Data Analysis: What is Your Data Trying to Tell You?

The drive for visibility across the supply chain is based on the assumption of accurate data at every step. However, as the flood of data collected in the supply chain grows, it's becoming more difficult to manage and analyze it for strategic insights. Any inaccuracies could lead to conclusions that result in misguided strategic and tactical decisions.

Unfortunately, many organizations still operate in siloed environments with data collected and housed in fragments across different departments, such as location-based procurement teams. Organizations that expand their data management and data analysis capabilities often do so without verifying the accuracy and depth of the data. There may be a mismatch between what products have been sold, what's been shipped, and what's been returned. What's in the database may not reflect the reality on the inventory shelves. Or product data may have incorrect dimensions, leading to false assumptions about warehouse space and shipping weights.

The results of initiatives such as inventory optimization and carrier compliance could be skewed from low-quality data, leading to decisions that could reduce efficiency in your supply chain.

Are you making decisions driven by inaccurate data?

Data Cleaning

Analysis Drives Decisions, Start with Better Data

Good decisions start with clean, accurate data. Data input via manual processes or information that may require on-the-spot decision-making tends to have lower accuracy than data collected through technology. Back-end systems that are incompatible may require redundant inputs, leading to duplication and mistakes.

 

As the flood of data grows, it's vital to close the loop - collection is not enough. The information must be converted to actionable insights to deliver value across the supply chain. Clean data is simply information that reflects a high degree of confidence in its accuracy, stored in the correct, usable format.

Confirm Accuracy, End Goal before Analysis

Data KPI

Identify end uses. Decide which challenges you want the data to help solve to decide which data to collect.

Implement standards. Develop standards for collecting and manage data such as formats and keywords.

Focus on the most relevant information. Understand the inputs that are most critical to your business

Convert to actionable insights. Focus on data for KPIs and decision-making.

With accurate, thorough data, your organization can uncover hidden opportunities to optimize your processes. Optimization software and simulation tools can reveal options that drive structural changes to deliver the highest level of value to the customer. With increasing customer expectations for improved visibility into product locations and expected delivery times, data accuracy has never been more essential.

Objective Data View Accelerates Performance

Keep in mind that data accuracy is a marathon, not a sprint. It requires systems and policies in place over the long term. Work with an Enterprise Logistics Provider with deep technical expertise in data analysis and cleaning processes to improve current data and set up improved processes going forward. A trusted third party can help develop an objective view of your data landscape, including visibility down to the SKU level to generate strategic insights and shape demand forecasting. 

For more insights into your data accuracy journey, read our resource guide: AI, Blockchain, Machine Learning: Is Your Data Ready?

Is Your Data Ready

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