Data-Driven Decisions for Biopharma Supply Chains
Learn more about how recent advancements in big data and data-driven automation can drive real-time visibility and cost reduction to streamline your supply chain.
What is Data-Driven Supply Chain Management?
In recent years, our news feeds have been inundated with stories of new and innovative technologies across a variety of industries. Some major examples include artificial intelligence and machine learning.
For businesses, it is critical to embrace innovation, as opposed to working against it. Technology can help departments speed up processes, automate routine tasks, and find common areas for concern or other patterns. Supply chain management is no different. With multiple moving parts, supply chains often have multiple communication touchpoints, processes, and deliverables from beginning to end.
In this article, we discuss how advancements in areas like big data, process automation, forecasting, performance reporting, and more, are helping to streamline supply chains at organizations who are willing to take the leap and incorporate the innovative technologies into existing, manual task areas.
External vs. Internal Data
A big question when getting into data-driven decision making is how to determine which data is the most effective to collect and use. Many find it difficult to determine the fine balance between too much and too little data. A good way to ease yourself or your organization into breaking down available data by category is to look at internal versus external data.
External Data
External data takes a look at your organization relative to the industry, market, or global positioning as a whole by collecting data from competitors, industry reports, customers, and more. Some examples of external data include social listening through social media platforms, or economic indicators. These data points give an organization a better understanding of what their target market is looking for. Additionally, economic factors and industry trends can help a company with risk mitigation and forecasting.
Internal Data
Typically, when people consider impactful data to collect for the supply chain, internal data encompasses all of the data generated within a company. This can include HR, sales, manufacturing, financial data, and more. An important feature to distinguish internal from external data is that it cannot be accessed by anyone outside of the company without express permission from the organization.
When analyzing the effectiveness of current processes and procedures, internal data will likely be the most useful tool. Thus, you will typically see a higher volume of internal data used in data-driven decision making within the supply chain, though both are utilized for the most holistic approach.
Data-Driven Technologies
Another factor you need to consider when approaching data-driven decisions is the type, or types of technologies you would like to implement in order to make sense of your data. This is an incredibly important step in the process, as the data-processing technology you choose should be selected based on your desired outcomes.
Artificial Intelligence & Machine Learning
Artificial intelligence is becoming a major buzzword, in biopharmaceuticals and beyond. Although it is a developing technology, artificial intelligence (AI) and machine learning (ML) have already proven effective for automating and optimizing a variety of tasks in supply chain management.
One frequently discussed use of AI in the supply chain is route optimization. When we consider what the fastest route to a destination is in our day-to-day life, we are likely to simply pick the fastest route from point A to B in our GPS. However, in the supply chain, some things have to stop at multiple locations in order to reach their destination. Companies utilize AI and ML to analyze things like traffic patterns, upcoming weather, and historical data to optimize route planning and logistics before items even leave the facility.
AI can also be effective in the warehouse. It can help to automate more routine tasks like packing and sorting, giving warehouse employees more time to focus on other tasks. Having AI pack and sort can also improve the accuracy of orders packed by removing the possibility of human error.
To stay ahead of constantly shifting industry trends, it will be imperative that organizations keep a pulse on the advancements of AI technologies, and continuously find new ways to implement them into their process.
Predictive Analytics
Predictive analytics are a proactive tool to implement in the supply chain. It can help improve efficiency and accuracy in demand forecasting, and help an organization better understand what their competitors are likely to do in the future. The technology analyzes current market trends, historical internal and external data, point-of-sale data, and more to provide accurate predictions.
It is important to note that although predictive analytics often provides accurate results, it is still helpful to have a data specialist look through provided predictions. Data driven decision making is informed decision making, but we still have not developed tools that can accurately see the future. Having a trained professional interpret the findings of predictive analytics provides an extra layer of risk management, as they can use personal experience to determine which predictions they agree or disagree with.
Inventory Optimization
Inventory management is an ongoing task at any organization that deals with tangible goods. Having the proper inventory ensures customers get their orders on time, and processes in the company run smoothly. Often overlooked, inventory management even extends to things like resources for facilities to keep employees comfortable and the space clean.
All of these factors indicate the importance of inventory management for keeping a business running smoothly. However, as it is a continuously ongoing process, sometimes things get overlooked. This could mean an individual accidentally forgets to order something, or demand predictions are incorrect, and a company ends up with extensive backstock of a product. This costs an organization money from goods purchased, and for inventory storage.
