scholarly journals Intermittent demand forecasting for medical consumables with short life cycle using a dynamic neural network during the COVID-19 epidemic

2020 ◽  
Vol 26 (4) ◽  
pp. 3106-3122
Author(s):  
Peipei Liu
Keyword(s):  
Neural Network ◽  
Life Cycle ◽  
Model Selection ◽  
Demand Forecasting ◽  
Seasonal Adjustment ◽  
Intermittent Demand ◽  
Short Life ◽  
Dynamic Neural Network ◽  
Demand Models ◽  
The Impact

Accurate demand forecasting is always critical to supply chain management. However, many uncertain factors in the market make this issue a huge challenge. Especially during the current COVID-19 outbreak, the shortage of certain types of medical consumables has become a global problem. The intermittent demand forecast of medical consumables with a short life cycle brings some new challenges, such as the demand occurring randomly in many time periods with zero demand. In this research, a seasonal adjustment method is introduced to deal with seasonal influences, and a dynamic neural network model with optimized model selection procedure and an appropriate model selection criterion are introduced as the main forecasting models. In addition, in order to reduce the impact of zero demand, it adds some input nodes to the neural network by preprocessing the original input data. Lastly, a modified error measurement method is proposed for performance evaluation. Experimental results show that the proposed forecasting framework is superior to other intermittent demand models.

scholarly journals Forecasting intermittent and sparse time series: A unified probabilistic framework via deep renewal processes

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0259764
Author(s):  
Ali Caner Türkmen ◽  
Tim Januschowski ◽  
Yuyang Wang ◽  
Ali Taylan Cemgil
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Discrete Time ◽  
Predictive Accuracy ◽  
Demand Forecasting ◽  
Trivial Extension ◽  
Renewal Processes ◽  
Probabilistic Forecasting ◽  
Unified Framework ◽  
Intermittent Demand

Intermittency are a common and challenging problem in demand forecasting. We introduce a new, unified framework for building probabilistic forecasting models for intermittent demand time series, which incorporates and allows to generalize existing methods in several directions. Our framework is based on extensions of well-established model-based methods to discrete-time renewal processes, which can parsimoniously account for patterns such as aging, clustering and quasi-periodicity in demand arrivals. The connection to discrete-time renewal processes allows not only for a principled extension of Croston-type models, but additionally for a natural inclusion of neural network based models—by replacing exponential smoothing with a recurrent neural network. We also demonstrate that modeling continuous-time demand arrivals, i.e., with a temporal point process, is possible via a trivial extension of our framework. This leads to more flexible modeling in scenarios where data of individual purchase orders are directly available with granular timestamps. Complementing this theoretical advancement, we demonstrate the efficacy of our framework for forecasting practice via an extensive empirical study on standard intermittent demand data sets, in which we report predictive accuracy in a variety of scenarios.

Impact of Generational Commonality of Short Life Cycle Products in Manufacturing and Remanufacturing Processes

2020 ◽  
Vol 142 (12) ◽  
Author(s):  
Jinju Kim ◽  
Harrison M. Kim
Keyword(s):  
Life Cycle ◽  
Environmental Problems ◽  
Utilization Rate ◽  
Short Life ◽  
Trade Offs ◽  
Technological Advances ◽  
Technological Obsolescence ◽  
New Generation ◽  
The Impact ◽  
Short Life Cycle Products

Abstract Short life cycle products are frequently replaced and discarded, even though they are resource-intensive products. Technological advances and rapid changes in demand have led manufacturers to develop their innovative next-generation products quickly, which not only enables multiple generations to coexist in the market but also speeds up the technological obsolescence of products. Diversity of collected end-of-life (EoL) and rapid technological obsolescence make the effective recovery of EoL products difficult. The low utilization rate of EoL products causes serious environmental problems such as e-waste and waste of natural resources. To deal with the conflict between the technical evolution of products and the promotion of social benefits in solving environmental problems, this paper focuses on the impact of generational commonality effects on the overall production process including manufacturing and remanufacturing. Generational commonality leads to an increase in the efficiency of manufacturing due to reducing related costs. Additionally, from the remanufacturing perspective, the interchangeability between generations can help collect the EoL products needed for remanufacturing. On the other hand, it causes a weakening of the level of performance and technology evolution between generations that significantly affect the demand for short life cycle products. Therefore, this study identifies these trade-offs of generational commonality levels in both manufacturing and remanufacturing based on a quantitative approach. This study finds how different pricing strategies, production plans, and recovery costs are based on the designs of a new generation with a different degree of generational commonality.

