Zero Inventory – ein überholtes Konzept?

2017 ◽  
Vol 112 (10) ◽  
pp. 666-669
Author(s):  
Edgar Heim ◽  
Frederik Miszak ◽  
Michael Lustenberger
Keyword(s):  
Zero Inventory

Guidelines for setup-cost reduction programs to achieve zero inventory

1990 ◽  
Vol 9 (1) ◽  
pp. 85-100 ◽  
Author(s):  
James R. Freeland ◽  
John P. Leschke ◽  
Elliott N. Weiss
Keyword(s):  
Cost Reduction ◽  
Setup Cost ◽  
Setup Cost Reduction ◽  
Zero Inventory

scholarly journals Many steps towards zero inventory

1992 ◽  
Vol 59 (3) ◽  
pp. 359-369 ◽  
Author(s):  
Herbert J. Grünwald ◽  
Leonard Fortuin
Keyword(s):  
Zero Inventory

Towards Zero-Warehousing Smart Manufacturing from Zero-Inventory Just-In-Time production

2020 ◽  
Vol 64 ◽  
pp. 101932 ◽  
Author(s):  
Zhongyuan Lyu ◽  
Peng Lin ◽  
Daqiang Guo ◽  
George Q. Huang
Keyword(s):  
Smart Manufacturing ◽  
Just In Time ◽  
Time Production ◽  
Zero Inventory

Research on Prediction Model of Car Sales Based on Grey Neural Network

2014 ◽  
Vol 889-890 ◽  
pp. 1569-1573
Author(s):  
Shou Qi Cao ◽  
Ya Wen Zhu
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Small Sample ◽  
Just In Time ◽  
Practical Application ◽  
Correlation Data ◽  
Sample Data ◽  
Management Technology ◽  
Car Sales ◽  
Zero Inventory

Nowadays, zero inventory and just in time production and management technology are got attention by more and more domestic and foreign automobile enterprises. Accurate prediction of car sales is the key technology for ensuring zero inventory and product planning of enterprises. In this paper, a prediction model about car sales based on grey neural network was researched. Combining the advantages of grey model in the way of dealing of small sample data, and BP neural network in finding regularity of weak correlation data, the accuracy of the prediction results was improved. Finally, the model was validated by practical application of automobile enterprise production data.

The Analysis of Zero Inventory Drift Variants Based on Simple and General Order-Up-To Policies

2010 ◽  
Vol 1 (3) ◽  
pp. 37-52
Author(s):  
Jianing He ◽  
Haibo Wang
Keyword(s):  
Analysis Of Variance ◽  
Lead Time ◽  
Minimum Mean Square Error ◽  
Lead Times ◽  
Control Engineering ◽  
Final Value ◽  
Demand Pattern ◽  
General Order ◽  
The Stability ◽  
Zero Inventory

In this paper, simple and general Order-Up-To (OUT) models with Minimum Mean Square Error (MMSE) forecast for the AR(1) demand pattern are introduced in the control engineering perspective. Important insights about lead-time misidentification are derived from the analysis of variance discrepancy. By applying the Final Value Theorem (FVI), a final value offset (i.e., inventory drift) is proved to exist and can be measured even though the actual lead-time is known. In this regard, to eliminate the inherent offset and keep the system variances acceptable, two kinds of zero inventory drift variants based on the general OUT model are presented. The analysis of variance amplification suggests lead-times should always be estimated conservatively in variant models. The stability conditions for zero inventory drift variants are evaluated in succession and some valuable attributes of the new variants are illustrated via spreadsheet simulation under the assumption that lead-time misidentification is inevitable.

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