Predicting Purchase Orders Delivery Times Using Regression Models With Dimension Reduction

2018 ◽  
Author(s):  
Jundi Liu ◽  
Steven Hwang ◽  
Walter Yund ◽  
Linda Ng Boyle ◽  
Ashis G. Banerjee
Keyword(s):  
Dimension Reduction ◽  
Information Exchange ◽  
Raw Materials ◽  
Supervised Machine Learning ◽  
Categorical Variables ◽  
Prediction Errors ◽  
Limited Information ◽  
Time Estimates ◽  
Delivery Times ◽  
Effective Performance

In current supply chain operations, the transactions among suppliers and original equipment manufacturers (OEMs) are sometimes inefficient and unreliable due to limited information exchange and lack of knowledge about the supplier capabilities. For the OEMs, majority of downstream operations are sequential, requiring the availabilities of all the parts on time to ensure successful executions of production schedules. Therefore, accurate prediction of the delivery times of purchase orders (POs) is critical to satisfying these requirements. However, such prediction is challenging due to the suppliers’ distributed locations, time-varying capabilities and capacities, and unexpected changes in raw materials procurements. We address some of these challenges by developing supervised machine learning models in the form of Random Forests and Quantile Regression Forests that are trained on historical PO transactional data. Further, given the fact that many predictors are categorical variables, we apply a dimension reduction method to identify the most influential category levels. Results on real-world OEM data show effective performance with substantially lower prediction errors than supplier-provided delivery time estimates.

A Predictive Analytics Tool to Provide Visibility Into Completion of Work Orders in Supply Chain Systems

2020 ◽  
Vol 20 (3) ◽  
Author(s):  
Jundi Liu ◽  
Steven Hwang ◽  
Walter Yund ◽  
Joel D. Neidig ◽  
Scott M. Hartford ◽  
...  
Keyword(s):  
Supply Chain ◽  
Real World ◽  
Information Exchange ◽  
Prediction Models ◽  
Predictive Analytics ◽  
Ease Of Use ◽  
Categorical Variables ◽  
Limited Information ◽  
Prediction Problems ◽  
Work Orders

Abstract In current supply chain operations, original equipment manufacturers (OEMs) procure parts from hundreds of globally distributed suppliers, which are often small- and medium-scale enterprises (SMEs). The SMEs also obtain parts from many other dispersed suppliers, some of whom act as sole sources of critical parts, leading to the creation of complex supply chain networks. These characteristics necessitate having a high degree of visibility into the flow of parts through the networks to facilitate decision making for OEMs and SMEs, alike. However, such visibility is typically restricted in real-world operations due to limited information exchange among the buyers and suppliers. Therefore, we need an alternate mechanism to acquire this kind of visibility, particularly for critical prediction problems, such as purchase orders deliveries and sales orders fulfillments, together referred as work orders completion times. In this paper, we present one such surrogate mechanism in the form of supervised learning, where ensembles of decision trees are trained on historical transactional data. Furthermore, since many of the predictors are categorical variables, we apply a dimension reduction method to identify the most influential category levels. Results on real-world supply chain data show effective performance with substantially lower prediction errors than the original completion time estimates. In addition, we develop a web-based visibility tool to facilitate the real-time use of the prediction models. We also conduct a structured usability test to customize the tool interface. The testing results provide multiple helpful suggestions on enhancing the ease-of-use of the tool.

A Hybrid Statistical Method for Accurate Prediction of Supplier Delivery Times of Aircraft Engine Parts

2015 ◽  
Author(s):  
Ashis Gopal Banerjee ◽  
Walter Yund ◽  
Dan Yang ◽  
Peter Koudal ◽  
John Carbone ◽  
...  
Keyword(s):  
Statistical Method ◽  
Aircraft Engine ◽  
Accurate Prediction ◽  
Distribution Model ◽  
Prediction Errors ◽  
Hybrid Form ◽  
Delivery Times ◽  
Multivariate Gamma Distribution ◽  
Transportation Times ◽  
Delivery Dates

Aircraft engine assembly operations require thousands of parts provided by several geographically distributed suppliers. A majority of the operation steps are sequential, necessitating the availability of all the parts at appropriate times for these steps to be completed successfully. Thus, being able to accurately predict the availabilities of parts based on supplier deliveries is critical to minimizing the delays in meeting the customer demands. However, such accurate prediction is challenging due to the large lead times of these parts, limited knowledge of supplier capacities and capabilities, macroeconomic trends affecting material procurement and transportation times, and unreliable delivery date estimates provided by the suppliers themselves. We address these challenges by developing a statistical method that learns a hybrid stepwise regression — generalized multivariate gamma distribution model from historical transactional data on closed part purchase orders and is able to infer part delivery dates sufficiently before the supplier-promised delivery dates for open purchase orders. The hybrid form of the model makes it robust to data quality and short-term temporal effects as well as biased toward overestimating rather than underestimating the part delivery dates. Test results on real-world purchase orders demonstrate effective performance with low prediction errors and constantly high ratios of true positive to false positive predictions.

