Steel distribution risk early-warning method of automobile accessory enterprises

An auto parts and risk early warning technology, applied in forecasting, instrumentation, data processing applications, etc., can solve problems such as inability to synchronize updates, inability to automatically measure and pre-warn breakpoints, and lack of calculation models, so as to reduce business risks and avoid resource waste. , the effect of reducing costs

Inactive Publication Date: 2013-12-18
烟台宝井钢材加工有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The two parties lack an effective calculation model to dynamically track the distribution and delivery status of parts. When the production and picking instructions of the parts factory are adjusted, the Kanban cannot update the delivery status of parts synchronously. Automatic calculation and early warning of breakpoints that may occur within the cycle

Method used

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  • Steel distribution risk early-warning method of automobile accessory enterprises
  • Steel distribution risk early-warning method of automobile accessory enterprises

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0030] (1) Select 6 kinds of auto parts, namely rear axle upper part, left beam lower part, right beam upper part, right beam lower part, control arm upper part left and control arm upper part right, numbered i =1, 2, 3, 4, 5, 6;

[0031] (2) order i =1, that is, select the upper plate of the rear axle for calculation;

[0032] (3) Use the year-on-year period ( T =7) The average value of the known demand replaces the predicted daily demand, assuming that the known demand of this cycle is 512.0471 kg, 660.3 kg, 669.06 kg, 540.7 kg, 590.5 kg, 530.2 kg, 667.5 kg , to calculate the daily demand for this cycle is 595.7582 kg;

[0033] (4) The standard deviation of the daily demand for this part is is 69.58228 kg;

[0034] (5) One time unit for this part T Variance of quantity demanded is 33891.86;

[0035] (6) Calculate the normal distribution probability of the enterprise under the 98% demand rate as 0.99, and find out its safety factor z =2.33;

[0036] (7) The calc...

Embodiment 2

[0044] (1) Select 5 kinds of auto parts, namely the lower part of the rear axle, the lower part of the left beam, the upper part of the right beam, the upper part of the control arm left and the upper part of the control arm right, i =1,2,3,4,5;

[0045] (2) order i =1, that is, select the lower part of the rear axle for calculation;

[0046] (3) Use the year-on-year period ( T =7) The average value of the known demand replaces the predicted daily demand, assuming that the known demand of this cycle is 445.7412 kg, 582.8923 kg, 466.3 kg, 563.4 kg, 455.9 kg, 512.3 kg, 557.3 kg , to calculate the daily demand for this cycle is 511.9762 kg;

[0047] (4) The standard deviation of the daily demand for this part is is 56.78289 kilograms;

[0048] (5) One time unit for this part Variance of quantity demanded is 22570.08;

[0049] (6) Calculate the normal distribution probability of the enterprise under the 98% demand rate as 0.99, and find out its safety factor z =2.33; ...

Embodiment 3

[0058] (1) Select 4 kinds of auto parts, which are the lower part of the front beam, the lower part of the left beam, the lower part of the right beam and the upper part of the control arm. i =1,2,3,4;

[0059] (2) order i =1, that is, select the lower part of the front beam for calculation;

[0060] (3) Use the year-on-year period ( T =7) The average value of the known demand replaces the predicted daily demand, assuming that the known demand of this cycle is 2500 kg, 1800 kg, 3000 kg, 5000 kg, 4000 kg, 3500 kg, 2900 kg , to calculate the daily demand for this cycle is 3242.86 kg;

[0061] (4) The standard deviation of the daily demand for this part is is 1043.8026 kg;

[0062] (5) One time unit for this part Variance of quantity demanded is 7.63E+06;

[0063] (6) Calculate the normal distribution probability of the enterprise under the 98% demand rate as 0.99, and find out its safety factor z =2.33;

[0064] (7) The calculated safety stock is 6434.63 kg;

...

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Abstract

The invention discloses a steel distribution risk early-warning method of automobile accessory enterprises. The steel distribution risk early-warning method aims to guarantee stable production of an automobile accessory manufacturer, more accurate safety inventory is obtained by means of demand quantity standard deviation, demand quantity variance in a period, standard normal distribution indicators and other factors, distribution early-warning calculation is performed by means of the safety inventory, the demand quantity and residual inventory, and possible breaking points of parts of various vehicle types can be predicted at future stages. The steel distribution risk early-warning method reduces business risk greatly, avoids waste of resources, and lowers cost effectively.

Description

Technical field: [0001] The invention relates to a steel distribution risk early warning method for auto parts enterprises, and belongs to the field of supply chain distribution early warning calculation. Background technique: [0002] Steel distribution enterprises, as the raw material suppliers of steel used in various auto parts factories, play an important role in the supply and distribution hub in the steel-auto supply chain. Accurate inventory and distribution tracking management can promote the operational efficiency of the entire supply chain and effectively reduce the risk of material outages in parts factories and even automobile OEMs. In the past, after the stamping plan of the parts factory was released, the processing and distribution tracking of steel substrates for each part by steel distribution companies was highly resource-intensive and time-intensive. The two parties need to communicate and confirm the actual production and delivery performance multiple t...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/08G06Q50/28G06Q10/04
Inventor 韩勇邓大坤刘利郭川苏庆堂
Owner 烟台宝井钢材加工有限公司
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