Getting and sending medicines is complex. It includes many people, companies, and governing offices. Manufacturers, medicine buying groups, and healthcare services need to work in concert to ensure a seamless flow of prescribed drugs.
While all these stakeholders have a strong desire to reduce complexity, consumer-driven touchpoints add a new factor to supply chain management.
Patients want custom healthcare solutions and online access. They use personal health trackers and online platforms. As such, there's a need for supply chains to address human behavior.
The desire for personalized care began before the pandemic. But, COVID-19 sped up healthcare digitalization. Therefore, large medical supply chains need automation and AI-driven insights. These help companies understand and act on changes to how people behave and the tools they use.
The failure to forecast demand and adjust medicine production and distribution could upset the supply chain. Less adaptable ways can't respond quickly to changes. As a result, poor people experiences and outcomes may occur.
Merck's supply chain helps about 90 million people in more than 100 countries. The model uses automation, such as algorithms for forecasting demand. Yet, these models still needed some form of human interaction.
Merck wanted to build responsiveness and flexibility into its supply chain. A "self-driven" system helps Merck meet future challenges. First, Merck developed a "supply chain control tower." The control tower is a digital dashboard. It provides real-time insights into its supply chain.
Over the last four years, Merck improved its digital supply chain. Merck automated planning and manufacturing systems. Today, AI, ML, analytics, and sensors deliver real-time data and increase visibility.
The new systems give Merck the ability to:
Improvements to each area help Merck get medicines to patients without delay. The self-driving supply chain aids Merck's goal to be there "for people at every step, helping to create, improve and prolong life."
Merck partnered with several tech companies. Doing so helped Merck automate its supply chain. For example, Merck relies on Aera Technology Inc for cognitive automation. Cognitive technologies use natural language processing, machine learning, and robotics to automate systems. It increases visibility into the pharmaceutical supply chain. Plus, the tech offers mental input to healthcare leaders.
Merck also uses Tracelink to assign a unique product number to medicines. Merck can view the journey and touchpoints for each drug via product tracking. A track-and-trace network reduces counterfeit medicines and helps Merck follow regulations.
Partnering with tech companies gives Merck more time to focus on consumer solutions. It also ensures precise demand forecasts. AI- and ML-driven platforms reduce human errors and downtime. Moreover, digital systems offer smart insights for proactive decision-making.
Task automation reduces the need for human intervention. But, Merck understands the worth of its workforce. Instead of asking employees to complete data entry or number crunching, Merck looks for places where humans can add value.
To this end, Merck is upskilling supply or demand planning staff. It uses a "digital supply chain academy" for worker retraining. Merck shifts staff from planning and forecasting to probing data and delivering business insights.
Combining tech platforms with a human touch lets Merck make quicker, data-based decisions. Plus, Merck can find trends affecting supply chains.
Real-time decision-making requires end-to-end supply chain transparency. Companies must produce insights into user behavior and its effect on supply chains. But, total visibility isn't possible without advanced tools. Machine learning and neuronal networks offer these features.
Shifting to a digital supply chain allows Merck to manage:
A connected system provides data to assess end-to-end supply chain performance. Also, smart tools and methods let Merck meet patient demands.
Medicine supply chain management is hard to achieve without technology. But, deep learning and automation help companies improve systems while prioritizing patients.