scholarly journals Probable evidence of a Middle Palaeolithic site in the northern parts of the Susiana Plain, Khuzestan, Iran

2019 ◽  
Vol 46 ◽  
pp. 424-433
Author(s):  
Saeid Bahramiyan
Keyword(s):  
Raw Materials ◽  
Middle Palaeolithic ◽  
Limited Information ◽  
Zagros Mountains ◽  
Significant Progress ◽  
Khuzestan Province ◽  
Different Types ◽  
Northern Regions ◽  
Considerable Body ◽  
The Karkheh River

There is a considerable body of studies regarding the activities of the Pleistocene human population in the Zagros and Alborz regions of Iran, as well as significant progress in the Palaeolithic studies in other regions, such as the foothills, plains and deserts’ margins. However, some of these peripheral regions and foothills are still neglected, and the information about the Palaeolithic period in these areas is limited. Khuzestan province, especially its northern regions, is one of these unstudied regions, yet the limited information about this region seems very interesting. Khervali, located on the western foothills of the Zagros Mountains and on the northern heights of Susa, nearby the western bank of the Karkheh River, is one of the few Palaeolithic sites identified in recent years. The site was identified in 2012 and was systemically surveyed. Due to the extension of the site and the distribution of the artefacts, sampling all the site was not feasible, therefore, four sections of the site were chosen for taking the samples and a total of 330 stone artefacts were collected. The results of the techno-typology analyses, as well as the frequency of the flakes, the Levallois samples and different types of scrapers, revealed that the artefacts date to the middle Palaeolithic period, with considerable access to the local raw materials.

scholarly journals Sequential Updating of Quantitative Requirements for Increased Flexibility in Robust Systems Design

2019 ◽  
Vol 1 (1) ◽  
pp. 3531-3540
Author(s):  
Matthias Funk ◽  
Marcus Jautze ◽  
Manfred Strohe ◽  
Markus Zimmermann
Keyword(s):  
Information Exchange ◽  
Systems Design ◽  
Distributed Teams ◽  
Limited Information ◽  
Design Variables ◽  
Decision Variables ◽  
Subsystem Design ◽  
Sequential Updating ◽  
Early Decision ◽  
Independent Design

AbstractIn early development stages of complex systems, interacting subsystems (including components) are often designed simultaneously by distributed teams with limited information exchange. Distributed development becomes possible by assigning teams independent design goals expressed as quantitative requirements equipped with tolerances to provide flexibility for design: so-called solution-spaces are high-dimensional sets of permissible subsystem properties on which requirements on the system performance are satisfied. Edges of box-shaped solution spaces are permissible intervals serving as decoupled (mutually independent) requirements for subsystem design variables. Unfortunately, decoupling often leads to prohibitively small intervals. In so-called solution-compensation spaces, permissible intervals for early-decision variables are increased by a compensation mechanism using late-decision variables. This paper presents a multi-step development process where groups of design variables successively change role from early-decision to late-decision type in order to maximize flexibility. Applying this to a vehicle chassis design problem demonstrates the effectiveness of the approach.

scholarly journals Testing Novel Portland Cement Formulations with Carbon Nanotubes and Intrinsic Properties Revelation: Nanoindentation Analysis with Machine Learning on Microstructure Identification

Nanomaterials ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 645 ◽  
Author(s):  
Georgios Konstantopoulos ◽  
Elias P. Koumoulos ◽  
Costas A. Charitidis
Keyword(s):  
Machine Learning ◽  
Carbon Nanotubes ◽  
Portland Cement ◽  
Information Exchange ◽  
Supervised Machine Learning ◽  
Multidimensional Space ◽  
Knowledge Gain ◽  
Phase Identification ◽  
Structure Property ◽  
Local Optima

Nanoindentation was utilized as a non-destructive technique to identify Portland Cement hydration phases. Artificial Intelligence (AI) and semi-supervised Machine Learning (ML) were used for knowledge gain on the effect of carbon nanotubes to nanomechanics in novel cement formulations. Data labelling is performed with unsupervised ML with k-means clustering. Supervised ML classification is used in order to predict the hydration products composition and 97.6% accuracy was achieved. Analysis included multiple nanoindentation raw data variables, and required less time to execute than conventional single component probability density analysis (PDA). Also, PDA was less informative than ML regarding information exchange and re-usability of input in design predictions. In principle, ML is the appropriate science for predictive modeling, such as cement phase identification and facilitates the acquisition of precise results. This study introduces unbiased structure-property relations with ML to monitor cement durability based on cement phases nanomechanics compared to PDA, which offers a solution based on local optima of a multidimensional space solution. Evaluation of nanomaterials inclusion in composite reinforcement using semi-supervised ML was proved feasible. This methodology is expected to contribute to design informatics due to the high prediction metrics, which holds promise for the transfer learning potential of these models for studying other novel cement formulations.

scholarly journals Comparison of Implicit vs. Explicit Regime Identification in Machine Learning Methods for Solar Irradiance Prediction

