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With cutthroat competition playing out in the world, pharmaceutical manufacturers might find traditional IT systems and infrastructure restricting their growth.
FREMONT, CA: The international pharmaceutical industry is rising, and the spending on the sector might nearly hit $1.5 trillion in the next three years. Due to this escalation, manufacturers will encounter a few challenges that include expanding distribution networks to meet the requirements of a broader audience.
Leveraging the big data can assist the manufacturers in building transparency throughout the supply chain and allow proactive action to alleviate the risks of any disturbances and counterfeiting. The channel of a pharmaceutical drug, which goes from manufacturer to patient, is an entirely different path than the rest of the supply chains such as foods and beverages. The product is passed from the direct control of the manufacturer through several touchpoints in a very complex supply chain. Passing through the complications reflects that most of the drugs are temperature-sensitive and need a particular type of handling. Pharmaceutical products, if neglected by not maintaining the prescribed temperature range, can lose its effectiveness and might become a threat for the patients consuming it, leading to an additional loss of cost.
Traditional IT systems can make the manufacturers blind when it comes to their inventory and distribution methods. It can also hinder the manufacturers from keeping track of various products that get in and out of the supply chain. Such abilities are essential among the distraction and carry the potential for theft within the supply chain that triggers the counterfeiting issue of the industry. Furthermore, the inability to reach the ins-and-outs of the supply chain makes it difficult to make sure a faster time in the market to satisfy the rising demand for personalized medicines.
On the other hand, big data analytics lend a hand in improving the measurement of the factors that are necessary to maintain in a supply chain such as pressure and temperature, by communicating and monitoring. Besides, big data analytics also allow condition monitoring and predictive maintenance via the collected sensor data such as vibration and temperature, which extensively narrows down the machine downtime, when it comes to drug production and packaging. Along with the gathered information, companies can execute the measures to recognize the probable component flaws and resolve them before any mechanical failure. Nevertheless, manufacturers can expand the efficiency of their maintenance teams and focus their efforts on debasing the assets.