Automatic purchasing system for spare parts of cigarette enterprises

Abstract

The present invention discloses an automatic purchase system for spare parts of a cigarette enterprise, including a purchase prediction module and an automatic purchase module; the system can scientifically and reasonably predict the monthly minimum purchase quantity of spare parts for a single product based on the monthly planned production of departments / workshops, daily consumption of spare parts, historical data and other information data, so as to avoid the situation of too much or too little spare parts reserves, reduce the monthly spare parts capital investment, and enable it to meet the needs of normal production, and avoid the occurrence of spare parts deterioration due to long-term storage. The system can automatically generate a purchase order based on the monthly minimum usage and inventory, and upload it to the purchase system in real time to complete the purchase declaration and purchase work, effectively changing the subjective judgment of the purchase quantity of spare parts for equipment in the existing tobacco industry, and then realizing the refined management of spare parts procurement.

Description

Technical Field

[0001] The invention relates to the field of spare parts management in the tobacco industry, and in particular to an automatic spare parts purchasing system for cigarette enterprises. Background Art

[0002] Spare parts reserves are a difficult problem that every cigarette processing company has to face. It manifests itself in two aspects: one is the uncertainty of spare parts demand, and the other is the gradual increase in the amount of funds occupied by spare parts due to over-stocking.

[0003] The monthly production volume of cigarette manufacturing enterprises is unique in the industry. It is planned and scheduled, so the production time of equipment each month will vary greatly. In order to reduce spare parts investment and realize lean management of spare parts, the ideal management state is to purchase spare parts in a targeted manner according to planned production every month. However, the current spare parts purchase volume and minimum inventory volume are purchased by buyers based on their experience, which is related to the buyers' personal experience and violates the lean management concept.

[0004] Because of purchasing and inventory management based on experience, a certain number of spare parts will be purchased in excess to avoid the situation where there are no spare parts available for emergency production. Sometimes, due to adjustments in product structure or equipment updates, excess spare parts will remain in the warehouse for a long time or have nowhere to be used, causing the quality of spare parts to deteriorate or even be scrapped, which in turn results in a large amount of spare parts funds being occupied.

[0005] In order to solve this problem, the present application hopes to achieve scientific procurement of spare parts and reduce the capital investment in spare parts. Summary of the invention

[0006] To solve the above problems, the present application provides an automatic spare parts purchasing system for cigarette enterprises, which is used for scientific prediction of the monthly spare parts procurement volume under the planned production conditions of the tobacco industry. This system can reduce the proportion of monthly spare parts capital investment, reduce the waiting time of spare parts in the warehouse, and increase the spare parts availability rate. It can realize scientific prediction and automatic purchase of spare parts through information technology.

[0007] The technical solution adopted by the present invention to solve the technical problem is:

[0008] Automatic purchasing system for spare parts of cigarette enterprises, including:

[0009] Purchase forecast module, interactive module and automatic purchase module;

[0010] The procurement forecast module includes a monthly single-machine production time calculation model and a monthly spare parts usage forecast model;

[0011] The monthly single-machine production time calculation model is a linear equation calculation model based on the production plans and production days of different departments / workshops:

[0012] Silk-making workshop: planned production batch*(batch weight / rated production flow)=monthly single-machine production time;

[0013] Rolling and packaging workshop: planned production volume of soft or hard packaging / (planned number of units to be opened*average single machine capacity) = monthly single machine production time;

[0014] Other departments: planned production volume / daily production capacity = monthly single machine production time;

[0015] The monthly spare parts usage forecast model:

[0016] Prediction model: BP neural network model;

[0017] Model parameters: hidden layer is 1, input layer is 1, output layer is 1, and the number of training steps is 1000;

[0018] Data sources: Monthly production time of a single machine, spare parts replacement interval;

[0019] Among them, the monthly single-machine production time is calculated by the monthly single-machine production time calculation model;

[0020] The spare parts replacement interval is obtained from the daily operation data of the equipment;

[0021] The monthly spare parts consumption of a certain model is:

[0022] Monthly spare parts usage forecast value + revised value + planned preventive maintenance times = monthly spare parts usage;

[0023] Among them, the correction value is:

[0024] (purchase volume of spare parts for a certain model in the past year / 12)*5%=correction value (round the result to an integer);

[0025] Planned preventive maintenance times:

[0026] N1+N2+…= planned preventive maintenance times; where N is the planned maintenance times of equipment using the same spare parts;

[0027] Monthly spare parts purchase volume forecast:

[0028] Monthly spare parts usage - inventory = monthly spare parts purchase quantity;

[0029] The interactive module includes an input unit and an analysis unit;

[0030] The input unit is used for department / workshop selection, production plan input, and planned preventive maintenance times input;

[0031] The analysis unit is used to analyze the spare parts usage forecast data, spare parts purchase volume forecast data and actual data within the cycle and display them using visualization means;

[0032] The automatic purchasing module includes a purchase order generating unit and an order sending unit;

[0033] The purchase order generation unit generates a purchase order according to the result of the purchase prediction module;

[0034] The order sending unit uploads the purchase order to the procurement system after it is approved to complete the purchase declaration.

