3 research outputs found
A Multi-Phase Approach for Product Hierarchy Forecasting in Supply Chain Management: Application to MonarchFx Inc
Hierarchical time series demands exist in many industries and are often
associated with the product, time frame, or geographic aggregations.
Traditionally, these hierarchies have been forecasted using top-down,
bottom-up, or middle-out approaches. The question we aim to answer is how to
utilize child-level forecasts to improve parent-level forecasts in a
hierarchical supply chain. Improved forecasts can be used to considerably
reduce logistics costs, especially in e-commerce. We propose a novel
multi-phase hierarchical (MPH) approach. Our method involves forecasting each
series in the hierarchy independently using machine learning models, then
combining all forecasts to allow a second phase model estimation at the parent
level. Sales data from MonarchFx Inc. (a logistics solutions provider) is used
to evaluate our approach and compare it to bottom-up and top-down methods. Our
results demonstrate an 82-90% improvement in forecast accuracy using the
proposed approach. Using the proposed method, supply chain planners can derive
more accurate forecasting models to exploit the benefit of multivariate data.Comment: 25 pages, 2 figures, 8 table
Developing Hybrid Machine Learning Models to Assign Health Score to Railcar Fleets for Optimal Decision Making
A large amount of data is generated during the operation of a railcar fleet,
which can easily lead to dimensional disaster and reduce the resiliency of the
railcar network. To solve these issues and offer predictive maintenance, this
research introduces a hybrid fault diagnosis expert system method that combines
density-based spatial clustering of applications with noise (DBSCAN) and
principal component analysis (PCA). Firstly, the DBSCAN method is used to
cluster categorical data that are similar to one another within the same group.
Secondly, PCA algorithm is applied to reduce the dimensionality of the data and
eliminate redundancy in order to improve the accuracy of fault diagnosis.
Finally, we explain the engineered features and evaluate the selected models by
using the Gain Chart and Area Under Curve (AUC) metrics. We use the hybrid
expert system model to enhance maintenance planning decisions by assigning a
health score to the railcar system of the North American Railcar Owner (NARO).
According to the experimental results, our expert model can detect 96.4% of
failures within 50% of the sample. This suggests that our method is effective
at diagnosing failures in railcars fleet.Comment: 21 pages, 7 figures, 3 table