platforms will be the key to minimizing human error and leveraging operational data to generate strategic insights and improve productivity and accuracy. This can be optimized by implementing an Internet of Things (IoT) platform, which interconnects digital, physical, and third-party information through the use of chips, cameras, sensors, and networks. IoT connections, therefore, generate a vast amount of monitoring data, which can then be unlocked and optimized by using advanced analytics with AI opportunities.

A popular example of IoT coupled with AI technology would be quality assurance along the production line with interconnected sensorized devices and computer vision systems. Additional sensors that track environmental conditions along the supply chain ensure the pharmaceuticals’ development and distribution operations’ safety.

Including robotics solutions in the pharmaceutical supply chain helps reach an unprecedented level of automation to increase operation accuracy, repeatability, and throughput, while reducing human errors and improving quality and compliance.

Related case study: Optimizing drug distribution activities to hospital pharmacies networkTo improve current large scale procurement processes, a pharma company approached us to use applied analytics to stock and distribute drugs among US hospitals. Our challenge? Maximizing savings by streamlining the procurement of medication across the hospital network and their pharmacies. Read more about this case study.

Smart automation

Digitalization and automation of operations can help businesses from the pharma industry establish cost-effective, reliable, and robust processes coordinated across the pharmaceutical supply chain. Adopting AI tools, such as image recognition and analysis, into distribution platforms will be the key to minimizing human error and leveraging operational data to generate strategic insights and improve productivity and accuracy. This can be optimized by implementing an Internet of Things (IoT) platform, which interconnects digital, physical, and third-party information through the use of chips, cameras, sensors, and networks. IoT connections, therefore, generate a vast amount of monitoring data, which can then be unlocked and optimized by using advanced analytics with AI opportunities.

A popular example of IoT coupled with AI technology would be quality assurance along the production line with interconnected sensorized devices and computer vision systems. Additional sensors that track environmental conditions along the supply chain ensure the pharmaceuticals’ development and distribution operations’ safety.

Including robotics solutions in the pharmaceutical supply chain helps reach an unprecedented level of automation to increase operation accuracy, repeatability, and throughput, while reducing human errors and improving quality and compliance.

A secure process that is resistant to counterfeit

Medicines contamination is not an option for pharmaceutical companies. If a defective drug gets to a patient, the consequences are catastrophic and, in the worst cases, fatal.

End-user safety is always the main driver for drug manufacturers to ensure product quality, and companies introduce quality assurance processes to comply with regulatory affairs. Combining AI with other advanced technologies, such as mentioned in the previous paragraph, automation, can create a transparent, secure system and shielded from counterfeit and substandard drugs. AI algorithms, based on deep learning or machine learning, can be designed to distinguish between genuine and counterfeit versions of the same drug that can affect patient health.

A digitized and integrated quality and compliance system can be a competitive advantage in terms of quality and safety and costs. Drug manufacturers also want to avoid costly recalls and investigations if the affected medicine gets to market, investigations, and lawsuits that could drag on endlessly for years and continue to rise into huge losses. A manufacturing line that focuses on quality by design brings even more advantages, including reduced waste, minimized risks, and can eliminate errors early on in the drug development process.

Roadmap to support AI adoption in the pharma supply chain

The promise of AI in streamlining the pharma supply chain and delivering the next generation of medicines is clear. However, as is the case with most new technologies, outdated IT infrastructure and skillsets limit its uptake on the ground. To develop these new capabilities, we propose an iterative approach to scope the project and evaluate the Proof of AI Development. This approach allows companies to gain confidence in the models and use them as foundations for new service models supported by new abilities and analytical competencies before moving towards mature AI adoption.

Spot challenges where AI can bring unique value

Before you even start building any model, you have to define upfront how it will be used and why that’s going to make the experience better for your business and patients. For that, we recommend our custom AI Design Sprint workshops, where in just two days, we scope and quickly validate new project ideas. You can read more about this collaborative technique here.

Make sure your data is in order

Ensuring your data house is mature enough is critical. Artificial Intelligence models need a strong pipeline of clean, normalized data to produce quality results. You will need a digitized ecosystem that gathers historical data on suppliers, manufacturing, orders, transportations, etc. If you don’t have normalized data stored, start collecting it now. If you think about future AI development in a specific area, think early about the data needs it is going to produce. It is important to consult your idea with a data scientist to prepare a data collection strategy that will improve your future model’s accuracy and provide better value for your business. Digital transformation and building united data systems also take time, so make sure to plan it.

Start small with Proof of AI and be prepared for failure

Pharma supply chains must be ready for failure. Equally, this is also not a sector where a failure is an option. Testing with agility is key. Therefore, we recommend starting with a minimum viable product (MVP) version of your idea. The Proof of AI, as we call it, guarantees a much-reduced risk of failure, as it only takes a couple of weeks to develop. Still, you can quickly observe what AI brings and decide whether to follow up with further investment, whether you should shift or move forward to define your next key goal.

Hire the right experts’ team

Implementing artificial intelligence requires significant investment in terms of both finances and resources. Commonly, pharmaceutical manufacturers and distributors don’t have the skills required to implement AI solutions within their IT personnel. Outsourcing AI implementation efforts to a skilled agency with ML experts on board is much simpler than building an in-house data science team from the ground up without knowing how to do it. Keep in mind that engineers are not your only need. Just as much you need designers and project managers that have solid experience with the specifics of AI products.

Collaborate

Collaboration is vital for an AI technology project to succeed, especially in the SCM sector. Not just between global supply chain partners, but pharmaceutical companies must also pool resources and ideas to ensure the technology reaches its full potential. The healthcare sector has its specifics. Still, there is much to learn for the life sciences industry from other business sectors and their AI applications in SCM. Collaboration for healthcare projects also means cooperation with legal and regulatory departments for safe implementation.

Track and optimize for key metrics

Scale your digital solution, build automated data pipelines, and deploy your artificial intelligence products for real-time usage. Constantly improve by adjusting algorithms to meet your desired benchmarks and KPIs. See how it brings results for your business, and think about the next AI project that will boost the medicine supply chain for more sustainable healthcare industry.

Conclusions

The true value that AI-based software brings to the healthcare industry and various pharmaceutical companies is indisputable. Smart adoption of these emerging technologies for the pharmaceutical supply chains will enhance productivity, reduce waste and cut costs. Pharmaceutical organizations must adopt a proactive strategy to maximize value from prior investments by focusing on digitalization in existing solutions and processes and generating new value by embracing new, improved solutions, systems, and best practices to produce drugs highest standard.

References

PWC Pharma 2020: Supplying the future

Forbes - How Artificial Intelligence Is Improving The Pharma Supply Chain