[0035] The beneficial effects brought by the present invention are:

[0036] The automatic spare parts purchasing system for cigarette enterprises based on the present application can scientifically and reasonably predict the monthly minimum purchase quantity of single spare parts according to the department / workshop monthly planned production schedule, daily consumption of spare parts, historical data and other information data, so as to avoid excessive or insufficient spare parts reserves, reduce monthly spare parts capital investment, and enable it to meet normal production needs, avoiding the occurrence of spare parts degradation due to long-term storage.

[0037] Based on information systems and communication means, this system can automatically generate purchase orders based on the monthly minimum usage and inventory, and upload them to the procurement system in real time to complete purchase declaration and purchasing work, effectively changing the current subjective judgment of equipment spare parts procurement volume in the tobacco industry, and thus realizing refined management of spare parts procurement. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0039] Figure 1 This is the system block diagram of the automatic spare parts purchasing system for cigarette companies;

[0040] Figure 2 This is a schematic diagram of the algorithm principle of the procurement forecast module;

[0041] Figure 3 This is a schematic diagram of the architecture of the monthly spare parts usage prediction model. DETAILED DESCRIPTION

[0042] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0043] Example 1

[0044] Reference Figure 1 , automatic purchasing system for spare parts of cigarette enterprises, including:

[0045] Procurement forecast module, interaction module and automatic procurement module.

[0046] Reference Figure 2 ,The procurement forecast module includes a monthly single machine production time calculation model and a monthly spare parts usage forecast model;

[0047] (1) Monthly single-machine production time calculation model:

[0048] Since the work contents of each department / workshop of a cigarette manufacturing enterprise are different, the calculation methods of the single-machine production time (i.e., single-machine operation time) vary greatly, so different calculation formulas need to be established. This model is a linear equation calculation model based on the production plans (i.e., production structures) and production days of different departments / workshops:

[0049] Silk-making workshop: planned production batch*(batch weight / rated production flow)=monthly single-machine production time;

[0050] Rolling and packaging workshop: planned production volume of soft or hard packaging / (planned number of units to be opened*average single machine capacity) = monthly single machine production time;

[0051] Other departments: planned production volume / daily production capacity = monthly single machine production time;

[0052] (2) Monthly spare parts usage forecast model:

[0053] Prediction model: BP neural network model;

[0054] Reference Figure 3 , model parameters: hidden layer is 1, input layer is 1, output layer is 1, and the number of training steps is 1000;

[0055] Data sources: Monthly production time of a single machine, spare parts replacement interval;

[0056] Among them, the monthly single-machine production time is calculated by the monthly single-machine production time calculation model;

[0057] The spare parts replacement interval is obtained from the daily operation data of the equipment. Since the daily operation data of the equipment, such as daily repairs and work records, are saved by the company's equipment management system and used as historical production data, they can be directly called;

[0058] Based on this, the monthly spare parts consumption of a certain model is:

[0059] Monthly spare parts usage forecast value + revised value + planned preventive maintenance times = monthly spare parts usage;

[0060] Among them, the correction value is:

[0061] (the purchase volume of spare parts of a certain model in the past year (i.e. the historical purchase volume, which is called by the equipment management system) / 12)*5%=correction value (the result is rounded to an integer);

[0062] Planned preventive maintenance times:

[0063] N1+N2+…= planned preventive maintenance times; where N is the planned maintenance times of equipment using the same spare parts, taking bearing (FL204) as an example:

[0064] This type of spare parts is needed on the conveyor belts of the silk-making workshop, winding workshop and logistics center. Taking May as an example, the demand for this type of spare parts during preventive maintenance in the silk-making workshop, winding workshop and logistics center was N1=105, N2=89 and N3=75.

[0065] (3) Monthly spare parts procurement volume forecast:

[0066] Calculated by the automatic calculation unit: Monthly spare parts usage - inventory = Monthly spare parts purchase quantity.

[0067] The interactive module includes an input unit and an analysis unit:

[0068] Input unit, used for department / workshop selection, production plan input, and planned preventive maintenance times input;

[0069] The analysis unit is used to analyze the spare parts usage forecast data, spare parts purchase volume forecast data and actual data within the cycle and display them using visualization methods. The analysis data can be used to verify the effectiveness of the model, and can also be used to optimize and correct the model to improve the model prediction accuracy;

[0070] The automatic purchasing module includes a purchase order generating unit and an order sending unit;

[0071] The purchase order generation unit generates a purchase order according to the result of the purchase forecast module;

[0072] After the purchase order is approved, the order sending unit uploads it to the procurement system to complete the purchase declaration.

