Challenges in Leveraging Supply Chain Data for Decision-Making
Updated: Oct 16
Getting started with cleaning up your organization's data is no easy task. Often Manufacturers face similar challenges – data resides in multiple systems, there is limited visibility into who has access to the data and users are unsure if it’s clean. What does this result in for supply chain folks?
Extracting data from multiple systems, and spending 80% of their time manually vetting the data to make sure it’s clean. So, what’s the problem? That 80% of time spent on data collection and harmonization results in only 20% of time spent on driving actions.
This is why problems with inventory expiration, supplier reliability, inaccurate forecasting etc occur. Often too much time was spent on the pre-work and not enough time spent on tracking and eliminating some of these challenges.
Let’s take a deep dive into what some of the most common challenges around supply chain data are today.
1. Data Quality: Ensuring data accuracy, consistency, and relevancy can be challenging due to factors like data entry errors, incomplete records, and data integration 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 for your organization 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 big challenge we see amongst most of our customer base and prospects within the market. 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 need to 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.