Lean operation model of clothing retail stores from the perspective of data empowerment

Abstract

The present application relates to the technical field of retail management, and particularly refers to a lean operation model for clothing retail stores from the perspective of data empowerment. The model comprises a four-dimensional health degree evaluation module, a data report generation module, a store manager interview consensus module and a closed-loop execution review module. The four-dimensional health degree evaluation module constructs a mathematical model based on four dimensions of sales efficiency, human efficiency quality, member value and product efficiency and their subordinate indicators, and outputs an overall health degree score OHS to realize quality diagnosis of store operation. The data report generation module generates five key business reports to convert raw data into visual insights. The store manager interview consensus module realizes the integration of data and experience through a structured eight-step process and generates a SMART rectification plan. The closed-loop execution review module realizes the PDCA cycle through an agile review mechanism. The present application solves problems such as low operation efficiency, decision-making relying on experience and insufficient rectification execution of clothing retail stores through the deep integration of data-driven and lean operation.

Description

Technical Field

[0001] This invention relates to the field of retail management technology, and in particular to a lean operation model for apparel retail stores from a data-enabled perspective. Background Technology

[0002] The apparel retail industry traditionally relies on an extensive management model based on experience. While companies generally possess sales, membership, and financial data, this data is scattered across various systems, failing to be effectively transformed into actionable insights for frontline operations. Some research has proposed data-driven decision-making concepts, but these often focus on macro-level ideas or specific technological applications (such as AI recommendation systems), lacking comprehensive and implementable lean operational solutions for individual stores.

[0003] Furthermore, existing store management methods have the following shortcomings: First, they rely on outcome-based indicators (such as sales revenue) and lack monitoring of process indicators, leading to delayed problem detection; second, communication between store managers and operations supervisors is often based on subjective experience, making it difficult to reach a consensus; and third, rectification plans lack structure and quantifiability, resulting in insufficient execution. Therefore, there is an urgent need for a lean operations model that integrates data diagnosis, consensus building, precise execution, and agile review. Summary of the Invention

[0004] To address the aforementioned issues, this invention provides a lean operation model for apparel retail stores from a data-driven perspective. Through the deep integration of data-driven approaches and lean operations, it enables quantitative diagnosis of store performance, accurate identification of problems, efficient execution of rectification measures, and continuous optimization.

[0005] The technical solution of the present invention is as follows: A data-driven lean operation model for apparel retail stores includes a four-dimensional health assessment module, a data report generation module, a store manager consensus-building module, and a closed-loop execution review module. Among these: The four-dimensional health assessment module constructs a quantitative assessment system from four dimensions: sales efficiency, human resource efficiency, member value, and product efficiency. Based on a mathematical model, it calculates the overall health score (OHS) to achieve objective diagnosis and early warning of abnormalities in the store's health. The data report generation module generates five key business reports (monthly sales trend report, process data report, member analysis report, employee sales data report, and mid-category sales report), transforming raw data into visual and actionable insights; The store manager interview consensus module operates through a structured eight-step process (preparation, start, self-inspection, diagnosis, identification, creative solution, consensus, and review) to bridge the gap between data and experience and form a SMART rectification plan. The closed-loop execution review module ensures the effective execution and continuous iteration of the rectification plan through weekly reviews, remote spot checks, and the PDCA cycle.

[0006] Furthermore, the model is built on the DCER framework and includes four stages: Diagnose, Consensus, Execute, and Review, forming a closed-loop management system from data to action.

[0007] The beneficial effects of this invention are as follows: By using a four-dimensional health model and OHS scores, we can achieve quantitative assessment and early warning of abnormalities in store conditions, thereby improving the accuracy of decision-making. Five key business reports transform complex data into intuitive insights, supporting process-oriented management; By using the eight-step method of store manager interviews, data diagnosis is combined with front-line experience to form a consensus and stimulate team initiative. By using SMART planning and agile debriefing, we ensure that corrective measures are executable, quantifiable, and iterative, thus forming a closed loop of continuous improvement. Attached Figure Description

[0008] Figure 1 This is a monthly sales data table for stores with abnormal sales.