[0073] Example 2

[0074] The automatic purchasing method of spare parts for cigarette enterprises comprises the following steps:

[0075] S1 inputs the department / workshop, the production plan of each department / workshop, and the planned preventive maintenance times of a certain model of spare parts;

[0076] S2 selects the department / workshop, the production plan of the department / workshop and a certain type of spare parts to be purchased;

[0077] S3 Procurement Forecast

[0078] S3.1 Month Single Machine Production Hours Calculation:

[0079] Because the work contents of each department / workshop of a cigarette manufacturing enterprise are different, the calculation methods of the production time of a single machine vary greatly, so different calculation formulas need to be established. This model is a linear equation calculation model based on the production plans and production days of different departments / workshops:

[0080] Silk-making workshop: planned production batch*(batch weight / rated production flow)=monthly single-machine production time;

[0081] Rolling and packaging workshop: planned production volume of soft or hard packaging / (planned number of units to be opened*average single machine capacity) = monthly single machine production time;

[0082] Other departments: planned production volume / daily production capacity = monthly single machine production time;

[0083] S3. Spare parts usage forecast for February:

[0084] Prediction model: BP neural network model;

[0085] Model parameters: hidden layer is 1, input layer is 1, output layer is 1, and the number of training steps is 1000;

[0086] Data sources: Monthly production time of a single machine, spare parts replacement interval;

[0087] Among them, the monthly single-machine production time is calculated by the monthly single-machine production time calculation model;

[0088] The spare parts replacement interval is obtained from the daily operation data of the equipment. Since the daily operation data of the equipment, such as daily repairs and work records, are saved by the company's equipment management system and used as historical production data, they can be directly called;

[0089] S3.3 The monthly spare parts consumption of a certain model is:

[0090] Monthly spare parts usage forecast value + revised value + planned preventive maintenance times = monthly spare parts usage;

[0091] S3. Spare parts procurement forecast for April:

[0092] Monthly spare parts usage - inventory = monthly spare parts purchase quantity;

[0093] S4 Automatic Purchasing

[0094] Generate purchase orders based on the monthly spare parts purchase volume forecast;

[0095] After the purchase order is approved, it will be uploaded and sent to the procurement system to complete the purchase declaration;

[0096] S5 Data Analysis

[0097] The spare parts usage forecast data, spare parts procurement forecast data and actual data within the set period are analyzed and displayed using visualization methods.

[0098] It should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. Automatic purchasing system for spare parts of cigarette enterprises, Features: include Purchase forecast module and automatic purchase module; The procurement forecast module includes a monthly single-machine production time calculation model and a monthly spare parts usage forecast model; The monthly single-machine production time calculation model is a linear equation calculation model established based on the production plans and production days of different departments / workshops; The monthly spare parts usage prediction model is a BP neural network model: Model parameters: hidden layer is 1, input layer is 1, output layer is 1, and the number of training steps is 1000; Data sources: Monthly production time of a single machine, spare parts replacement interval; The monthly spare parts consumption of a certain model is: Monthly spare parts usage forecast value + revised value + planned preventive maintenance times = monthly spare parts usage; Among them, the correction value is: (purchase volume of spare parts for a certain model in the past year / 12)*5%=correction value; Planned preventive maintenance times: N1+N2+…= planned preventive maintenance times, where N is the planned maintenance times of the equipment using the same spare parts; Monthly spare parts purchase volume forecast for a certain model of spare parts: Monthly spare parts usage - inventory = monthly spare parts purchase quantity; The automatic purchasing module includes a purchase order generating unit and an order sending unit; The purchase order generating unit generates a purchase order according to the purchase forecast result of a certain model of spare parts; The order sending unit uploads the purchase order to the procurement system after it is approved to complete the purchase declaration.

2. The automatic purchasing system for spare parts of cigarette enterprises according to claim 1, Features: The automatic spare parts purchasing system also includes an interaction unit; The interaction module includes an input unit and an analysis unit; The input unit is used for department / workshop selection, production plan input, and planned preventive maintenance times input; The analysis unit is used to analyze the spare parts usage forecast data, spare parts purchase quantity forecast data and actual data within the cycle and display them using visualization means.

3. The automatic purchasing system for spare parts of cigarette enterprises according to claim 1, Features: The monthly single-machine production time calculation model is based on different departments / workshops: Silk-making workshop: planned production batch*(batch weight / rated production flow)=monthly single-machine production time; Rolling and packaging workshop: planned production volume of soft or hard packaging / (planned number of units to be opened*average single machine capacity) = monthly single machine production time; Other departments: planned production volume / daily production capacity = monthly single-machine production time.

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