Leveraging Data Science and Artificial Intelligence to Overcome Pharmaceutical Distribution Challenges

The pharmaceutical distribution process is becoming increasingly complex. Obtaining accurate, timely, and complete information on the product’s location in transit can be challenging for pharmaceutical distributors. Data science and artificial intelligence are two emerging technologies that can help overcome these challenges. In this blog post, we will explore the current challenges with pharmaceutical distribution processes, how data science and artificial intelligence are already being applied to the pharmaceutical supply chain, and how they may bring even more value.

How are Pharmaceuticals Distributed?

The pharmaceutical distribution process is a complex undertaking that requires careful planning and teamwork. As the industry becomes more competitive, companies are looking for ways to cut costs while maintaining their high-quality standards to gain an edge over competitors. The hope is then that these savings will be passed on to consumers to lower drug prices, but this may not always be the case.

So, how are drugs distributed? The pharmaceutical distribution process requires careful planning and teamwork. The physical, financial, and information flows among stakeholders involved in the distribution ecosystem are highly complex.

Three patient-facing players have a direct role in providing pharmaceutical access in a way that is visible to the patient:

• Providers - treat patients through various methods, often prescribing pharmaceuticals to help manage symptoms or conditions.
• Pharmacies - distribute prescribed drugs and may related medical treatments, as well. The pharmacy operates separately from the provider, but they may be part of a larger system.
• Payors - collects payments from them and uses their revenue to cover medical issues that arise (usually health insurance companies or governments).

Other ecosystem players play an integral part in providing health care to patients that aren’t directly visible to them:

• Pharmaceutical Manufacturer - brings a breadth of therapies to market, all designed with the patient in mind. They offer continuity of supply and work within highly regulated environments so you can count on quality and safety when it comes to your own care.
• Distributor - provides patients with timely access to safe prescription pharmaceuticals by purchasing from many manufacturers, ensuring the products are warehoused and transported appropriately and in compliance with regulations.

Last but not least come the three hidden players that are responsible for negotiations:

• Group partnering organizations (GPOs) - help providers and pharmacies save money by aggregating purchasing volume to negotiate discounts from manufacturers and other ecosystem participants, resulting in lower-priced medications.
• Pharmacy benefit managers (PBMs) negotiate with providers and pharmacies as formularies dictate what drugs patients can access at various dispensation points and the price they pay for those drugs.
• Pharmacy services administration organizations (PSAOs) - support the pharmacy community with a mission to make drugs affordable. PSAO ensures that small independent pharmacies have access to affordably-priced prescription drugs by supplying them with necessary resources like tools, systems, and consultative support, as well as negotiating contracts between payors and PBMs. Hence, they can offer competitive prices while still giving quality care at every turn of service.

Within the pharmaceutical supply chain, drug wholesalers play an important role. They are responsible for transporting drugs to retail pharmacy stores. The pharmaceutical distribution process requires a complex set of steps to get the drugs from the drug manufacturers to patients, distributors, and retailers. Retailers (e.g., specialty pharmacies) are then responsible for stocking these products on store shelves before purchasing them when needed. These intermediaries play an important role since it’s unlikely that most people will have access to hospital pharmacy services without going through a retailer at some point.

There are three classes of prescription pharmaceuticals designed to manage patient care:

• Branded pharmaceuticals are manufactured by a company that holds exclusive rights and the brand name granted by the patent and deprives competitors of the ability to do so.
• Several companies can manufacture generic prescription drugs (generic drugs), and this occurs after the exclusive patent rights granted to the original manufacturer have expired. The level of competition in this market segment is very high.
• Specialty drugs are designed to treat costly medical issues incurred by a small fraction of the population. This segment has a branded and generic component based on whether the patent granting exclusivity has expired.

All three drug categories have different pharmaceutical supply chain operations in place:

• Pharmacy Distribution - for pharmacies, health systems & hospitals;
• Specialty Drugs Distribution - specialty drugs for physician practices and specialty pharmacy;
• Medical Practice Distribution - for non-specialty practices.

Some regions may have just one wholesaler, and others may have five, with each handling a different specialty product line. Branded drugs and some specialty drugs, due to patents, are developed, manufactured, and distributed by just one big pharmaceutical company (the Pfizer-BioNTech COVID-19 vaccine as an example).

In Europe, it’s common for there to be 100s of wholesalers in large countries like Germany or France. This can lead to differences in availability of products across markets as well as logistical challenges such as how long medicines take between manufacture, warehousing, and finally being delivered at pharmacy counters or hospital according to local needs (e.g., significant for time-sensitive specialty drugs, and again as an example, the Pfizer-BioNTech COVID-19 vaccine with distribution operations built on a flexible, just-in-time system, which will ship the frozen vials direct to the point of vaccination).