[0009] Figure 2 This is a process data table for abnormal stores.

[0010] Figure 3 This is a table showing the completion status of members at stores with abnormal records.

[0011] Figure 4 This is a report on abnormal sales data of store employees.

[0012] Figure 5 This is a table of the top ten best-selling clothing items in different categories of stores with abnormal sales.

[0013] Figure 6 This is a confirmation form for rectification of abnormal stores.

[0014] Figure 7 This is a weekly / monthly progress plan. Detailed Implementation

[0015] The present invention will now be described in detail with reference to the appendix and embodiments. This embodiment uses a branded clothing retail store as an example to apply the model of the present invention for operational optimization.

[0016] Step 1: Using core diagnostic tools: a four-dimensional health assessment model and quantitative system Moving beyond a singular focus on sales figures, we have developed a forward-looking, multi-dimensional, and comprehensive store health diagnostic model. Based on four fundamental principles, we have created a holistic sales performance evaluation model, comprising 4 major categories, 9 subcategories, and 17 tertiary indicators.

[0017] 1. Sales Efficiency Dimension (D1) This dimension includes two aspects: sales target achievement and business growth. It mainly includes three secondary indicators: monthly target achievement rate, year-on-year / month-on-month growth rate, and number of consecutive monthly anomalies.

[0018] 1) Target achievement rate, further subdivided into monthly completion rate and cumulative completion rate, which reflect the short-term and long-term performance respectively; 2) Growth quality: The dynamic trend of sales performance is systematically evaluated through two indicators: year-on-year growth rate and month-on-month growth rate. 3) Trend judgment: Based on the indicator of consecutive abnormal months, identify potential fluctuations and abnormalities in sales performance.

[0019] 2. Human Resource Efficiency Quality Dimension (D2) It primarily focuses on team and individual work efficiency and the quality of the sales process, with key metrics including: Uplink Rate (UPT), Average Transaction Value (ATV), Average Transaction Value (IPT), Sales Per Person (SE), Try-on Rate, and Conversion Rate.

[0020] 1) Team effectiveness is evaluated by comprehensively assessing the overall collaboration and output capabilities of the sales team through three indicators: cross-selling rate, average order value, and average order value. 2) Individual efficacy, including the contribution of top employees and the efficiency difference coefficient, is used to identify high-performing employees and analyze the distribution of efficiency within the team; 3) Process metrics: Monitor key behaviors and conversion efficiency in the sales process through trial rate and transaction conversion rate.

[0021] 3. Member Value Dimension (D3) This section measures the contribution, activity, and development potential of the membership system. Specific sub-measures include the following indicators: member sales share, member repurchase rate, dormant member reactivation rate, and number of new members developed.

[0022] 1) Member contribution: Quantify the contribution of the member group to the overall sales volume by measuring the percentage of sales generated by members and the average order value per member. 2) Member activity, including repurchase rate and dormant member activation rate, is used to measure members' continued willingness to purchase and activation effectiveness; 3) Membership growth: Dynamically monitor changes in membership size and health status through the number of new members and membership churn rate.

[0023] 4. Product Effectiveness Dimension (D4) This dimension emphasizes product management and category performance, including the following core indicators: SKU efficiency, sell-through rate, top 10 category share and changes, and new product performance.

[0024] 1) Category health: Assess the inventory turnover efficiency and sales performance of goods through SKU efficiency and sell-through rate; 2) Product category structure: Based on the proportion and fluctuation of the top 10 product categories, analyze the stability and changing trends of core product categories in sales; 3) New product performance: The market acceptance of newly launched products and their sales-driving effect are measured by the new product sell-out rate and the contribution of new products.

