In the supply chain realm, accurate information empowers companies to effectively manage their operations. Through data analysis, professionals pinpoint effective strategies and identify inefficiencies that hinder progress. Armed with this tool, organizations achieve higher efficiency, flexibility, and competitiveness in today’s supply chain landscape.

Learning the fundamentals is the first step towards harnessing the enduring benefits of data. It all starts from data collection.

A well-digitalized modern supply chain will produce some type of data every step of the way. Industry 4.0 technologies make it easy to see this data in real time, and later use it for optimizing operations, improving efficiency, and ensuring effective decision-making.

Let’s take a look at the types of data that can be collected at various points throughout the supply chain.

Procurement

 
The procurement stage of the supply chain involves sourcing, purchasing, and acquiring goods or services from external suppliers. It includes activities such as supplier selection, negotiation, contract management, and ensuring timely delivery of materials or services to meet organizational needs. Zluri describes seven steps of the procurement cycle within the software procurement framework, but these same steps can be stretched to the procurement of material goods as well.

Each step can produce valuable data. For example:

• Supplier data. Data is collected regarding supplier performance, quality of materials or products supplied, lead times, and pricing. This can involve tracking metrics such as on-time deliveries, defect rates, and supplier responsiveness.
• Contract data. Information on contract terms, negotiations, and compliance data is collected to ensure contractual obligations are met.

Production

 
This stage involves the actual creation of the items that will later embark on their supply chain journey, or, in technical terms, the conversion of raw materials into finished products through manufacturing processes. It includes activities such as assembly, fabrication, testing, and quality assurance to produce goods that meet customer specifications and demand.

• Manufacturing Process produces a ton of data, gathered from various sensors, machines, and systems on the shop floor. The companies who distribute their own goods can use this data to optimize their planning. Production schedules, capacity utilization, and machine performance all inform efficient resource allocation and scheduling to meet demand.
• Quality Control. Inspection data, testing results, and feedback loops from quality assurance processes provide insights into product quality and potential improvements.

Inventory Management

 
Warehouses and storages are ripe with data. Every single item listed on the shelves has a paper trail that tells where it came from and where it is going, but that’s just the tip of the iceberg. Inventory data is the jumping-off point for predictive and prescriptive analytics. Here’s how it is gathered:

• Inventory levels, reorder points, and stock movements are collected through inventory management systems. This helps in maintaining optimal inventory levels and avoiding stockouts or overstock situations.
• Data gathered from sales, historical trends, market analysis, and customer feedback is used to forecast demand accurately.

Distribution And Delivery

 
This stage addresses the time when the stored goods are getting transported to their final destination. This could be a network of large retailers receiving bulk orders from a wholesaler (distribution) or a single end-user getting their post in the mail (delivery). Either option will give the supply chain company lots of valuable information.

• Warehousing. Data on inventory in warehouses, storage conditions, picking rates, and order fulfillment metrics are collected to streamline warehouse operations.
• Transportation. Data related to transportation routes, carrier performance, shipping times, fuel efficiency, and vehicle tracking is gathered to optimize logistics and reduce costs.
• Last Mile Delivery. Data on delivery schedules, routes, delivery times, customer preferences, and delivery confirmation is collected to ensure timely and accurate delivery to end customers.
• Customer Feedback: Post-delivery feedback and returns data provide insights into customer satisfaction, product quality issues, and areas for improvement in the delivery process.

Inventory Levels

 
This term refers to the detailed information regarding the quantity, location, and status of goods or materials held by a company at any given time. Inventory levels data typically includes real-time updates on stock levels in warehouses or distribution centers, reorder points based on demand forecasts, lead times for replenishment, and storage conditions. It is often collected and managed through inventory management systems or enterprise resource planning (ERP) software.

Order Volumes

 
The number of orders received or processed within a specific timeframe is the order volume. This data encompasses the quantity of products ordered, order dates, customer locations, and order processing times. It provides insights into demand patterns, seasonal fluctuations, and customer preferences. Order volumes data helps businesses forecast future demand and plan for it accordingly.

Supplier Performance

 
This metric is more external; rather than analyzing your own company, supplier performance data refers to the metrics and information collected to evaluate the reliability of your suppliers. It includes KPIs like delivery times, quality of goods, responsiveness to issues or changes, and overall reliability. This data can become the deciding factor in whether you wish to continue the relationship with a specific supplier or look for a different partner. Supplier performance data is often integrated into supplier dashboards within supply chain management systems.

Transportation Metrics

 
Transportation is a huge part of supply chain that produces its own host of sub-categories of valuable data. It is no surprise: optimizing transportation through data analytics means investing in faster deliveries, safer routes, and lower fuel intake.

• On-time Performance. Percentage of shipments delivered on time compared to scheduled delivery dates.
• Transit Time. Actual time taken for shipments to move from origin to destination, measured against expected transit times.
• Transportation Costs. Fuel costs, carrier fees, handling charges, and transportation management system (TMS) costs.
• Carrier Performance. Carrier reliability, frequency of delays, and customer service responsiveness.

