Data is a powerful tool for optimizing your supply chain. But even the smartest analysts can make supply chain data mistakes. In this blog post, Danny Wang walks you through the 5 most common pitfalls that can derail your data-driven insights.

From overlooking outliers to neglecting frontline feedback, these mistakes can lead to costly errors and suboptimal decisions. Let’s dive in and ensure your data is working for you, not against you.

Supply Chain Data Analysis Mistakes

In today’s data-driven landscape, supply chain and operations teams are increasingly expected to optimize performance through vast datasets. From the movement and storage of materials to inventory management and order fulfillment, data informs almost every decision. While the availability of this data offers immense opportunities, it also presents significant challenges. Errors when handling or interpreting data can lead to low quality conclusions and suboptimal decision-making. Below are the 5 most common pitfalls when analyzing supply chain and operations data. 

1. Dismissing Outliers Without Context

A frequent mistake in data analysis is the automatic removal of statistical outliers. While outliers can skew averages and lead to misleading conclusions, they also provide crucial insights into underlying issues. In supply chain operations, outliers often reflect exceptional events such as demand spikes, supply disruptions, or process errors. Instead of discarding them immediately, it’s crucial to investigate the root causes. 

For instance, sudden spikes in inventory receipts might be dismissed as input errors. However, further analysis could reveal underlying issues such as: 

• Suboptimal procurement processes and order cadence 

• Over-reliance on volatile sectors like container freight 

• Preparation for the launch of new product lines or major marketing campaigns 

Key takeaway: Outliers can uncover inefficiencies or disruptions and should be investigated before removal – they may reveal opportunities for improvement. 

2. Using Truncated Data Sets

Another common error arises from working with incomplete or misaligned data sets. Truncated data can distort trends and introduce noise, undermining the reliability of insights. This issue frequently occurs when mismatches exist between data collection and reporting periods. 

Consider the challenge of generating monthly reports from weekly data for a quarterly review. To create an accurate picture of monthly trends, you will likely have to do one of the following: 

• Obtain daily data for more granular insights (if available) 

• Include an additional period before and after the desired timeframe to account for partial weeks 

Key takeaway: Understand your data’s collection cadence and adjust your analysis strategy accordingly. 

3. Overlooking “Common Sense” Checks

Large datasets often contain inconsistencies and errors that are difficult to detect manually. Setting up basic “common sense” checkpoints can help quickly identify critical errors.  

Say you work in a DC that primarily distributes home goods to retailers. You have a large variety of SKUs and are looking to purchase MHEs to store them. You have a fairly robust route-planning system so you know your weight per pallet is fairly reliable. However, your cubiscan broke a few years ago and it has not been replaced. As a result, volumetric data for new SKUs and product lines is unreliable. While you are confident that these are directionally correct, you know there are still quite a few SKUs where the dimensions are completely incorrect and may impact your purchase.  

A quick way to spot errors is to cross-check SKU density and highlight potential discrepancies. Consider applying these flags:  

• SKUs with densities unusually greater than steel or less than a feather 

• Unexpected relative density between product classes (e.g., ceramic items should be denser than plastic storage bins) 

Key takeaway: Implement clear benchmarks to quickly spot impactful errors in large datasets and ensure data integrity.

4. Precision Mismatch

Achieving the right level of precision is a delicate balance in data analysis. Incorrect precision can lead to an overfit or oversimplified model.  

Models with excess complexity can overemphasize noise and anomalies. While the resulting analysis may seem precise, insights are less accurate and obscure true underlying trends. On the other hand, oversimplifying your models can mask critical variations that are essential for localized decision-making. 

For instance, in facility design, an overly precise model might over-allocate space based on short-term data spikes, leading to inefficiencies in picking and capacity management. As capacity changes often require stepwise investments, this can lead to premature capital deployment and incur unnecessary opportunity costs. 

Conversely, simplifying inventory models across multiple warehouses can obscure important details. While useful in certain circumstances, this analysis loses visibility on whether nodes are struggling and whether they are experiencing inventory shortages or excesses. Losing this granularity can result in poor allocation decisions, missed orders, and increased holding costs.  

Key takeaway: Strive for simplicity without sacrificing accuracy. Tailor your model’s granularity to the operational context to maintain actionable insights.

5. Neglecting Insights from the Floor

Data analysis is essential for optimizing supply chain performance, it doesn’t capture every operational nuance. Failing to engage with front-line employees can result in misinterpretations and missed opportunities to identify root causes. Floor workers often have valuable insights into equipment issues, bottlenecks, or layout inefficiencies that may not be immediately evident from the data. 

Firsthand insight can also be a useful indicator during data validation. For example, when converting unit data to pallets with an unreliable item master, a quick conversation with the floor manager can help benchmark overall volume and distribution. 

Key takeaway: Regularly communicate with front-line employees to gather qualitative insights that complement and contextualize the data. Their observations can provide critical validation or challenge assumptions, giving you a fuller understanding of operational performance. 

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Supply Chain Data Mistakes – Recap

Data analysis holds vast opportunities to boost efficiency, cut costs, and enhance decision-making. However, realizing these benefits requires a careful approach. Misinterpretation, incomplete data, or incorrect application can undermine your efforts and lead to missed opportunities. By balancing technical rigor with operational insights, you can leverage data to drive meaningful improvements and achieve strategic goals. 

Does this apply to you and your business? Find out more by reaching out to the LIDD team today.  

Supply Chain Optimization