[0025] Step 2: Constructing a mathematical quantification model: To provide an objective assessment, an Overall Health Score (OHS) model was constructed: 1. Overall Model Framework Overall Health Score (OHS) OHS=∑(w_i×D_i)i=1 to 4 Where: w_i is the weight of the i-th dimension, and D_i is the score of the i-th dimension. Constraints: ∑w_i=1, and w_i≥0 2. Mathematical Model of Sales Efficiency Dimension (D1) D1 = α1 × CTR + α2 × GR + α3 × TCR in: CTR (Completion Rate Score) = min(S_actual / S_target×100, 120) GR (Growth Rate Score) = [1 + (S_current - S_previous) / |S_previous|] × 50 TCR (Trend Continuity Score) = 100 - 20 × k (where k is the number of consecutive months of abnormality) Weight allocation: α1 + α2 + α3 = 1 Recommended values: α1=0.5, α2=0.3, α3=0.2 3. Mathematical Model of Human Resource Efficiency Quality Dimension (D2) D2=β1×UPT+β2×ATV+β3×IPT+β4×SE Component definition: UPT (Adultery Percentage Score) = (UPT_actual / UPT_benchmark) × 50 + 50 ATV (Average Transaction Value Score) = (ATV_actual / ATV_region) × 100 IPT (Price Per Item Score) = (IPT_actual / IPT_standard) × 100 SE (Efficiency Score) = (SE_actual / SE_target) × 100 Constraint: β1+β2+β3+β4=1 Suggested weights: β1=0.4, β2=0.3, β3=0.2, β4=0.1 4. Mathematical Model of Member Value Dimension (D3) D3 = γ1 × MSR + γ2 × RR + γ3 × MG Component calculation: MSR (Member Sales Percentage Score) = (MSR_actual / 0.8) × 100 (maximum 120) RR (Repurchase Rate Score) = (RR_actual / 0.02) × 100 × λ (λ is an adjustment factor) MG (Member Growth Score) = 50 × (1 + Net_growth / Total_members) Weights: γ1 + γ2 + γ3 = 1, Recommendation: γ1 = 0.5, γ2 = 0.3, γ3 = 0.2 5. Mathematical Model for Product Efficiency Dimension (D4) D4 = δ1 × CE + δ2 × IS + δ3 × NP Component model: CE (Category Efficiency Score) = ∑(w_j × CR_j), j = 1 to 10 (TOP10 categories) IS (Inventory Health Score) = 100 × (1 - |Actual_turnover - Ideal_turnover| / Ideal_turnover) NP (New Product Performance Score) = (S_new / S_total) × (S_new / Q_new) × 100 Weights: δ1 + δ2 + δ3 = 1, Recommendation: δ1 = 0.5, δ2 = 0.3, δ3 = 0.2 In summary, health levels are categorized into three groups based on OHS scores: "Healthy" (OHS score ≥ 85), "Sub-healthy" (OHS score 70–85), and "Abnormal" (OHS score < 70). This classification allows businesses to quickly identify the efficacy levels of their products. For "Healthy" products, they can maintain their existing operational strategies, leveraging their remaining advantages to improve product profitability. For "Sub-healthy" products, they can delve deeper into the causes of their problems and take targeted countermeasures, such as adjusting inventory and increasing the promotion of new products, to further improve product efficacy. For "Abnormal" products, sufficient attention should be paid to analyzing the problems from multiple perspectives and taking timely corrective measures, such as adjusting product structure and clearing out slow-moving items, to improve product efficacy.

[0026] Step 3: Establishing the Data Foundation: A System of Five Key Business Reports This system brings the four-dimensional model to life, transforming raw Excel data into diagnostic evidence: like Figure 1 (Monthly Sales Change Table) shows the time points and extent of sales declines. Table 1 displays the monthly sales change trajectory over time, with periods of year-on-year / month-on-month declines exceeding thresholds clearly marked on the coordinates. Industry reference lines and regional average lines are added for comparison.

[0027] Establish time series functions: S_t=β0+β1t+β2t²+ε Where S_t represents the sales revenue in period t, and the trend score is obtained through regression analysis: Trend_score=100×(1-|β1| / mean(S)) like Figure 2 (Process Data Table) shows: Focusing on cross-selling rate, average order value, etc., reveals problems related to "people." This report collects product combination information and customer purchasing behavior from transaction data, calculates and analyzes the average number of cross-selling items per order and the average order value, and makes horizontal comparisons with historical data from the same period and stores in the same region. When the cross-selling rate is below the threshold, there may be problems such as ineffective sales staff recommendations and unreasonable product display layout; when the average order value fluctuates abnormally, it indicates that the design of promotional activities or the inventory depth of high-margin products should be checked.

