Mining production raw material warehouse management purchase intelligent management method

Abstract

The invention provides a mining production raw material warehouse management purchase intelligent management method. The method comprises the following steps: S1, carrying out raw material demand analysis and prediction; s2, carrying out raw material inventory optimization; s3, making a raw material purchasing plan; s4, raw material arrival inspection and warehousing are carried out; according to the method, the efficiency, precision and reliability of automatic batching in mining production are remarkably improved, and the problems of inaccurate prediction and low inventory management efficiency in the prior art are solved.

Description

Technical Field

[0001] The present invention specifically relates to an intelligent management method for the procurement of a mine production raw material warehouse. Background Art

[0002] The existing mine production mainly includes steps such as raw material reclaiming and weighing, ore conveying, automatic weighing, ore property analysis, raw material storage, mixing and packaging, etc. However, the existing warehouse management and procurement methods are relatively simple and primitive, and there are often risks of inventory backlog and out-of-stock. Both warehouse management and procurement are carried out by manual control methods. Therefore, it is very difficult to achieve efficient intelligent warehouse management and procurement. Therefore, this application proposes an intelligent management method for the procurement of a mine production raw material warehouse to solve the above problems. Summary of the Invention

[0003] The purpose of the present invention is to provide an intelligent management method for the procurement of a mine production raw material warehouse in view of the deficiencies of the prior art, and this intelligent management method for the procurement of a mine production raw material warehouse can well solve the above problems.

[0004] To meet the above requirements, the technical solution adopted by the present invention is: providing an intelligent management method for the procurement of a mine production raw material warehouse, and this intelligent management method for the procurement of a mine production raw material warehouse includes the following steps:

[0005] S1: Steps for raw material demand analysis and prediction, specifically including:

[0006] S11: Collect raw material usage data in the past few years from historical production records, sales data, and market research reports;

[0007] S12: Remove outliers, missing values, and duplicate data to ensure the accuracy and integrity of the data;

[0008] S13: Use the ARIMA model to fit the historical data and determine the model parameters;

[0009] S14: Divide the data into a training set and a test set, and use the training set to train the ARIMA model;

[0010] S15: Use the trained model to predict the raw material demand for a period of time in the future;

[0011] S16: Compare the prediction result with the actual demand, and adjust the model parameters to improve the prediction accuracy;

[0012] S2: Steps for raw material inventory optimization, specifically including:

[0013] S21: According to the predicted raw material demand, determine the annual demand D, the order cost per order S, and the unit inventory holding cost H;

[0014] S22: Calculate the optimal order quantity for each raw material using the EOQ formula;

[0015] S23: Obtain the safety stock level based on demand fluctuations and supplier delivery times;

[0016] S24: Use the inventory management system to monitor the inventory level in real time and trigger replenishment reminders;

[0017] S3: Steps for formulating the raw material procurement plan, specifically including:

[0018] S31: Evaluate each supplier based on the supplier's delivery time, price, and quality;

[0019] S32: Use a linear programming model with the goal of minimizing the total procurement cost, considering the supplier's delivery capacity and inventory limitations;

[0020] S33: Calculate the optimal procurement plan using a simplex method linear programming solver;

[0021] S34: Generate a purchase order according to the optimal plan and send it to the supplier;

[0022] S35: Track the execution status of the purchase order in real time to ensure timely arrival of goods;

[0023] S4: Steps for raw material arrival inspection and warehousing, specifically including:

[0024] S41: Randomly select samples from the arriving raw materials for quality inspection according to the AQL standard;

[0025] S42: Check the physical and chemical properties of the samples to ensure compliance with the procurement requirements;

[0026] S43: Check whether the quantity of the arriving goods is consistent with the purchase order;

[0027] S44: Register the qualified raw materials in the warehouse and update the inventory management system;

[0028] S45: Return the unqualified raw materials;

[0029] Preferably, in step S1, the ARIMA model captures trends and seasonality in the data through three parts: autoregression, differencing, and moving average. The specific formula is:

[0030]

[0031] Where:

[0032] φ(B): Autoregressive polynomial;

[0033] θ(B): Moving average polynomial;

[0034] B: Lag operator;

[0035] d: Order of differencing;

[0036] y i : Time series data;

[0037] ∈ t : White noise;

[0038] Periodic component;

[0039] α k : Amplitude of the k-th periodic component;

[0040] T: Period length;

[0041] φ k Phase angle of the k-th periodic component.