Pharmaceutical supply chains, which vary in complexity and scope depending on the size of a company or its specialty, have been an important component of the success of a particular organization. The traditional pharmaceutical supply chain is designed for companies with a couple of facilities that need raw materials from one facility shipped out to make finished products at another. However, this process can be quite costly as it requires expensive warehouses and trucks used exclusively for these purposes. Secondly, it is not an easy process to scale. With so many players vying for market share amidst the rising costs of operating any business, striving to maintain profitability has become even more difficult than ever before. The advent? Data science and artificial intelligence!

Current Challenges in the Drug Distribution Process for Wholesalers and Retailers

Distribution operations start with the drug manufacturers who produce fresh batches of medicine. Wholesalers distribute those products to pharmacies while simultaneously managing inventory levels for retailers to provide customers with access to medical supplies when they need them most during emergencies or pandemics.

Complying with Evolving Regulations

In today’s world, these distributors are faced with continual challenges every day - new regulations that govern drug production and distribution like the Drug Supply Chain Security Act (DSCSA) championed by distributors, which aims to trace prescription drugs as they move through the supply chain, staying in line with controlled substance Suspicious Order Monitoring (SOM) programs, increased scrutiny on product safety from regulators such as the FDA.

Legacy IT Infrastructure

One of the current challenges with the pharmaceutical supply chain is outmoded systems, making it difficult for distributors, pharmacies, and physicians alike who need timely access to needed medications in emergencies such as pandemics or natural disasters.

Keeping Drug Prices in Check

Another challenge for wholesalers working within the pharmaceutical distribution process is the high cost of healthcare in the United States and other countries. With an ever-increasing number of Americans opting for higher deductible health plans and price-sensitive customers due to recent drug shortages, pharmaceutical distributors are faced with a need to supply affordable drugs and ensure their profits go up while costs stay down.

Balancing the Demand and Stock

Another challenge comes from the difficulty in identifying and locating patients that need a specific drug at a certain time. This is especially important in the event of a drug shortage. With artificial intelligence being applied at every level of the pharmaceutical supply chain from production line management through delivery logistics, future disruptions could be mitigated before happening.

Opposite to drug shortage lays stocking up with medications no one is buying. Pharma companies can’t predict what will sell or not even when there are shortages. Balancing between PO (Purchase Order) management and SKUs (or Stock Keeping Units) are a significant part of this challenge.

High Customer Experience Expectations

The next challenge is that pharma distribution processes do not always consider what patients want or expect from suppliers when it comes to service quality, such as which locations they’d like to receive deliveries at or how quickly they would prefer the medications be delivered.

A Battleground for Big Players?

Lastly, pharmaceutical supply chain processes can be time-consuming and expensive to set up, while achieving positive outcomes is difficult, especially for small pharma companies or start-ups trying to break into the industry. Smaller drug distributors also play an important role in the pharmaceutical ecosystem by serving independent customers or operating in narrow geographic- or product-focused niche markets.

Data Science and AI in Drug Distribution - Current State

To address the issues with the pharmaceutical supply chain, artificial intelligence has been proposed as a solution by many experts, as an example, McKinsey & Company report states: “AI will transform pharma supply chains by optimizing inventory levels at all points on the continuum based not only on historical demand patterns.”

Pharma distributors have been looking for new solutions to their major challenges, and data science is at the heart of their search. Data enables pharmaceutical companies to understand what consumers want from a service-oriented perspective, including which locations they prefer receiving deliveries from or how quickly they would like medications delivered. Artificial intelligence can be used in pharma distribution processes, such as forecasting demand by analyzing past drug purchases and predicting future purchasing patterns based on customer behaviors.

Data science is being applied to pharmaceutical distribution problems, such as optimizing delivery routes and identifying the best time for a product to be delivered. Artificial intelligence has been explored in pharma distribution solutions, such as using it to identify patterns of drug movement across different points of sale.

Data science and artificial intelligence are being leveraged by major pharma companies like Pfizer or Roche to overcome the aforementioned pharmaceutical distribution issues efficiently. These advancements will help improve access, quality, safety, transparency, and oversight across all levels of the supply chain.

Another example of a company already using AI in the drug distribution process is Genco’s wholesaler company. They are using AI to optimize their delivery routes to make the most efficient deliveries possible to save money, fuel, and valuable time while providing a reliable service for customers.

Drugmakers, like Johnson & Johnson, have also used AI and begun incorporating it into their manufacturing process to identify potential risks before production starts or when problems arise during production. This type of solution will help lower recalls and reduce costs associated with identifying and fixing quality issues at a later stage of the pharmaceutical distribution process.