scholarly journals Forecasting peak energy demand for smart buildings

2020 ◽  
Author(s):  
Mona A. Alduailij ◽  
Ioan Petri ◽  
Omer Rana ◽  
Mai A. Alduailij ◽  
Abdulrahman S. Aldawood
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Energy Consumption ◽  
Energy Demand ◽  
Arima Model ◽  
Demand Forecasting ◽  
User Preferences ◽  
Machine Learning Techniques ◽  
Peak Energy ◽  
The Impact

AbstractPredicting energy consumption in buildings plays an important part in the process of digital transformation of the built environment, and for understanding the potential for energy savings. This also contributes to reducing the impact of climate change, where buildings need to increase their adaptability and resilience while reducing energy consumption and maintain user comfort. The use of Internet of Things devices for monitoring and control of energy consumption in buildings can take into account user preferences, event monitoring and building optimization. Detecting peak energy demand from historical building data can enable users to manage their energy use more efficiently, while also enabling real-time response strategies (including control and actuation) to known or future scenarios. Several statistical, time series, and machine learning techniques are proposed in this work to predict electricity consumption for five different building types, by using peak demand forecasting to achieve energy efficiency. We have used several indigenous and exogenous variables with a view to test different energy forecasting scenarios. The suggested techniques are evaluated for creating predictive models, including linear Regression, dynamic regression, ARIMA time series, exponential smoothing time series, artificial neural network, and deep neural network. We conduct the analysis on an energy consumption dataset of five buildings from 2014 until 2019. Our results show that for a day ahead prediction, the ARIMA model outperforms the other approaches with an accuracy of 98.91% when executed over a 168 h (1 week) of uninterrupted data for five government buildings.

scholarly journals Short-Term Electricity Demand Forecasting: Impact Analysis of Temperature for Thailand

Energies ◽  
2020 ◽  
Vol 13 (10) ◽  
pp. 2498 ◽  
Author(s):  
Kamal Chapagain ◽  
Somsak Kittipiyakul ◽  
Pisut Kulthanavit
Keyword(s):  
Neural Network ◽  
Demand Forecasting ◽  
Electricity Demand ◽  
Least Square ◽  
Weather Variables ◽  
Forecasting Accuracy ◽  
Regression Methods ◽  
Electricity Demand Forecasting ◽  
Selection Of Variables ◽  
The Impact

Accurate electricity demand forecasting for a short horizon is very important for day-to-day control, scheduling, operation, planning, and stability of the power system. The main factors that affect the forecasting accuracy are deterministic variables and weather variables such as types of days and temperature. Due to the tropical climate of Thailand, the marginal impact of weather variables on electricity demand is worth analyzing. Therefore, this paper primarily focuses on the impact of temperature and other deterministic variables on Thai electricity demand. Accuracy improvement is also considered during model design. Based on the characteristics of demand, the overall dataset is divided into four different subgroups and models are developed for each subgroup. The regression models are estimated using Ordinary Least Square (OLS) methods for uncorrelated errors, and General Least Square (GLS) methods for correlated errors, respectively. While Feed Forward Artificial Neural Network (FF-ANN) as a simple Deep Neural Network (DNN) is estimated to compare the accuracy with regression methods, several experiments conducted for determination of training length, selection of variables, and the number of neurons show some major findings. The first finding is that regression methods can have better forecasting accuracy than FF-ANN for Thailand’s dataset. Unlike much existing literature, the temperature effect on Thai electricity demand is very interesting because of their linear relationship. The marginal impacts of temperature on electricity demand are also maximal at night hours. The maximum impact of temperature during night hours happens at 11 p.m., is 300 MW/ ° C, about 4 % rise in demand while during day hours, the temperature impact is only 10 MW/ ° C to 200 MW/ ° C about 1.4 % to 2.6 % rise.