[0042] Preferably, in step S2, the EOQ model determines the optimal order quantity by minimizing the total inventory cost, and the specific formula is:

[0043]

[0044] Where:

[0045] Exponential function, used to adjust the optimal order quantity;

[0046] σ: Standard deviation of demand;

[0047] μ: Mean of demand;

[0048] e: Natural constant;

[0049] Q * : Optimal order quantity;

[0050] D: Annual demand;

[0051] S: Ordering cost per order;

[0052] H: Holding cost per unit of inventory.

[0053] Preferably, in step S3, the linear programming model finds the optimal procurement plan through the objective function and constraint conditions, and the objective function is specifically as follows:

[0054]

[0055] Where:

[0056] Z: Total procurement cost;

[0057] c i : Unit cost of the i-th supplier;

[0058] x i : The quantity purchased from the i-th supplier;

[0059] Regularization term to prevent the purchase quantity from being too concentrated or dispersed;

[0060] λ: Regularization coefficient;

[0061] The mean value of the purchase quantity.

[0062] Preferably, in step S4, the AQL inspection determines whether the quality of the entire batch of raw materials reaches the acceptable level through sampling inspection. The specific formula is:

[0063]

[0064] Where:

[0065] Gaussian function used to adjust the AQL value;

[0066] m: Sampling quantity;

[0067] μ m : Ideal sampling quantity;

[0068] σ m : Standard deviation of the sampling quantity.

[0069] The advantages of the intelligent management method for the purchase of mine production raw material warehouses are as follows:

[0070] (1) Through time series analysis and inventory optimization models, the accuracy of raw material demand forecasting and the efficiency of inventory management are improved, and inventory backlogs and out-of-stock risks are reduced.

[0071] (2) Using calculus optimization methods and statistical regression analysis, the accurate calculation of the batching ratio and the high-precision calibration of equipment are ensured, and the consistency of batching and product quality are improved. Brief Description of the Drawings

[0072] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The same reference numerals are used to represent the same or similar parts in these drawings. The schematic embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation of the present application. In the drawings:

[0073] Figure 1 Schematically shows a flowchart of an intelligent management method for the purchase of mine production raw material warehouses according to an embodiment of the present application. Detailed Embodiments

[0074] To make the objectives, technical solutions, and advantages of this application clearer, the following further elaborates on this application in detail in conjunction with the accompanying drawings and specific embodiments.

[0075] In the following description, references to "one embodiment", "an embodiment", "one example", "an example", etc. indicate that the embodiment or example so described may include a specific feature, structure, characteristic, property, element, or limitation, but not every embodiment or example necessarily includes the specific feature, structure, characteristic, property, element, or limitation. Additionally, repeated use of the phrase "according to an embodiment of this application" may, although possibly referring to the same embodiment, not necessarily refer to the same embodiment.

[0076] For simplicity, certain technical features well-known to those skilled in the art are omitted in the following description.

[0077] According to an embodiment of this application, an intelligent management method for the procurement of mining production raw material warehouses is provided. As Figure 1 shown, it includes the following steps:

[0078] S1: Steps for raw material demand analysis and prediction, specifically including:

[0079] S11: Collect raw material usage data for the past few years from historical production records, sales data, and market research reports;

[0080] S12: Remove outliers, missing values, and duplicate data to ensure the accuracy and integrity of the data;

[0081] S13: Use the ARIMA model to fit the historical data and determine the model parameters;

[0082] S14: Divide the data into a training set and a test set, and use the training set to train the ARIMA model;

[0083] S15: Use the trained model to predict the raw material demand for a future period;

[0084] S16: Compare the prediction results with the actual demand, and adjust the model parameters to improve the prediction accuracy;

[0085] S2: Steps for raw material inventory optimization, specifically including:

[0086] S21: Determine the annual demand D, the order cost per order S, and the unit inventory holding cost H according to the predicted raw material demand;

[0087] S22: Use the EOQ formula to calculate the optimal order quantity for each raw material;

[0088] S23: Obtain the safety inventory level based on demand fluctuations and supplier delivery times;

[0089] S24: Use the inventory management system to monitor the inventory level in real time and trigger a replenishment reminder;

[0090] S3: Steps for formulating a raw material procurement plan, specifically including:

[0091] S31: Evaluate each supplier based on the supplier's delivery time, price, and quality;