[0028] Constructing the efficiency frontier function: Efficiency=f(UPT,ATV,Staff_count)=θ1UPT+θ2ATV-θ3Staff_count Relative efficiency score: Eff_score=(Efficiency_actual / Efficiency_best)×100.

[0029] like Figure 3 (Member Analysis Table) shows: Reflecting member momentum and contribution value, assessing customer relationship management level. Based on key indicators such as member purchase frequency, purchase amount, and recent purchase time, a member activity scoring model and a member contribution value stratification system are constructed. Horizontally, the differences in momentum and contribution value among different member levels are analyzed to identify high-value member groups at risk of churn; vertically, the changes in individual member lifecycles are analyzed to pinpoint key opportunities to reactivate dormant members. Customer Value CLV Function. Customer Value CLV Function: CLV=∑[P(t)×S(t)×(1+r)^(-t)] Where P(t) is the retention probability in period t, S(t) is the consumption in period t, and r is the discount rate.

[0030] like Figure 4 (The employee sales data table) shows that this report is used to analyze individual employee data, providing data for one-on-one coaching or addressing weaknesses to identify high-performing and low-performing employees. It collects data on the types of goods sold, quantities sold, sales amounts, and customer feedback for each employee daily, establishing a sales performance analysis model. It then compares the sales performance of employees in the same position horizontally by sales category to identify high-performing and low-performing employees; and vertically compares the changes in individual employee sales performance to identify employees with weak sales skills or poor sales service attitudes. After analyzing the report data, managers provide corresponding training and performance coaching plans and methods to improve the sales performance of all employees.

[0031] Constructing the performance distribution function: Z_score=(X_i-μ) / σ Outliers (Z_score>2 or<-2) are identified by fitting a normal distribution.

[0032] like Figure 5 (The table showing the sales percentage by category) is shown below. This table can monitor the status of product categories, guide the elimination and restocking of goods, and statistically analyze the sales quantity, sales amount, and the proportion of goods with small sales in each category. By comparing the sales percentage of each category horizontally, it can identify the goods with good and poor sales performance, providing data for elimination and selection. By comparing the changes in the sales percentage of each category vertically, it can analyze consumer demand and guide the adjustment of display strategies based on the strengths and weaknesses of product display performance. For example, displaying the best-selling goods in easily visible places can improve consumer shopping satisfaction and sales percentage.

[0033] ABC classification function: Category_score=(S_share×Profit_share) / (1+CV) CV stands for the coefficient of variation of sales volume, which measures stability.

[0034] Step 4: Building a Consensus Engine: The Eight-Step Method for Store Manager Interviews After the data diagnostics are completed, the operations manager will lead a joint review of the issues with the store manager. To bridge the gap between the "cold" nature of data and the "vague" nature of experience, we have created a fixed outline for the "dialogue" process, which is implemented in 8 steps.

[0035] 1) Preparation: Prepare interview materials based on data. The operations manager needs to collect basic operational data for the store, such as daily sales reports, sales completion rate, cross-selling rate, repurchase rate, and other core indicators.

[0036] 2) Begin: The operations manager requires creating a safe and effective dialogue environment. When meeting with the store manager, the store manager should begin the conversation in a calm, gentle, and tolerant manner, expressing that the purpose of the meeting is to discuss issues arising in store operations and find solutions, not to criticize or blame. This will make employees feel respected and valued, thus alleviating their concerns and encouraging openness. The meeting should be held in a relatively quiet and comfortable location to avoid disturbances and create an atmosphere conducive to effective communication. During discussions, managers should listen attentively to employees' opinions, avoid interrupting, and refrain from immediately dismissing differing viewpoints. Offering affirmation through nods and eye contact demonstrates recognition of the employee.

[0037] The key points for conducting interviews at the above stages are as follows: (1) Environmental requirements: Choose an independent space and adopt a 45° seating arrangement to avoid office stress scenarios.

