Tracking, measuring, and analyzing the mountains of data produced by the pharmaceutical supply chain is a monumental task. The advent of serialization and track and trace compliance mandates introduced an exponential increase in product data, and digital sensors that can track and transmit information about the various links in a supply chain provide unparalleled opportunities to measure, analyze and improve performance goals.
As supply chain digital transformation continues to accelerate, advances in software and cloud-based platform applications allow life sciences companies to harness traditional delivery, production, and shipping records and transform it into valuable data. Supply chain data analytics uses these powerful software tools to find patterns and trends in supply chain data.
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Why supply chain data analytics are important
As the pharmaceutical supply chain becomes increasingly global and medicine becomes increasingly personalized, these vast supply chain networks must increasingly find ways to react to real-time changes in supply and demand. A surge in demand for an essential product can cause operating costs to spike. Unexpected drops in an enterprise's products or services can leave a company with excess inventory.
Traditional methods of supply chain risk management have relied on stockpiling and similar tactics as a band-aid approach to fluctuations in demand. Stockpiling can, in some cases, allow operations to continue for a time even when upstream deliveries are disrupted, or to contend with fluctuations in demand for an organization's product.
These methods are not completely without their own risks. An enterprise could lose product to wastage or spoil, watch their potential profits be entirely consumed by the costs of storage, or fail to maintain a competitive advantage in the market.
Increasingly, companies are building out extensive supply chain networks to diversify their supply and increase overall supply chain resiliency, while at the same time implementing more effective means of sensing demand and increasing forecasting accuracy.
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How supply chain data analytics is implemented
Supply chain data analytics requires accurate data to forecast demand, understand the disposition of products throughout the end-to-end supply chain, and, ultimately, sense patterns. The more complete the record of a supply chain is available, the better the results will be. For example, if one step in the supply chain includes information that is missing or filled in with random values after the fact, any analysis may end up trying to fit information that is inaccurate. This information will be analyzed using software tools which will not necessarily know there is bad data in the mix. More detail and accuracy yield greater insights.
The most basic supply chain data analytics will use this data to help construct visualizations for a human operator to analyze for trends. More advanced supply chain data analytics uses automated pattern detection software and even artificial intelligence algorithms to find and identify correlations in the data.
Supply chain data analytics software can be run locally by an enterprise or in the cloud, though enterprise systems on their own suffer from data silos and a lack of visibility beyond the enterprise's four walls. Cloud implementations may offer an easy to use platform to access the software, or they may use standalone apps as portals.
Supply chain data analytics offers organizations increased insight into their supply chains, enabling more informed decisions about production. The information gleaned from supply chain data can help an organization streamline operations and pursue supply chain optimization.
Instead of having to build inefficient supply chain redundancy in case there are unforeseen disruptions, an organization using supply chain data analytics can prioritize their operations based on performance metrics and real-time data. Being able to proactively identify risks of disruption creates a more agile and resilient supply chain.
Supply chain data analytics allow an organization to optimize their operations across a wide range of dimensions, from supplier relationship management to extending global supply chain value networks to ensuring patients receive the medicines they need, on time and in full.
FAQs: How can supply chain data analytics help make supply chains more robust?
How can supply chain data analytics help make supply chains more robust?
Sophisticated data analytics software can help to identify patterns and trends that would otherwise be lost in the flood of information created by supply chains. This can reveal supply chain opportunities that would otherwise be overlooked. Being able to proactively determine where a supply chain fails can help organizations increase supply chain resiliency by taking measures to reinforce weaknesses, or quickly pivot to meet demand.
Why is supply chain data analytics better than just building extra redundancy into a supply chain?
Supply chain data analytics is often more cost effective than simply increasing the amount of suppliers or the capacity of warehouses. In addition, by using advanced prediction algorithms, an organization can anticipate softening downstream demand and avoid spending resources producing when it isn't economical to do so.
How does supply chain data analytics help reduce waste and energy usage?
The unused, excess capacity organizations carry in order to be able to mitigate supply chain disruptions require energy to maintain, and with increasingly specialized medicines with exacting requirements, in many cases stockpiling is not a viable option. Supply chain data analytics can help organizations operate more leanly, scaling operations according to available upstream supplies and downstream demands.
How can artificial intelligence be used in supply chain data analytics?
Artificial intelligence algorithms are powerful computer programs that can find patterns in data that humans might miss. These patterns and trends may be very subtle, linking seemingly disparate elements with far flung effects. Being able to find and highlight these small correlations is a big benefit of supply chain data analytics.
What can an enterprise do to optimize their supply chain?
Supply chain optimizations can include making purchases when prices are low, or can be as complex as reorganizing production to take advantage of more efficient delivery and shipping schedules. In order to be able to make informed choices about what types of changes need to be undertaken to optimize a supply chain, rigorous supply chain data analytics should be used.