[0092] S32: Use a linear programming model with the goal of minimizing the total procurement cost, considering the supplier's delivery capacity and inventory constraints;

[0093] S33: Calculate the optimal procurement plan using a simplex method linear programming solver;

[0094] S34: Generate a purchase order according to the optimal plan and send it to the supplier;

[0095] S35: Track the execution status of the purchase order in real time to ensure timely arrival of goods;

[0096] S4: Steps for inspecting and warehousing the arrived raw materials, specifically including:

[0097] S41: Randomly select samples from the arrived raw materials for quality inspection according to the AQL standard;

[0098] S42: Check the physical and chemical properties of the samples to ensure compliance with the procurement requirements;

[0099] S43: Check whether the quantity of the arrived goods is consistent with the purchase order;

[0100] S44: Register the qualified raw materials in the warehouse and update the inventory management system;

[0101] S45: Return the unqualified raw materials;

[0102] According to an embodiment of the present application, in step S1 of the intelligent management method for the mine production raw material warehouse management and procurement, the ARIMA model captures trends and seasonality in the data through three parts: autoregression, differencing, and moving average. The specific formula is:

[0103]

[0104] Where:

[0105] φ(B): Autoregressive polynomial;

[0106] θ(B): Moving average polynomial;

[0107] B: Lag operator;

[0108] d: Number of differencing;

[0109] y t : Time series data;

[0110] ∈ t : White noise;

[0111] Periodic component;

[0112] α k : Is the amplitude of the k-th periodic component;

[0113] T: Period length;

[0114] φ k Phase angle of the k-th periodic component.

[0115] According to an embodiment of the present application, in step S2 of the intelligent management method for the procurement of mine production raw material warehouses, the EOQ model determines the optimal order quantity by minimizing the total inventory cost. The specific formula is:

[0116]

[0117] Where:

[0118] Exponential function, used to adjust the optimal order quantity;

[0119] σ: Standard deviation of demand;

[0120] μ: Mean of demand;

[0121] e: Natural constant;

[0122] Q * : Optimal order quantity;

[0123] D: Annual demand;

[0124] S: Ordering cost per order;

[0125] H: Unit inventory holding cost.

[0126] According to an embodiment of the present application, in step S3 of the intelligent management method for the procurement of mine production raw material warehouses, the linear programming model finds the optimal procurement plan through the objective function and constraint conditions. The objective function is specifically as follows:

[0127]

[0128] Where:

[0129] Z: Total procurement cost;

[0130] c i : Unit cost of the i-th supplier;

[0131] x i : The quantity purchased from the i-th supplier;

[0132] Regularization term to prevent over - concentration or over - dispersion of the purchase quantity;

[0133] λ: Regularization coefficient;

[0134] The mean value of the purchase quantity.

[0135] According to an embodiment of the present application, in step S4 of the intelligent management method for the purchase of the mine - used production raw material warehouse, the AQL inspection determines whether the quality of the entire batch of raw materials reaches an acceptable level through sampling inspection. The specific formula is:

[0136]

[0137] Where:

[0138] Gaussian function, used to adjust the AQL value;

[0139] m: Sampling quantity;

[0140] μ m : Ideal sampling quantity;

[0141] σ m : Standard deviation of the sampling quantity.

[0142] According to an embodiment of the present application, the raw material demand analysis and prediction in the intelligent management method for the purchase of the mine - used production raw material warehouse can be carried out in the following manner:

[0143] 1. Data collection:

[0144] Collect the raw material usage data of the past few years from historical production records, sales data, and market research reports. Ensure that the data covers scenarios of different seasons, different production batches, and different market demands.

[0145] 2. Data cleaning:

[0146] Use data cleaning tools (such as the Pandas library in Python) to remove outliers, missing values, and duplicate data. Standardize the data to ensure consistent data formats.

[0147] 3. Time - series analysis:

[0148] Use statistical software (such as the statsmodels library in R or Python) to perform time series decomposition on historical data, and extract trend, seasonal, and residual components. Determine the parameters (p, d, q) of the ARIMA model through the autocorrelation function (ACF) and partial autocorrelation function (PACF).

[0149] 4. Model training:

[0150] Divide the data into a training set (80%) and a test set (20%), and use the training set to train the ARIMA model. Select the optimal model by minimizing the AIC (Akaike Information Criterion) or BIC (Bayesian Information Criterion).