[0038] (2) Opening remarks: "Our goal today is to solve the problem, not to assign blame. The company will provide full support." (3) Self-check: Store managers conduct self-assessments and collaboratively analyze data. Guidance for this phase includes easily understood assessment dimensions, such as the manager's accurate understanding of their store's sales targets, their self-assessment of their team's management skills, and their intuitive perception of their store's daily customer service capabilities. Objective evaluation dimensions, such as quantitative indicators, or subjective descriptions, such as key events, are used to encourage store managers to reflect on their initial judgments based on daily operational practices. Subsequently, the collected basic operational data is reviewed together with the store manager, comparing their self-assessment with the data indicators. For example, comparing the manager's sales completion rate forecast with actual data identifies cognitive biases, providing objective evidence for further in-depth discussions of the root causes of problems.

[0039] (1) Data presentation method: Use visual charts to avoid piling up raw data.

[0040] (2) Questioning techniques: Use the 5W2H analysis method, for example, "Why is the association rate low? → Why don't employees recommend a second product? → Is it a problem of ability or motivation?" 4) Diagnosis: The operations manager uses follow-up questions (such as "Why is the cross-selling rate low?" and "What are the underlying reasons?") to uncover deeper causes. During this process, the operations manager must remain patient and professional, gradually guiding the store manager from superficial issues to the core problems. For example, when the store manager reports "low customer traffic," further inquiries can be made such as "Which time periods have low traffic?" and "Did concurrent promotions by nearby competitors affect customer flow?" Through progressively deeper questioning, the operations manager can break free from preconceived notions and identify overlooked operational loopholes. Simultaneously, the analysis must be validated by combining historical store data and regional market dynamics to avoid subjective assumptions.

[0041] 5) Identification: The operations manager and store manager jointly identify no more than three core reasons, and ultimately determine one core reason. The author's practical experience shows that in the short term (generally within one month), a store can only resolve one major issue.

[0042] 6) Creative solution: The operations manager guides store managers to propose corresponding solutions. The solution creation phase is the output stage of the core cause identification phase. It requires store managers to propose specific countermeasures based on the identified core causes. These measures must be actionable, effectively implemented, and efficiently allocated with resources. During this process, the solution creation phase requires store managers to ask "heuristic" questions such as "How can we increase store traffic?" and "How can we improve overall store sales performance by increasing cross-selling?" to broaden their thinking. Furthermore, past successful experiences and industry-leading best practices are shared to stimulate divergent thinking and provide store managers with ideas and clues.

[0043] (1) Problem convergence principle: adopt the Pareto principle and focus on the core contradiction.

[0044] (2) Solution generation method: Use brainstorming or brainstorming to stimulate innovative thinking.

[0045] 7) Consensus: The store manager fills in the action plan in the "Weekly / Monthly Progress Plan," clearly defining the goals, actions, responsible parties, and timelines. Subsequently, the operations supervisor and store manager jointly conduct a detailed review of the plan, ensuring that each action closely revolves around the core root cause and that the goals are measurable. Responsible parties must clearly understand their duties, and timelines must be reasonable and actionable for subsequent tracking and evaluation. Once approved, the plan becomes a shared action guideline, ensuring that every step moves towards solving the core problem.

[0046] 8) Post-mortem analysis: The operations manager and store manager determine the review and follow-up points. At each review point, the operations manager and store manager jointly review the execution of the action plan. Based on detailed data, on-site observations, and employee feedback, a comprehensive evaluation of the effectiveness of each action is conducted. For actions that failed to achieve the desired results, the reasons are analyzed in depth to determine whether it was due to changes in external market conditions, poor execution, or an unreasonable plan. For actions that were executed well and achieved good results, lessons are summarized to facilitate subsequent actions and replication in operations. Based on the review results, the action plan is dynamically adjusted to enable more precise actions in subsequent operational improvement efforts.

[0047] (1) Rectification Plan: Follow the SMART principle and formulate detailed and specific action items.

[0048] (2) Follow-up mechanism: Establish weekly review nodes and establish corresponding inspection standards.

[0049] This method, which integrates the methodology of psychological counseling, transforms the company's business instructions from a "top-down" model to a consensus of "shared goals," significantly improving the acceptance, participation, and execution of store managers and employees.

