Steel product production control method based on consumption forecast

A production control method and consumption technology, applied in forecasting, manufacturing computing systems, instruments, etc., can solve problems such as inaccurate forecasting, lack of modeling, and inability to combine historical data with real-time information

Active Publication Date: 2013-06-12
上海宝钢钢材贸易有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Sometimes the data of the corresponding month in previous years is also referred to, but there is no modeling, and the experience is relatively large, so it is impossible to scientifically combine historical data with real-time information
inaccurate forecast

Method used

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  • Steel product production control method based on consumption forecast
  • Steel product production control method based on consumption forecast
  • Steel product production control method based on consumption forecast

Examples

Experimental program
Comparison scheme
Effect test

no. 1 example

[0065] In step a), select 4 kinds of auto parts, which are rear door outer panel, rear door floor, front door outer panel and front door floor, numbered i=1, 2, 3, 4.

[0066] In step b), set the auto part number i=1, that is, select the outer panel of the rear door for calculation.

[0067] In step c), the critical coefficient α=0.45 is taken.

[0068] In step d), the production plan for the model A in which the first type of component is located is 17,800 in the next month (May).

[0069] In step e), the first predicted consumption of steel corresponding to the first type of component (ie, rear door outer panel) is calculated from the production plan of A model 1 It is 845 tons of hot-dip galvanized sheets of certain specifications.

[0070] In step f), use this kind of steel consumption regression model to calculate the second predicted consumption M in May i , the regression model of the first type of component (that is, the outer panel of the rear door) is y=-0.109x 5...

no. 2 example

[0078] In step a), select 6 kinds of auto parts, which are respectively the upper part of the left beam, the lower part of the left beam, the upper part of the right beam, the lower part of the right beam, the left part of the upper part of the control arm and the right part of the upper part of the control arm, and the number i= 1, 2, 3, 4, 5, 6.

[0079] In step b), let the auto part number i=1, that is, select the upper piece of the left beam for calculation.

[0080] In step c), the critical coefficient α=0.45 is taken.

[0081] In step d), the production plan for the model B of the first type of component (that is, the upper piece of the left beam) in the next month (July) is 15,470 units.

[0082] In step e), the first predicted consumption R of steel corresponding to the first type of component is calculated by the production plan 1 It is 580 tons of 0.65mm×885mm cold-rolled steel plate.

[0083] In step f), the consumption regression model of this kind of steel is u...

no. 3 example

[0089] In step a), 5 kinds of auto parts are selected, namely the left spring seat, the right spring seat, the upper part of the rear axle, the lower part of the rear axle and the lower part of the control arm, and the numbers i=1, 2, 3, 4, 5.

[0090] In step b), let the auto part number i=1, and select the left spring seat for calculation.

[0091] In step c), the critical coefficient α=0.45 is taken.

[0092] In step d), the production plan for the model C of the first type of component (that is, the left spring seat) in the next month (August) is 23,760 units.

[0093] In step e), the first predicted consumption R of steel corresponding to the first type of component (that is, the left spring seat) is calculated by the production plan 1 It is 710 tons of 0.75mm×1145mm electro-galvanized sheet.

[0094] In step f), the second predicted consumption M in August is calculated according to the regression model i , the regression model of the first type of component (that is,...

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Abstract

The invention discloses a steel product production control method based on consumption forecast. The method comprises: 1) conducting numbering of parts; 2) selecting a part and setting a critical coefficient; 3) calculating first forecast consumption of steel products based on the consumption of the parts; 4) calculating second forecast consumption of the steel products based on a regression model of the steel product consumption; 5) comparing the first forecast consumption with the second forecast consumption, when the first forecast consumption and the second forecast consumption meet a compensation condition, conducting compensation; 6) conducting compensation for the first forecast consumption, and compensation being related to the first forecast consumption and the second forecast consumption; and 7) taking the first forecast consumption after compensated as final forecast consumption, conducting regulating for production of the steel products based on the final forecast consumption so as to enable steel product yield to confirm to the final forecast consumption, returning to the step 2), and selecting the next part, executing the step 2)-7) until processing of all parts being completed.

Description

technical field [0001] The invention relates to the technical field of production control, in particular to a steel production control method based on consumption forecast. Background technique [0002] With the intensification of competition in the steel industry, while continuously improving product quality, various steel production enterprises have gradually begun to pay attention to improving service quality and reducing the overall cost of the supply chain. Among them, in the management of production and sales, the introduction of the VMI model has greatly promoted the operational efficiency of the entire supply chain and effectively reduced storage and other costs. [0003] In the field of automobile manufacturing, steel trading enterprises, as the raw material suppliers of steel for parts and components, are the managers of the output of the supply chain. For steel trading companies, accurate steel consumption forecasts can reduce market instability faced by supply c...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/04
CPCY02P90/30
Inventor 马苏
Owner 上海宝钢钢材贸易有限公司
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