AI-Augmented Predictive Business Intelligence Capabilities and Sustainable Supply Chain Performance Through the Serial Mediating Roles of Data-Driven Decision-Making and Inventory and Demand Visibility in Community Pharmacies
This study demonstrates that in community pharmacies, AI-augmented predictive business intelligence capabilities enhance sustainable supply chain performance not directly, but through a serial mediation pathway where they first foster data-driven decision-making, which subsequently improves inventory and demand visibility.
Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer
In the quiet aisles of a neighborhood pharmacy, a complex dance of numbers happens every day, though it is rarely seen by the customer. Behind the counter, pharmacists manage a delicate balance: they must keep shelves stocked with the right medicines for people who need them immediately, while ensuring that no medication sits too long and expires, turning a life-saving resource into medical waste. This balancing act is the heart of supply chain management, a field that studies how goods move from producers to consumers. For decades, experts have believed that simply having access to more data and powerful computer tools would automatically solve these problems. The logic was straightforward: if a business could see its inventory and demand clearly through advanced software, it would naturally make better choices, save money, and reduce waste. However, recent thinking in business science suggests that technology alone is not a magic switch. Instead, the value of digital tools depends entirely on how people use them to make decisions and how those decisions change the way the business actually operates.
This question of how technology translates into real-world results is particularly urgent for small healthcare retailers like community pharmacies. These businesses operate in a high-pressure environment where a shortage of a specific drug can affect a patient's health, and where expired stock represents a direct financial loss and an environmental hazard. Researchers from universities in Jordan set out to investigate exactly how artificial intelligence and predictive business intelligence tools function in these small settings. They wanted to know if buying sophisticated software was enough to create a sustainable supply chain, or if something else was required to turn digital data into tangible benefits like fewer empty shelves and less wasted medicine.
To find the answer, the research team surveyed 360 community pharmacies across four northern governorates in Jordan. They asked the owners, managers, and chief pharmacists about their use of digital systems, how they made purchasing decisions, and how well they could see what was happening with their stock and customer needs. The study focused on a specific type of digital capability called AI-augmented predictive business intelligence. In plain terms, this refers to systems that gather sales and inventory data to forecast future trends, such as predicting which medicines will be in high demand or which ones are moving too slowly. The researchers were not looking for fully autonomous robots making decisions; rather, they were interested in how these tools support human managers in spotting patterns and preparing for the future.
The study tested a specific chain of events to see how these digital tools actually worked. The researchers proposed that the software would first help managers make better, data-driven decisions. These improved decisions would then lead to greater visibility, meaning the pharmacy would have a clearer, real-time picture of their inventory levels and customer demand. Finally, this clarity would result in a more sustainable supply chain, characterized by lower costs, less waste, and better service for patients. The team analyzed the responses using advanced statistical methods to see which links in this chain were strong and which were weak.
The results revealed a surprising and important truth about how digital transformation works in small businesses. The study found that the AI-augmented tools were indeed very effective at helping pharmacists make better decisions. When the software provided insights into demand trends or flagged slow-moving items, the managers used that information to adjust their purchasing and stocking strategies. However, the study also found that having these better decisions did not automatically lead to a more sustainable supply chain. The direct link between the technology and the final outcome was not significant. In other words, simply having the software and making smarter choices with it was not enough to guarantee the desired results.
The missing piece of the puzzle turned out to be visibility. The data showed that the path to sustainability only worked when the improved decisions actually translated into a clear, operational view of the pharmacy's stock. When managers used the data to gain a sharp understanding of what was in their inventory and what customers were likely to need, only then did the supply chain become more efficient and sustainable. The study confirmed that the digital tools created value not by acting on their own, but by enabling a specific sequence: the technology improved decision-making, which in turn created better visibility, and that visibility was what finally reduced waste and ensured medicine availability.
This finding challenges the common assumption that buying the latest technology is a guaranteed solution. The researchers concluded that for community pharmacies, the investment in digital systems must be viewed as a tool to build better routines and clearer sightlines, not as a standalone fix. The technology acts as a catalyst, but its power is only realized when it is woven into the daily habits of the staff and used to create a transparent view of operations. Without this step of turning data into clear visibility, the potential benefits of the software remain untapped.
The study also highlighted the practical limits of this digital pathway. While the connection between data, decisions, and visibility was statistically significant, the overall impact on sustainability was modest. This suggests that while digital tools are a helpful part of the solution, they are not the whole story. Other factors, such as supplier relationships, government regulations, and the size of the pharmacy, also play major roles in how well a business performs. The researchers noted that their findings apply specifically to small community pharmacies in an emerging economy and may not tell the whole story for larger hospital systems or distributors.
Ultimately, this research offers a clear guide for pharmacy owners and managers. It suggests that the goal should not just be to install a new software system, but to design how that system is used. The most effective approach involves using digital dashboards to track reorder points, setting up alerts for medicines nearing their expiration dates, and regularly reviewing supplier performance. By focusing on these specific, routine actions, pharmacies can turn their digital capabilities into real improvements. The study confirms that the path to a sustainable supply chain is not a straight line from technology to results, but a journey that requires turning data into clear decisions and those decisions into visible, operational reality. For the small pharmacy, this means that the true power of artificial intelligence lies not in the code itself, but in how it helps the people behind the counter see their business more clearly and act with greater confidence.
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