[0151] 5. Demand forecasting:

[0152] Use the trained model to predict the raw material demand for a future period (such as one month or one quarter). Generate a confidence interval for the prediction results and evaluate the uncertainty of the prediction.

[0153] 6. Result verification:

[0154] Compare the prediction results with the actual demand, and calculate the mean squared error (MSE) or mean absolute error (MAE). Adjust the model parameters according to the error and retrain the model to improve the prediction accuracy.

[0155] Improve the accuracy of raw material demand forecasting in the above steps, reduce inventory backlogs or shortages caused by prediction errors. Evaluate the uncertainty of the prediction through the confidence interval, and provide more comprehensive information for decision-making. Optimize the production plan and reduce production costs.

[0156] According to an embodiment of the present application, the raw material inventory optimization in the intelligent management method for the procurement of the mine production raw material warehouse can be carried out in the following manner:

[0157] 1. Determine parameters:

[0158] According to the predicted raw material demand, determine the annual demand (D). Obtain the order cost per order (S) and the unit inventory holding cost (H) through financial data.

[0159] 2. Calculate the economic order quantity (EOQ):

[0160] Use the EOQ formula to calculate the optimal order quantity for each raw material.

[0161] 3. Determine the safety stock:

[0162] According to the demand fluctuation and the supplier's delivery time, calculate the safety stock level. Use statistical methods (such as normal distribution or Poisson distribution) to determine the safety stock quantity.

[0163] 4. Develop an inventory strategy:

[0164] Combine EOQ and safety stock to formulate an inventory management strategy for each raw material. Determine the reorder point and reorder quantity to ensure that the inventory level is always within a reasonable range.

[0165] 5. Implement inventory monitoring:

[0166] Use an inventory management system to monitor the inventory level in real time and trigger replenishment reminders. Regularly generate inventory reports and analyze inventory turnover and inventory costs.

[0167] The above steps reduce inventory holding costs and ordering costs, and optimize inventory management. By means of safety stock, the risk of out-of-stock caused by demand fluctuations or supply delays is reduced. Inventory turnover is increased and capital occupancy is reduced.

[0168] According to an embodiment of the present application, the formulation of the raw material procurement plan in the intelligent management method for the mine production raw material warehouse management and procurement can be carried out in the following manner:

[0169] 1. Supplier evaluation:

[0170] Evaluate the comprehensive score of each supplier according to the delivery time, price and quality of the supplier. Use the Analytic Hierarchy Process (AHP) or Multi-Criteria Decision Analysis (MCDA) to determine the weight of the supplier.

[0171] 2. Establish a procurement model:

[0172] Use a linear programming model with the goal of minimizing the total procurement cost, considering the delivery capacity of the supplier and inventory limitations.

[0173] 3. Solve the optimal solution:

[0174] Use a linear programming solver (such as the simplex method or the interior point method) to calculate the optimal procurement plan. Generate a procurement plan table to clarify the procurement quantity, procurement time and supplier of each raw material.

[0175] 4. Generate a purchase order:

[0176] Generate a purchase order according to the optimal plan and send it to the supplier. Confirm the order details with the supplier to ensure on-time delivery.

[0177] 5. Track the procurement progress:

[0178] Track the execution status of the purchase order in real time to ensure on-time arrival of goods. Timely handle delayed or abnormal orders.

[0179] The above steps optimize procurement costs and reduce procurement risks. By means of supplier evaluation and procurement model, the optimal supplier and procurement plan are selected. Procurement efficiency is improved and raw materials are ensured to arrive on time.

[0180] According to an embodiment of the present application, the intelligent management method for the procurement of raw material warehouses in mines can conduct the inspection and warehousing of raw materials in the following manner:

[0181] 1. Sampling inspection:

[0182] According to the AQL standard, randomly select samples from the arriving raw materials for quality inspection. Use statistical methods to determine the sampling quantity to ensure the reliability of the inspection results.

[0183] 2. Quality assessment:

[0184] Check the physical and chemical properties of the samples to ensure compliance with the procurement requirements. Use laboratory equipment (such as spectrometers or chromatographs) for precise detection.

[0185] 3. Quantity verification:

[0186] Verify whether the quantity of the arriving goods is consistent with the purchase order. Use barcode or RFID technology to automate the verification process.

[0187] 4. Warehousing registration:

[0188] Register the qualified raw materials in the warehouse and update the inventory management system. Mark the storage location to ensure that the raw materials are easy to find and manage.