[0050] Step 5: Execution and Closure: SMART Rectification Plan and Agile Retrospective For the agreed-upon solutions, the store manager formulates a SMART improvement plan. Based on this plan, the store manager organizes simulation exercises for each employee, and records the exercises and provides feedback. The operations supervisor implements process control through weekly data monitoring and remote video spot checks, and conducts weekly debriefings via telephone. Through these methods, if effective, the approach is reinforced; if ineffective, iterate rapidly, forming an agile PDCA cycle.

[0051] Develop a SMART corrective action plan. For example, to address the issue of "low store traffic," the operations manager's corrective action plan would be: "Each salesperson must conduct two simulated try-ons of the main products during shift changes each day, followed by supervision, recording, and feedback from the store manager." The plan is specific, measurable (twice daily), easy to execute, relevant (closely linked to the target), and has a daily deadline.

[0052] Implementation process monitoring. This is achieved through weekly reports (such as...). Figure 2 The monitoring includes cross-selling rate and remote video spot checks (drill situation). The operations supervisor will conduct frequent supervision on changes in the cross-selling rate and the drill situation. Store managers are required to report the drill situation daily: the reactions of employees during the simulated try-on process, the problems encountered and the solutions.

[0053] Implement iterative agile debriefing. Supervisors conduct weekly debriefing calls, reinforcing positive aspects and quickly revising negative ones. For example, if the sales test of the main product performed poorly this week, next week's test could be changed to a "one-sentence sales pitch for the main product." Figure 6 and Figure 7 As shown.

[0054] Empirical research findings and multi-case analysis Case Study 1: Store A of a Brand Company – Precise Diagnosis and System Improvement Problem Diagnosis: Data input model in July 2023: Sales efficiency: Completion rate 56.85%, far below the department average, resulting in a low score.

[0055] Human resource efficiency quality: linkage rate 2.08, < regional average 2.38.

[0056] Member value: Repurchase rate 1.36%, < Health level (2%).

[0057] Product effectiveness: Sales of short-sleeved T-shirts decreased by 226 units year-on-year (-56.8%).

[0058] The calculated OHS is 70.2 (sub-healthy), the AI ​​is abnormal, and the main weakness is in D1 (sales efficiency).

[0059] Root cause analysis: After discussions between the operations manager and store manager, it was found that there were issues such as a lack of a mechanism for identifying key products (decision lag of 2-3 weeks), insufficient staff pairing skills (new product training coverage rate of 60%), and low member interaction frequency. These problems directly led to low sales efficiency and failure to meet the cross-selling target.

[0060] To address the aforementioned issues, the operations team has developed targeted improvement measures, including: introducing product selection analysis tools to optimize the identification process for key products, thereby shortening the decision-making cycle; organizing specialized training to enhance employees' product pairing skills and ensuring 100% coverage of new product training; and planning frequent member activities to strengthen customer interaction and increase repurchase rates. These measures aim to fundamentally improve operational weaknesses and drive an overall performance recovery.

[0061] Improvement measures: Establish Wednesday merchandise data analysis meetings in stores (reducing the decision-making cycle to 3 days), implement "daily outfit matching" training, and launch the "old customers come back home" activity.

[0062] Results: One month later, the store’s cross-selling rate rose to 2.35 (+13%), the average order value reached 305 yuan (+12.5%), and the completion rate in the following month was 72% (+15.2%).

[0063] Case Study 2: A Brand Company's Store B – Data-Driven Acumen and Rapid Response Key Finding: The operations manager identified an opportunity in the shorts category through Report 5 (sales increased by 29 units, up 51.8%; revenue increased by 5789.5 yuan, up 82.7%). The operations manager immediately organized stores to conduct in-depth analysis of the shorts category, and developed a specific promotional plan based on historical sales data and market trends.

[0064] Rapid Response: The store immediately repositioned its displays to prime locations, meticulously crafted sales pitches, and implemented short-term incentive measures. The store manager optimized the display layout, placing shorts in more prominent positions and recommending them in combination with related items. Simultaneously, the store leveraged social media platforms to publish shorts styling guides to attract more customer attention. In terms of promotional strategies, the store launched limited-time discounts and spending-based reduction activities to further stimulate consumer demand. These measures effectively increased the exposure and conversion rate of shorts, resulting in a significant performance boost for the store.

