data analysis

1. Core data sources

The foundation of RFID data analysis is structured data collected throughout the entire process, with the main sources including:

Basic data of assets/items: static information such as unique ID, category, specification, value, and owner/department written through tags.

Dynamic flow data: Key node data recorded by the reader/writer, such as inbound/outbound time, movement path, borrower/lender, usage frequency, and maintenance records.

• Environment related data: Additional data in certain scenarios, such as drug temperature and humidity in medical settings and tool usage environment in industrial settings.

2. Core analysis direction and application scenarios

In different business scenarios, the focus of data analysis varies, ultimately serving the three major goals of 'cost reduction, efficiency improvement, and risk control':

(1) Inventory and Asset Efficiency Analysis

Core indicators: inventory turnover rate, proportion of stagnant assets, asset utilization rate, inventory variance rate.

Application example: In enterprise fixed asset management, by analyzing the 'asset usage frequency', it was found that out of 10 laptops in a certain department, 3 were used less than twice a month (stagnant assets), which can be transferred across departments to improve overall utilization; By analyzing the 'inventory turnover rate', identify a certain type of office furniture with slow turnover, and subsequently reduce the procurement volume to avoid backlog.

(2) Logistics and Supply Chain Optimization Analysis

Core indicators: Node circulation duration, abnormal retention rate, smuggling rate, and loss rate.

Application example: In the clothing retail scenario, analyzing the replenishment circulation time of 'store warehouse', it was found that the average time from a warehouse in a certain area to a store was 5 days (industry average 3 days). Tracing the data to 'sorting stage delay', optimizing the sorting process shortened it to 3 days; In the anti-counterfeiting traceability scenario, through the analysis of 'regional scanning code distribution', it is identified that products that should have been sold in East China appear in North China, quickly locating the source of counterfeit goods.

(3) Business Process Compliance and Risk Control Analysis

Core indicators: compliance rate of operations, number of abnormal operations, asset loss rate, medical error rate.

Application example: In the smart healthcare scenario, analyzing the 'patient drug' verification data, it was found that a certain department directly administered medication without scanning wristbands twice a month (compliance rate of 98%). Targeted training was strengthened to increase compliance rate to 100%; In the tool management scenario, through the analysis of 'return timeout rate', it was found that a certain team's tool return timeout accounted for 15%. A timeout warning mechanism was developed to reduce the risk of tool loss.

(4) User/Consumer Behavior Analysis (Retail/Service Scenarios)

Core indicators: try on rate, transaction conversion rate, member repurchase association rate, popular product circulation path.

Application example: In a clothing store scenario, using RFID recorded 'try on purchase' data, it was found that the try on rate of a certain shirt reached 30% but the transaction rate was only 5%. After optimizing pricing or display based on user feedback, the transaction rate increased to 12%; Through the analysis of 'member purchase product association', it was found that 60% of members who buy dresses will also buy scarves. A combination recommendation was launched to increase the average order value.

3. Core values

From 'passive recording' to 'active optimization': no longer relying solely on data to record asset/item status, but through analysis to discover management loopholes (such as stagnant assets and process delays), driving business optimization.

From 'experience based decision-making' to 'data-driven': replacing 'purchasing based on intuition'  'relying on manual judgment', using quantitative indicators such as turnover and utilization to guide procurement, inventory, and personnel management.

From 'single point monitoring' to 'global visibility': breaking down departmental data barriers, allowing managers to gain a global understanding of business dynamics through analysis reports such as logistics node duration charts and asset utilization heat maps.