It’s common for manufacturers to face supply chain data challenges that hinder decision making.
We understand initiating data cleanup is challenging. Manufacturers encounter issues with scattered data, limited visibility, and uncertainty about its cleanliness.
For supply chain professionals, the problem is spending 80% of their time manually verifying extracted data. This is typically a result of pulling data from multiple sources. Consequently, only 20% of time is allocated for actionable tasks.
Issues like inventory expiration and unreliable suppliers arise when too much time is spent on pre-work, neglecting effective tracking.
Explore today’s common challenges in supply chain data.
1. Data Quality
Ensuring data accuracy, consistency, and relevancy can be challenging. Several factors like data entry errors, incomplete records, and data integration provide difficulties. It’s key to have a clear understanding about where certain data resides, who is inputting the information and how frequently. Establishing a common data language and framework will increase confidence in the data you are reviewing.
2. Data Silos
Data might be scattered across different systems and departments, making it challenging to create a holistic view for decision-making. This is a common challenge we see amongst manufacturers. With multiple ERP, supply chain planning, and warehouse management systems in place, it’s difficult to access the right information in a timely manner.
3. Data Privacy and Security
With the increasing emphasis on data privacy regulations, organizations need to ensure that data usage complies with legal requirements and protects sensitive information.
4. Complexity
Analyzing large volumes of data can be complex and requires skilled personnel and sophisticated tools. Companies that build their own internal supply chain solutions should be equipped with an expert IT team, and cross collaborate with supply chain leaders. For a much quicker result, check out how The Owl can pool all your data into one central location in the cloud within a matter of weeks.
5. Bias and Interpretation
Even with accurate data, biases in data collection or analysis can lead to skewed insights. Additionally, interpreting data correctly is crucial for making informed decisions.
To summarize, data poses a challenge for many manufacturers when it comes to effective decision-making. In order to make real progress and truly leverage your organization’s data, take a close look at these elements and monitor them closely. Start small if needed but make sure to align with the strategic goals of the business as you move forward on your journey. Leaving data silos behind and replacing them with an integrated platform will be key to success!
Finally, no matter what changes you decide to make, it is essential to increase visibility across all facets of the operation by continuously revisiting current state processes. With these steps in mind, determine what you need to do in order to properly apply your findings when making decisions for long-term growth.
So don’t wait!
Take action today: Learn how we can help improve your data strategy for better insights into decision-making.