In conclusion, data science is already being incorporated by major pharma companies across all levels of the supply chain to overcome current challenges such as access, safety, transparency, etc. Looking at company budgets, it is definitely easier for big pharma companies to leverage AI solutions. But does it mean that smaller players cannot use AI and data science to their advantage and compete? Not at all. AI is a vast field, with many opportunities for innovation that can be leveraged by any pharmaceutical company, big or small.

How can Data Science and AI be Used to Overcome Drug Distribution Challenges?

Smart Drug Prices

Data science can be used to determine the likelihood of a patient being able to afford their prescribed medication. Pharmaceutical companies and distributors can acquire data on insurance plans and prescription drug pricing information. Using this information, it becomes easier for pharmaceutical distribution teams to identify patients who will not have access to affordable care to provide them with longer notice before they are due refills so that other options can be explored while ensuring compliance with regulations. The prices of prescription drugs greatly depend on the patient’s insurance coverage and the level of out-of-pocket costs. By using data analytics to identify patients who will not have access to affordable care, pharmaceutical companies can tailor their distribution strategy accordingly in a way that balances drug pricing with compliance requirements.

Secure Solutions for Drugs with a High Risk of Diversion

There is an increased demand for pharmaceutical distribution companies that specialize in controlled substances due to the required security measures needed and other federal regulations. Data science provides insights into which products would be at the highest risk of being diverted so these areas could receive more attention from distributors and regulators leading up to their release into the market. With this information, pharmaceutical companies will be better prepared when it comes time to distribute their products across various markets based on anticipated levels of illicit activity.

Predicting Shortages and Stock-Ups

Pharmaceutical companies are challenged with ensuring patients have access to affordable healthcare while staying compliant with regulations. Data science is increasingly being utilized within pharma distribution teams because of its ability to do predictive analytics, which evaluates risks related to compliance, including potential drug shortages or overages in production. Some ways this has been done include analyzing inventory levels, predicting future usage based on historical purchase order data, and determining the number of days a drug should be in stock. AI systems could also predict which medications need replenishment by analyzing inventory levels and predicting future usage based on volume patterns or past prescriptions. This way, pharma companies don’t have to guess or risk stocking up too much with medications no one is buying. They know exactly which patient needs what medication, and they can direct all other shipments elsewhere.

Artificial intelligence algorithms could also help predict when a certain product would sell out along its supply chain. With data analytics, wholesalers can better understand their customers’ needs, identify the top distribution locations for particular drugs, predict which patients may need specific medications in the future so they could be provided with reminders or even alerts about when a drug will expire if not used soon enough. This kind of predictive analytics is super powerful and can aid pharmaceutical distribution teams in estimating supply, demand, and inventory. Predictive analytics can also be used in other areas (read our article on predictive analytics in supply chain management).

Optimize Drug Distribution to Retailers

AI could also simulate the optimal way to distribute medication based on a hospital’s need. AI could also optimize drug distribution to retailers in a way that reduces the likelihood of shortages. For example, by identifying different stores and their needs, it would be possible to determine how much inventory should go where for the best chance of meeting demand over time based on pharmacy availability or proximity to other points along a supply chain.

AI can enable hospitals and pharmaceutical distributors with an unprecedented level of insight into what they need before any issues arise - this includes predicting when products are expected to run out and make adjustments accordingly; knowing which items are more popular than others, so those get pushed up in priority; understanding unit cost across all channels at every point during production operations so better decisions can be made about manufacturing priorities; being able to predict potential shortages of raw materials for production and taking steps to prevent them.

Related case study: Optimizing drug distribution activities to hospital pharmacies network

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

Reduce Warehousing Needs

Artificial intelligence has many applications that can improve pharma distribution processes by assisting warehouses in replenishing stock levels of products more quickly than humans alone would be able to do; predicting demand from prescription orders on behalf of physicians who prescribe them, and helping companies avoid stock-outs at stores level since AI systems have access to much larger quantities of information related to market trends. AI can also allow pharmaceutical distributors to manage better their inventory, including reducing the need for warehouses nearby by enabling a virtual warehouse; giving distribution managers more information about what is being stored so they know when it should be replaced or re-ordered; helping identify counterfeit and expired drugs while still providing consumers access to the medicines they need.

Find New Patterns in your Data

Machine learning can process data faster than humans can spot patterns more quickly and identify outliers that may otherwise go unnoticed. In addition, ML can be used to predict the likelihood of a drug shortage and make better early warning systems. Data science is also helping wholesalers in this industry win markets with predictive analytics on competitive advantages or customer relationships which are usually difficult to quantify but necessary for success over time. Customer data collected from customers’ online browsing history combined with geolocation information gives pharma companies insights that would otherwise not be available without Artificial Intelligence. According to McKinsey Research, this type of analysis has helped some pharmaceutical manufacturers increase sales by up to 25%.