Energies ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 689 ◽  
Author(s):  
Tyler McCandless ◽  
Susan Dettling ◽  
Sue Ellen Haupt
Keyword(s):  
Machine Learning ◽  
Solar Power ◽  
Network Models ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Validation Dataset ◽  
Prediction Errors ◽  
Learning Approaches ◽  
Power Prediction ◽  
Neural Network Models

This work compares the solar power forecasting performance of tree-based methods that include implicit regime-based models to explicit regime separation methods that utilize both unsupervised and supervised machine learning techniques. Previous studies have shown an improvement utilizing a regime-based machine learning approach in a climate with diverse cloud conditions. This study compares the machine learning approaches for solar power prediction at the Shagaya Renewable Energy Park in Kuwait, which is in an arid desert climate characterized by abundant sunshine. The regime-dependent artificial neural network models undergo a comprehensive parameter and hyperparameter tuning analysis to minimize the prediction errors on a test dataset. The final results that compare the different methods are computed on an independent validation dataset. The results show that the tree-based methods, the regression model tree approach, performs better than the explicit regime-dependent approach. These results appear to be a function of the predominantly sunny conditions that limit the ability of an unsupervised technique to separate regimes for which the relationship between the predictors and the predictand would differ for the supervised learning technique.

Planetree Patient-Centered Care and the Healing Arts

2001 ◽  
Vol 7 (1) ◽  
pp. 17-19 ◽  
Author(s):  
Susan B. Frampton
Keyword(s):  
San Francisco ◽  
Information Exchange ◽  
Medical Center ◽  
Academic Medical Center ◽  
Hospital Staff ◽  
Holistic Health ◽  
Limited Information ◽  
Patient Centered ◽  
Centered Care ◽  
Healing Arts

In 1985, one of the most far-reaching experiments in the realm of holistic health was launched. The first of five Planetree Model Hospitals opened at a large academic medical center in San Francisco. Everything in the unique medical-surgical unit was designed with the patient's perspective in mind. The unit was the culmination of many years of work by a group of visionaries inspired, fittingly, by a single patient. This patient had undergone a traumatic, though not unusual, hospital stay. Limited information exchange between patient and providers, limited visiting hours, imper sonal treatment by hospital staff, and harsh institutional surroundings had resulted in anything but a healing experience. Motivated by a vision of what a hospital could be, Planetree was founded with the mission to change the way health care was delivered, and to personalize, humanize, and demystify the patient experience.

scholarly journals МОДЕЛЮВАННЯ ХІМІКО-ТЕХНОЛОГІЧНИХ ПРОЦЕСІВ У SCADA ЗА ДОПОМОГОЮ ТЕХНОЛОГІЇ OPEN PLATFORM COMMUNICATIONS

2020 ◽  
pp. 59-66
Author(s):  
Ігор Леонідович Левчук ◽  
Олег Петрович Мисов ◽  
Ксенія Олексіївна Фесенко ◽  
Антон Романович Шейкус
Keyword(s):  
Mathematical Model ◽  
Control System ◽  
Mathematical Models ◽  
Information Exchange ◽  
Raw Materials ◽  
Synthesis Process ◽  
Technological Processes ◽  
Open Platform ◽  
Model Control ◽  
Opc Technology

The subject of study in the article are methods for integrating mathematical models of chemical-technological processes implemented in universal modeling programs into modern SCADA systems for developing and improving methods for controlling these processes. The goal is to develop a control system for the synthesis of acetylene in a kinetic reactor, based on a computer model created in universal modeling programs and integrated into SCADA using open platform communications (OPC) technology. Tasks: to create a mathematical model of the process of synthesis of acetylene based on the selected universal modeling program; to develop a way to integrate the resulting model into modern SCADA using OPC technology; to develop in SCADA a control system for the process of synthesis of acetylene according to a mathematical model as part of a functional human-machine interface and control subsystem algorithms; get transient graphs and prove the efficiency of the control system. Conduct a process study using a mathematical model. The methods used are computer simulation of technological processes; OPC technology; SCADA based management. The following results are obtained. A control system for the acetylene synthesis process based on SCADA Trace-Mode and a mathematical model implemented in the ChemCAD package has been developed, while the model - control system information exchange is implemented based on OPC technology. Checked and proved the efficiency of the resulting control system. A mathematical study of the process was carried out, an experimental dependence of the yield of the final product, acetylene, on the temperature, and consumption of raw materials at the inlet of the reactor was established. Conclusions. The novelty of the results is as follows. A new method is proposed for integrating mathematical models implemented in the ChemCAD modeling package into modern SCADA, based on OPC technology. A study of the process of acetylene synthesis by a mathematical model was carried out, experimental dependences of the acetylene yield on temperature and ethylene consumption at the inlet of the synthesis reactor were obtained. An analysis of the obtained experimental dependences showed the need to use cascade control algorithms to increase the efficiency of controlling the process of acetylene synthesis in a kinetic reactor.

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