Analogue-based demand forecasting of short life-cycle products: a regression approach and a comprehensive assessment

2016 ◽  
Vol 55 (8) ◽  
pp. 2336-2350 ◽  
Author(s):  
Mario José Basallo-Triana ◽  
Jesús Andrés Rodríguez-Sarasty ◽  
Hernán Darío Benitez-Restrepo
Keyword(s):  
Life Cycle ◽  
Demand Forecasting ◽  
Comprehensive Assessment ◽  
Short Life ◽  
Regression Approach ◽  
Short Life Cycle Products

The impact of atmospherics on WOM about short life-cycle products: the case of motion pictures

2018 ◽  
Vol 27 (5) ◽  
pp. 471-483 ◽  
Author(s):  
Leonidas Hatzithomas ◽  
Panagiotis Gkorezis ◽  
Athina Y. Zotou ◽  
George Tsourvakas
Keyword(s):  
Life Cycle ◽  
Positive Emotions ◽  
Relevant Literature ◽  
Motion Pictures ◽  
Causal Mechanism ◽  
Short Life ◽  
Content Type ◽  
Key Variables ◽  
The Impact ◽  
Short Life Cycle Products

Purpose This paper aims to empirically examine how atmospherics affect word of mouth (WOM) about the brand. The authors focus primarily on uncovering the causal mechanism in which such effect is serially mediated by both perceived positive emotions evoked by atmospherics and attitude toward the brand. Design/methodology/approach To test the research hypotheses, 314 Greek moviegoers were drafted to participate in a survey. Data were analysed using confirmatory factor analysis (AMOS) and the SPSS macro (PROCESS tool). The model was applied to motion pictures, as they provide a particularly good example of short life-cycle products. Findings Findings indicate that atmospherics are related to WOM about the brand through perceived emotions evoked by atmospherics and, in turn, attitude toward the brand. Research limitations/implications The present study extends the relevant literature by providing both direct and indirect links between atmospherics and WOM about a brand. Practical implications The model of the present study could be applied to other short life-cycle products that share key characteristics with motion pictures. Moreover, the present study increases movie producers and exhibitors’ understanding of the effects of theatre atmospherics on WOM about the movie and leads to practical suggestions and implications. Originality/value WOM is one of the key variables that can affect the profitability of short life-cycle products. To date, there was no evidence that atmospherics can influence WOM about a short life-cycle product.

scholarly journals Neural Network with Specialized Knowledge for Forecasting Intermittent Demand

2020 ◽  
Author(s):  
Alexandre Crepory Abbott de Oliveira ◽  
Jéssica Mendes Jorge ◽  
Andrea Cristina dos Santos ◽  
Geraldo Pereira Rocha Filho
Keyword(s):  
Neural Network ◽  
Demand Forecasting ◽  
Performance Measure ◽  
Specialized Knowledge ◽  
Intermittent Demand ◽  
Forecasting Methods ◽  
The Neural Network ◽  
Comparison Results ◽  
Hidden Layer ◽  
Demand Seasonality

Demand forecasting is an essential part of an efficient inventory control system. However, when the demand has an intermittent or lumpy behavior, forecasting it becomes a challenging task. Several methods have been developed to solve this issue, but nonetheless, they only consider the information about the occurrence of demand, failing to assess the drivers of the data behavior. With the current digitalization of the industry, more data is available and, therefore, the chances of finding a causal relationship between the available data and the demand increases. Considering that, this paper proposes a single-hidden layer neural network for forecasting irregularly spaced time series with attributes conveying information about the past demand, seasonality of the data and specialized knowledge about the process. The neural network proposed is compared with benchmark neural networks and traditional forecasting methods for intermittent demand using three different performance measures on actual demand data from an industry operating in the aircraft maintenance sector. Statistical analysis is conducted on comparison results to identify significant differences in the forecasting methods according to each performance measure.

Sign in / Sign up

Export Citation Format

Share Document