[0065] Results and Replication: The store achieved a completion rate of 105.33% in July, defying the trend. The store's experience was distilled into a "rapid response mechanism for blockbuster categories." The operations manager promoted this experience in the region, and 15 stores replicated the experience, resulting in an average sales increase of 8.5%.

[0066] This comprehensive mathematical model transforms qualitative diagnosis into quantitative assessment, providing apparel retail stores with a scientific tool for measuring store health. By regularly calculating OHS scores, businesses can achieve continuous monitoring and precise improvement of store health.

[0067] This invention presents a data-driven lean operation model for apparel retail stores, applicable to apparel chain enterprises, shopping mall stores, and other scenarios. It achieves lean store operation through a systematic and data-driven approach. Those skilled in the art can make modifications within the principles of this invention, and these modifications should also be considered within the scope of protection of this invention.

Claims

1. A lean operation model for apparel retail stores from a data-driven perspective, characterized by: Includes the following modules: The four-dimensional health assessment module is used to quantitatively evaluate store operation data from four dimensions: sales efficiency, human resource efficiency, member value, and product efficiency, and calculate the overall health score (OHS) based on the indicators of each dimension. The data report generation module is used to generate five key business reports, including a monthly sales trend report, a process data report, a member analysis report, an employee sales data report, and a mid-category sales report, enabling multi-dimensional visualization analysis of operational data; The store manager interview consensus module is implemented through a structured interview process that includes eight steps: preparation, start, self-inspection, diagnosis, identification, creative solution, consensus, and debriefing. It guides store managers and operations managers to jointly identify the root causes of problems and develop SMART rectification plans. The closed-loop execution review module is used to execute the SMART rectification plan and conduct periodic reviews, achieving continuous improvement through the PDCA cycle.

2. The lean operation model for apparel retail stores according to claim 1, characterized in that, In the four-dimensional health assessment module, the formula for calculating the overall health score (OHS) is as follows: OHS=i=1∑4(wi×Di) Where wi is the weight of the i-th dimension, Di is the score of the i-th dimension, and ∑wi=1.

3. The lean operation model for apparel retail stores according to claim 2, characterized in that, The formula for calculating the sales efficiency dimension D1 includes: D1 = α1 × CTR + α2 × GR + α3 × TCR Wherein, CTR is the completion rate score, GR is the growth rate score, TCR is the trend continuity score, and α1+α2+α3=1.

4. The lean operation model for apparel retail stores from a data-enabled perspective as described in claim 1, characterized in that, In the data report generation module, the monthly sales trend table is used to identify abnormal sales periods, the process data table is used to analyze the process indicators of cross-selling rate and average order value, the member analysis table is used to evaluate member activity and contribution, the employee sales data table is used to identify differences in employee performance, and the mid-category sales table is used to monitor category performance.

5. The lean operation model for apparel retail stores from a data-enabled perspective as described in claim 1, characterized in that, In the consensus module for store manager interviews, the self-inspection step includes store manager self-assessment and data comparison; the diagnosis step involves continuous questioning to uncover the root causes of problems; the identification step involves jointly identifying no more than three core reasons and focusing on one core reason; the solution creation step guides store managers to propose solutions; the consensus step forms a SMART rectification plan; and the debriefing step sets debriefing nodes and evaluates the implementation effect.

6. The lean operation model for apparel retail stores from a data-enabled perspective as described in claim 1, characterized in that, In the closed-loop execution review module, the review cycle is once a week. Process control is achieved through data monitoring, remote spot checks, and telephone reviews, and the rectification plan is dynamically adjusted.

7. The lean operation model for apparel retail stores from a data-enabled perspective as described in claim 1, characterized in that, The model is built on the DCER framework and includes four stages: Diagnose, Consensus, Execute, and Review, forming a closed-loop operation and management process.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the method steps of the lean operation model for apparel retail stores from the perspective of data empowerment as described in any one of claims 1-7.

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