Approximations for the Lead Time Variance: A Forecasting and Inventory Evaluation

2021 ◽  
Author(s):  
Patrick Saoud ◽  
Nikolaos Kourentzes ◽  
John Boylan
Keyword(s):  
Lead Time ◽  
Time Variance

scholarly journals Investing in Lead-Time Variability Reduction in a Quality-Adjusted Inventory Model with Finite-Range Stochastic Lead-Time

2008 ◽  
Vol 2008 ◽  
pp. 1-13 ◽  
Author(s):  
Farrokh Nasri ◽  
Javad Paknejad ◽  
John Affisco
Keyword(s):  
Lead Time ◽  
Variance Reduction ◽  
Inventory Model ◽  
Time Variability ◽  
Defective Items ◽  
Reduction Model ◽  
Time Variance ◽  
Total Inventory ◽  
Truncated Normal ◽  
The Impact

We study the impact of the efforts aimed at reducing the lead-time variability in a quality-adjusted stochastic inventory model. We assume that each lot contains a random number of defective units. More specifically, a logarithmic investment function is used that allows investment to be made to reduce lead-time variability. Explicit results for the optimal values of decision variables as well as optimal value of the variance of lead-time are obtained. A series of numerical exercises is presented to demonstrate the use of the models developed in this paper. Initially the lead-time variance reduction model (LTVR) is compared to the quality-adjusted model (QA) for different values of initial lead-time over uniformly distributed lead-time intervals from one to seven weeks. In all cases where investment is warranted, investment in lead-time reduction results in reduced lot sizes, variances, and total inventory costs. Further, both the reduction in lot-size and lead-time variance increase as the lead-time interval increases. Similar results are obtained when lead-time follows a truncated normal distribution. The impact of proportion of defective items was also examined for the uniform case resulting in the finding that the total inventory related costs of investing in lead-time variance reduction decrease significantly as the proportion defective decreases. Finally, the results of sensitivity analysis relating to proportion defective, interest rate, and setup cost show the lead-time variance reduction model to be quite robust and representative of practice.

The ROLE OF CHINA-PAK ECONOMIC CORRIDOR (CPEC) IN LOGISTIC SYSTEM OF PAKISTAN’S AND CHINA’S

2019 ◽  
Vol 6 (1) ◽  
pp. 48-50
Author(s):  
Ikram Uddin
Keyword(s):  
Persian Gulf ◽  
Lead Time ◽  
Fiber Optic ◽  
Delivery Time ◽  
Fiber Optic Cable ◽  
Total Cost ◽  
Economic Complexity ◽  
Shipping Cost ◽  
The Impact

This study will explain the impact of China-Pak Economic Corridor (CPEC) on logistic system of China and Pakistan. This project is estimated investment of US $90 billion, CPEC project is consists of various sub-projects including energy, road, railway and fiber optic cable but major portion will be spent on energy. This project will start from Kashgar port of china to Gwadar port of Pakistan. Transportation is sub-function of logistic that consists of 44% total cost of logistic system and 20% total cost of production of manufacturing and mainly shipping cost and transit/delivery time are critical for logistic system. According to OEC (The Observing Economic Complexity) currently, china is importing crude oil which 13.4% from Persian Gulf. CPEC will china for lead time that will be reduced from 45 days to 10 days and distance from 2500km to 1300km. This new route will help to china for less transit/deliver time and shipping cost in terms of logistic of china. Pakistan’s transportation will also improve through road, railway and fiber optic cabal projects from Karachi-Peshawar it will have speed 160km per hour and with help of pipeline between Gwadar to Nawabshah gas will be transported from Iran. According to (www.cpec.inf.com) Pakistan logistic industry will grow by US $30.77 billion in the end of 2020.

Rethinking Lead Time Reduction Investment: A Real Options Perspective

2008 ◽  
Author(s):  
Suzanne de Treville ◽  
Lenos Trigeorgis ◽  
Benjamin Avanzi
Keyword(s):  
Real Options ◽  
Lead Time ◽  
Lead Time Reduction ◽  
Time Reduction

scholarly journals Deep learning and cancer biomarkers: recognising lead-time bias

The Lancet ◽  
2021 ◽  
Vol 397 (10270) ◽  
pp. 194
Author(s):  
Michael Bretthauer ◽  
Magnus Løberg ◽  
Øyvind Holme ◽  
Hans-Olov Adami ◽  
Mette Kalager
Keyword(s):  
Deep Learning ◽  
Lead Time ◽  
Cancer Biomarkers ◽  
Lead Time Bias

scholarly journals A Spatiotemporal Convolutional Gated Recurrent Unit Network for Mean Wave Period Field Forecasting

2021 ◽  
Vol 9 (4) ◽  
pp. 383
Author(s):  
Ting Yu ◽  
Jichao Wang
Keyword(s):  
Lead Time ◽  
Wave Period ◽  
Data Driven ◽  
Lead Times ◽  
Proposed Model ◽  
Data Driven Approach ◽  
Mean Wave Period ◽  
Scattering Index ◽  
Gated Recurrent Unit ◽  
Methods Numerical

Mean wave period (MWP) is one of the key parameters affecting the design of marine facilities. Currently, there are two main methods, numerical and data-driven methods, for forecasting wave parameters, of which the latter are widely used. However, few studies have focused on MWP forecasting, and even fewer have investigated it with spatial and temporal information. In this study, correlations between ocean dynamic parameters are explored to obtain appropriate input features, significant wave height (SWH) and MWP. Subsequently, a data-driven approach, the convolution gated recurrent unit (Conv-GRU) model with spatiotemporal characteristics, is utilized to field forecast MWP with 1, 3, 6, 12, and 24-h lead times in the South China Sea. Six points at different locations and six consecutive moments at every 12-h intervals are selected to study the forecasting ability of the proposed model. The Conv-GRU model has a better performance than the single gated recurrent unit (GRU) model in terms of root mean square error (RMSE), the scattering index (SI), Bias, and the Pearson’s correlation coefficient (R). With the lead time increasing, the forecast effect shows a decreasing trend, specifically, the experiment displays a relatively smooth forecast curve and presents a great advantage in the short-term forecast of the MWP field in the Conv-GRU model, where the RMSE is 0.121 m for 1-h lead time.

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