Guest Post: Data Quality Matters - Unravelling the Impact of Bad Data on Inventory Expiration

By CFIN Newsdesk posted 08-08-2023 08:00


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By Hugo Fuentes, CFIN member and CEO of The Owl Solutions 


In the fast-paced world of food manufacturing, inventory expiration poses a persistent challenge for many companies. While it may be easy to blame inventory planners or analysts for these issues, the root cause often lies in outdated systems and processes that professionals are forced to work with. The harsh reality is that most manufacturers rely on a patchwork of three to five different IT systems to manage their supply chain, resulting in fragmented data and information silos that hinder productivity. 


Imagine the inventory planner juggling multiple ERPs, warehouse management systems, Power BI, and more, all to extract the necessary data into Excel spreadsheets for reports. This manual process not only consumes valuable time but also diverts attention from critical tasks such as coordinating action items and conducting root cause analyses. 


Supply chains are inherently complex, and their intricacies are only expected to grow in the future. Yet, persisting with outdated systems is similar to using a typewriter in an era of advanced word processors – it impedes efficiency, accuracy, and adaptability, leaving organizations vulnerable to costly inventory expiration. 


But how can you determine if your organization is suffering from poor data quality? Fortunately, there are several factors that can serve as red flags for poor data quality. 


Factors that contribute to poor data quality 


1. Manual data entry 

Some food companies still rely on manual data entry processes, which are prone to human errors. Inaccurate or incomplete data can result from typos, misinterpretation, or oversight during manual input, leading to poor data quality. 


2. Fragmented data sources 

Food companies often have multiple systems and databases that store different types of data, such as sales, inventory, and procurement. When these systems are not integrated or communicate poorly with each other, it can result in fragmented data sources. Inconsistent or disconnected data makes it challenging to have a holistic view of the business and can lead to poor data quality. 


3. Lack of data governance 

Data governance refers to the policies, processes, and procedures in place to ensure data integrity and quality. Without proper data governance practices, food companies may struggle to establish standardized data definitions, data validation rules, and data maintenance protocols. This lack of governance can result in data inconsistencies and inaccuracies. 


4. Complex supply chains 

Food companies often have complex supply chains involving multiple stakeholders, such as suppliers, distributors, and retailers. The flow of data across these entities can be fragmented and inconsistent, making it difficult to maintain accurate and up-to-date information. Issues such as delays in data transmission, incompatible data formats, or data discrepancies between different parties can contribute to poor data quality. 


5. Legacy systems 

Some food companies may still rely on outdated or legacy systems that were implemented before modern data management practices became prevalent. These systems may lack the capabilities to effectively capture, store, and analyze data, leading to data quality issues. Upgrading or migrating to more advanced systems can be a costly and time-consuming process, deterring some companies from improving their data quality. 


6. Lack of data expertise 

Managing data effectively requires specialized knowledge and skills. Food companies that do not have dedicated data professionals or data management expertise may struggle to implement robust data quality measures. Without the necessary expertise, it becomes challenging to identify and address data quality issues, resulting in poor data across the organization. 


Addressing the challenges posed by data quality in inventory management is crucial for food manufacturers. By investing in robust data management practices or partnering with third-party, full-service supply chain analytics providers, companies can achieve increased visibility into their inventory. This enhanced visibility enables inventory teams to spend less time vetting data and more time focusing on reviewing insights, conducting root cause analyses, and coordinating action items. 


The benefits of clear visibility into inventory extend beyond the companys bottom line. It plays a vital role in reducing the number of expired products, which not only impacts financial performance but also has sustainability implications for the food and beverage industry. A significant amount of food waste in the supply chain ends up in landfills, contributing to the emission of methanea greenhouse gas 25 times more potent than carbon dioxide (U.S. Environmental Protection Agency, 2022). Read more about how data analytics can reduce supply chain food waste 


By effectively reducing inventory expiration, companies can achieve multiple benefits. They improve their bottom line, enhance supply chain efficiency, and contribute to a more sustainable future for our planet. Taking proactive steps towards data-driven inventory management is not just a business imperative, but also an ethical responsibility in the face of environmental challenges. 


Recognizing the impact of bad data on inventory expiration and taking decisive action to improve data quality will unlock opportunities for better decision making, reduced waste, and a more resilient and environmentally conscious supply chain. 


Hugo Fuentes is the CEO of The Owl Solutions, a prominent supply chain analytics company committed to aiding mid-sized manufacturers in diminishing financial, environmental, and productivity waste through data-driven insights and actionable strategies.  If you are interested in learning more about how The Owl can help you reduce inventory waste through our inventory expiration module, please visit 

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