[0189] 5. Handling unqualified raw materials:

[0190] Return or rework the unqualified raw materials. Record the reasons for non-conformity and feedback to the supplier to avoid similar problems.

[0191] The above steps: Ensure that the quality of the raw materials meets the production requirements, reduce production losses caused by raw material quality problems. Improve the warehousing efficiency through automated verification and registration. Record the information of unqualified raw materials to provide a basis for supplier management.

[0192] The above-described embodiments merely represent several implementation manners of the present invention. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the claims.

Claims

1. An intelligent management method for the procurement of a mining production raw material warehouse, characterized in that, It includes the following steps: S1: Steps for raw material demand analysis and prediction, specifically including: S11: Collect raw material usage data for the past few years from historical production records, sales data, and market research reports; S12: Remove outliers, missing values, and duplicate data to ensure data accuracy and integrity; S13: Use the ARIMA model to fit the historical data and determine the model parameters; S14: Divide the data into a training set and a test set, and use the training set to train the ARIMA model; S15: Use the trained model to predict the raw material demand for a future period; S16: Compare the prediction results with the actual demand, and adjust the model parameters to improve the prediction accuracy; S2: Steps for raw material inventory optimization, specifically including: S21: Determine the annual demand D, the order cost per order S, and the unit inventory holding cost H according to the predicted raw material demand; S22: Use the EOQ formula to calculate the optimal order quantity for each raw material; S23: Obtain the safety inventory level based on demand fluctuations and supplier lead times; S24: Use the inventory management system to monitor the inventory level in real time and trigger replenishment reminders; S3: Steps for formulating a raw material procurement plan, specifically including: S31: Evaluate each supplier based on the supplier's delivery time, price, and quality; S32: Use a linear programming model with the goal of minimizing the total procurement cost, considering the supplier's delivery capacity and inventory limitations; S33: Use the simplex method linear programming solver to calculate the optimal procurement plan; S34: Generate a purchase order according to the optimal plan and send it to the supplier; S35: Track the execution status of the purchase order in real time to ensure timely arrival; S4: Steps for raw material arrival inspection and warehousing, specifically including: S41: Randomly select samples from the arriving raw materials for quality inspection according to the AQL standard; S42: Check the physical and chemical properties of the samples to ensure compliance with the procurement requirements; S43: Verify whether the quantity of the arrived goods is consistent with the purchase order; S44: Register the qualified raw materials in the warehouse and update the inventory management system; S45: Return the unqualified raw materials.

2. The intelligent management method for the procurement of the mine production raw material warehouse pipes according to claim 1, wherein: In step S1, the ARIMA model captures trends and seasonality in the data through three parts: autoregression, differencing, and moving average. The specific formula is: Where: φ(B): Autoregressive polynomial; θ(B): Moving average polynomial; B: Lag operator; d: Number of differencing; y t : Time series data; ∈ t : white noise; Periodic component; α k : is the amplitude of the k-th periodic component; T: Cycle length; φ k Phase angle of the k-th periodic component.

3. The intelligent management method for the procurement of mine production raw material warehouse pipes according to claim 1, characterized in that: In step S2, the EOQ model determines the optimal order quantity by minimizing the total inventory cost. The specific formula is: Where: An exponential function, used to adjust the optimal order quantity; σ: Standard deviation of demand; μ: Mean of demand; e: Natural constant; Q * : Optimal order quantity; D: Annual demand; S: Order cost per order; H: Unit inventory holding cost.

4. The intelligent management method for the procurement of mine production raw material warehouse pipes according to claim 1, characterized in that: In step S3, the linear programming model finds the optimal procurement plan through the objective function and constraints. The objective function is as follows: Where: Z: Total procurement cost; c i : The unit cost of the i-th supplier; x i : The quantity purchased from the i-th supplier; Regularization term to prevent the procurement quantity from being too concentrated or dispersed; λ: Regularization coefficient; The mean of the purchase quantity.

5. The intelligent management method for the procurement of mine production raw material warehouse pipes according to claim 1, characterized in that: In step S4, the AQL inspection determines whether the quality of the entire batch of raw materials meets the acceptable level through sampling inspection. The specific formula is: Where: Gaussian function, used to adjust the AQL value; m: Sampling quantity; μ m : Ideal sampling quantity; σ m : Standard deviation of the sampling quantity.

Images