To negate these challenges, many organizations are starting to implement cloud-based inventory optimization software. This implementation can lead to benefits like reduction of unpredicted stock levels, better demand forecasting for more strategic purchasing, and reduction in time spent taking inventory on a semi-annual basis.
One example of an organization that implemented a machine learning approach to their inventory optimization is Deloitte, who reported a “15% reduction in unproductive stock levels at DCs” after implementation.
Inventory management is crucial, especially in industries dealing with sensitive materials like biopharmaceuticals, where precision and reliability are paramount. At High Purity New England (HPNE), we understand the complexities of managing such inventories, and have extensive material compatibility resources to make sure we get the solutions you need to you when you need them. We strive to eliminate your supply chain stress by eliminating critical bottlenecks in common biopharmaceutical processes.
Challenges and Considerations
As impactful as data-driven decision making can be, there are a variety of challenges and pitfalls that organizations can face throughout the process. Firstly, it is important to understand that not all data is good data. Multiple issues with a data set can impact your results, such as duplicate values, incorrect data entry, or missing values.
As data collection is typically done by employees, or with the guidance of employees, there is always a possibility of human error. Incorrectly entered data can skew results, or can even prevent the software you are using from producing a finding at all. Establishing clear data governance policies can ensure data quality and consistency across the organization.
Secondly, many organizations face the challenge of using too much, or too little data. The proper data set for a given situation should provide all data for areas of interest, without branching out into data that would not be relevant to solving the problem you are aiming to get answers to.
For example, when solving a manufacturing issue with data-driven decision making, you likely will not require data from HR. However, if the issue is related to production output, HR data related to employee training and satisfaction could be relevant.
Extending a data pool beyond what is necessary can lead to multiple deficiencies in the data-driven decision process, including increased processing time, difficulty identifying meaningful patterns, and risk of overfitting models.
In the same school of thought, companies should be careful not to use data-driven decision making as a tool to build confidence around decisions that have already been made. Approaching a data set with a biased opinion can lead to a biased result. It is critical to enter data as a neutral party, letting the data speak for itself to produce the best result.
Of course, any discussion of data would be incomplete without discussing the security risks associated. When dealing with sensitive data, security and privacy are paramount. If you are manipulating sensitive data for data analysis during your decision making process, ensure that you are using reputable software, and are on a safe network. Consult your company for more details on their data security protocols and recommendations.
Overall Benefits of Data-Driven Decisions in Your Supply Chain
Incorporating data-driven decision-making into a supply chain provides significant benefits, primarily by enhancing visibility and optimizing operations. By leveraging data analytics, businesses can achieve more accurate demand forecasting, leading to improved inventory management and reduced costs. Real-time insights enable proactive risk mitigation, allowing for quicker responses to disruptions. Ultimately, this approach fosters a more agile, efficient, and resilient supply chain, contributing to increased profitability and customer satisfaction.
Upgrade Your Supply Chain with HPConnexx™
At HPNE, we are constantly working to ensure our customers receive top-tier solutions and services. We believe our deep understanding of the intricacies of manufacturing and supplying critical components makes us a valuable partner in navigating the complexities of data-driven optimization. Our multiple facilities ensure business continuity, regardless of industry disruption.
Additionally, we provide numerous avenues for process improvement that do not involve major technological pivots or huge investments in technological software and other equipment with our significant product portfolio.
For better safety and efficiency, explore our range of MasterMover Electric Tugs. These allow a single-operator to transport heavy bioprocessing equipment across your facility. Each tug has a variety of safety features designed to prevent avoidable workplace accidents.
Obtain flexibility and scalability in your process with our HPConnexx™ Single-Use Assemblies. By maintaining a smooth supply chain flow, these assemblies enable more accurate data collection. With over two decades of experience designing and manufacturing assemblies, our expert team has the knowledge and confidence to produce the perfect assembly for your unique needs every time. Our assemblies are brand agnostic, which eliminates the risk of supply chain bottlenecks caused by part brand dependencies.
Whether off the shelf or custom made, at High Purity New England, we are confident we can scale with you as your process grows with HPConnexx™. Interested in going beyond implementing data-driven decision making, and upgrading your process equipment? Talk to one of our experts to learn more.
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About HPNE
As the industry needs grow, High Purity New England, Inc. continues to supply the biopharmaceutical industry with a range of innovative products, from drug discovery and development to fill-finish, including their flagship product, custom single-use assemblies, as well as pumps, sensors, bioreactor systems, storage and handling solutions and other single-use solutions. Along with their own manufactured products for the global market, they are also a distributor for more than 18 brands in North America.