Innovecs has previously dissected how the access to descriptive analytics is a window into predictive and descriptive analysis, which helps to forecast the future operations and optimize them using the knowledge of the past. This is data’s profound influence on supply chain decision-making. It takes many forms.

Data from historical sales, market trends, and customer preferences allows us to accurately predict demand. This helps reduce stockouts and improve customer satisfaction. Along with efficient inventory management, it empowers organizations to maintain optimal stock levels and ensure products are available when needed.

Timely data on raw material availability, production capacity, and machine downtime enables efficient production planning. Real-time data on transportation routes supports efficient logistics management, and the information from customer orders, preferences, and feedback enables personalized customer service.

Adapting To Disruptions And Changes

 
The most powerful thing data can do for modern day logistics is the ability to predict potential crises and warn the company about them in advance, providing enough leeway to adjust to the situation with minimal losses. This can look like:

• Early Detection. Identify potential disruptions before they occur by analyzing historical data, market trends, and external factors.
• Risk Mitigation. Mitigate risks associated with inventory shortages, supplier delays, and market fluctuations by making immediate adjustments.
• Cost Savings. Reduce costs associated with expedited shipping, excess inventory, and downtime.
• Enhanced Customer Service. Meet customer expectations despite challenges.
• Competitive Advantage. Organizations that leverage predictive analytics and real-time data effectively can gain a competitive edge by being more responsive to market changes and customer demands.

A company’s plan to gather and analyze data is known as its data management strategy. There are many metrics to measure whether your strategy is a good fit, but, on the base level, to be beneficial it should provide you these three things:

• Accuracy. A robust data management strategy in the supply chain ensures data accuracy by establishing clear guidelines for data collection, storage, and validation processes.
• Consistency. Standardized formats, definitions, and integration protocols across systems ensure that the data you receive remains consistent and coherent.
• Accessibility. Centralize your data repositories and utilize cloud-based solutions to make the data easy to access. To make it easy to understand, establish user-friendly interfaces and data visualization tools.

The Challenges Of Data Silos

 
On the subject of accessibility, “data silos” occur when your data is only accessible to a part of your team, or only one team in your company, but not the others. This phenomenon is a remnant of the older, less digitalized supply chain industry, where having siloed teams with no way to share information between them was more common, though still inconvenient. This isolation of certain bits of data can cause considerable problems:

• Hindering of collaboration and comprehensive decision-making.
• Duplicated data entries across different silos can lead to inconsistencies and inefficiencies in data management.
• Integrating data from disparate silos is complex and time-consuming, making it difficult to achieve a unified view of operations.
• Siloed data may lack validation or standardization, resulting in inaccuracies and unreliable analytics.
• Silos can stifle innovation as insights derived from isolated data sources may not reflect the complete picture or potential opportunities.
• Siloed data can pose security vulnerabilities and compliance challenges due to inconsistent data governance and access controls.

If you already have data silos in your company, there is no easy fix: you will have to invest time and money into consolidating all of the data into a unified system that is user friendly and ideally cloud-based. A modern management system can also be a great starting off point for a new company that wants to avoid data silos from the get-go.

Having a Warehouse Management System (WMS) is not only great for data collection and integration; it can also be a fast track for your company’s growth. WMS offers a spectrum of features that will better manage your data and benefit your bottom line.

• Inventory Visibility. Real-time visibility into inventory levels, locations, and movements within warehouses is critical for accurate inventory management and demand forecasting.
• Order Accuracy. Ensure orders are picked, packed, and shipped accurately by guiding warehouse operations with barcode scanning and automated workflows.
• Optimized Space Utilization. WMS helps to make the most of your warehouse space by organizing inventory based on storage characteristics and turnover rates.
• Efficient Operations. WMS offers digital task prioritization, resource allocation, and performance monitoring. This enhances operational efficiency and reduces cycle times.
• Data Integration. WMS integrates with other supply chain systems (e.g., ERP, TMS) to provide seamless data flow across the supply chain network; the opposite of the data silos problem.

Data-driven decision-making has become indispensable in modern supply chain management, transforming how companies optimize operations and adapt to market dynamics.

At every stage, from procurement to distribution, data fuels insights that enhance efficiency, resilience, and customer satisfaction. Technologies like Warehouse Management Systems (WMS) are pivotal in this evolution, enabling real-time data integration and operational optimization. Strategic use of data not only streamlines processes and reduces costs but also increases competitiveness. As data analytics continues to advance, its role in supply chains will deepen, setting new benchmarks and driving sustainable growth.

Innovecs has helped many international partners to make their supply chains more data-driven through the use of cutting-edge technology and expert consultation. If your business could use a better data management strategy or would benefit from WMS optimization, reach out to us and let’s talk.