React to Anomalies in Pharmaceutical Supply Chain

AI could help make product recalls easier with automated operations that would alert all stakeholders immediately after identifying risk. This saves time and reduces stress on staff who are stretched thin during emergencies like Ebola outbreaks where there was a shortage of qualified staff.

Gain Competitive Advantage

As pharmaceutical companies face increasing competition, they are looking for other ways of making money. For example, pharmaceuticals can use distribution networks as additional revenue generators if a more rational and efficient drug distribution infrastructure is developed. The pharma industry was, until now, one where the supply chain dominated logistics and decision-making processes were ill-informed because most data available for analysis was on product shipments rather than demand trends. There has been a shift in this paradigm with an increased focus on customer insights that will help address these challenges head-on through artificial intelligence solutions like machine learning algorithms.

In summary, Data Science and Artificial Intelligence have many benefits for solving pharmaceutical distribution problems. Still, before implementing any solutions, there are several challenges like a lack of knowledge about how AI works within this sector and the cost of implementation due to its complexity.

Why are Data Scientists Needed for a Successful Implementation of AI in the Drug Distribution Operations?

To successfully implement artificial intelligence, data scientists are needed. Data science is the application of statistical skills and analysis on large datasets. It is a fast-growing field that sees many different applications in pharmaceutical distribution operations. Skilled data scientists are crucial for the success of artificial intelligence in the pharmaceutical distribution process because they can find and evaluate appropriate datasets, analyze them to see what conclusions and insights are possible, and help make sense of large amounts of data. They also play an important role in making sense of large amounts of data by highlighting patterns or trends (e.g., identifying potential anomalies) with machine learning concepts such as pattern recognition, predictive analytics, prescriptive analytics, or anomaly detection.

Data Science and AI Implementation Tips

Data Scientists Need to be Involved from the Start

Pharma companies have been taking a slow approach in adopting AI. This is large because data scientists are not typically part of pharmaceutical organizations, and this disconnect has caused many problems in drug supply chains, including forecasting errors and missed opportunities for maximizing profits.

The Process of Spotting AI Opportunities in Drug Distribution should be User-Centric

The team focused on the AI implementation (AI Experts) need to assess the specific needs and risks of a particular pharmaceutical company. The decision as to whether or not AI is right for a company will depend on how it can bring value in solving problems or how it can expand frontiers and bring totally new value. Your company can choose to integrate AI into existing pharmaceutical distribution processes or create an entirely new process. We advise collaboratively approaching this step during AI Design Sprints.

The AI Implementation Process is a Team Sport

If you decide to outsource data scientists, they need streamlined communication access to various departments at your company. They should be able to speak freely with experts within pharmaceutical organizations such as manufacturing, marketing, regulatory affairs, sales & distribution. Moreover, there is a need for coordination between various departments as the implementation of artificial intelligence will require changes in policies and procedures. Policymakers are needed who have expertise in how data science should be adapted to pharmaceuticals, with special attention paid to regulatory affairs and cybersecurity. Having Product Managers experienced in coordinating AI implementations on board is crucial.

Consider Risks from the Start

Risks include changing customer expectations due to not understanding that AI works best when it’s part of a human-centric approach where people conduct tasks alongside machines rather than replace them. Investing time and money into wrong problems and, therefore, wrong solutions are terrible in effects. Rapidly evolving technology could lead pharma companies down rabbit holes they won’t be able to get out of because while good technologies sometimes come from unexpected places, bad ones always do too.

Understand the Culture of Iterative Experiments

Building AI models is always an iterative process. You should start with some data samples and build your first Proof of Concept model (or Proof of AI, as we like to call it). Then you will want to test your model in a small-scale environment. And finally, once the model is robust enough for production, we always advise testing it against some data samples that are not included in any of the training sets which were used when creating the AI model.

The Future of Drug Distribution with Data Science and Artificial Intelligence

As expected, Data Science is a rapidly growing field, so new innovations emerge at lightning speed! It’s hard not to get excited when you think of the possibilities that the future holds. AI is going to continue becoming more and more important for pharmaceutical distribution operations because it solves many of the current challenges. It is important that also discovery and development of drugs can be challenging, not only their distributed, and here AI can help too.Today’s pharma companies are already using AI to find solutions and leverage their business. No matter your size or place along the chain, you can use AI by leveraging a human-centric approach that helps identify problems and find the best possible solutions. To do this successfully, consider hiring experienced data scientists and AI because they’ll be able to guide your company through the process while implementing these technologies where appropriate. If you’re still unsure whether or not your company should invest in an expert like this, take some time today to explore what other organizations have done with AI - read about how others’ successes could work for yours too!