scholarly journals The application of demand forecast models: case study on bearing factory

2018 ◽  
Vol 4 (15) ◽  
Author(s):  
Bruno Santos Correa ◽  
Rosivan Cunha da Silva ◽  
Maílson Batista de Vilhena ◽  
Ana Paula de Souza e Silva
Keyword(s):  
Demand Forecast ◽  
Bearing Factory ◽  
Forecast Models

scholarly journals The application of demand forecast models: case study on bearing factory

2018 ◽  
Vol 4 (15) ◽  
Author(s):  
Bruno Santos Correa ◽  
Rosivan Cunha da Silva ◽  
Maílson Batista de Vilhena ◽  
Ana Paula de Souza e Silva
Keyword(s):  
Demand Forecast ◽  
Bearing Factory ◽  
Forecast Models

scholarly journals Landslide early warning based on failure forecast models: the example of Mt. de La Saxe rockslide, northern Italy

2015 ◽  
Vol 3 (2) ◽  
pp. 1511-1525 ◽  
Author(s):  
A. Manconi ◽  
D. Giordan
Keyword(s):  
Early Warning ◽  
Early Warning Systems ◽  
Northern Italy ◽  
Warning Systems ◽  
Surface Displacements ◽  
Forecast Models ◽  
Landslide Early Warning ◽  
Critical Phases ◽  
Temporal Windows

Abstract. We investigate the use of landslide failure forecast models by exploiting near-real-time monitoring data. Starting from the inverse velocity theory, we analyze landslide surface displacements on different temporal windows, and apply straightforward statistical methods to obtain confidence intervals on the estimated time of failure. Here we describe the main concepts of our method, and show an example of application to a real emergency scenario, the La Saxe rockslide, Aosta Valley region, northern Italy. Based on the herein presented case study, we identify operational thresholds based on the reliability of the forecast models, in order to support the management of early warning systems in the most critical phases of the landslide emergency.

scholarly journals Electrical power supply consumption in education sector and energy audit: Case study of University of Jos

2021 ◽  
Vol 40 (2) ◽  
pp. 321-328
Author(s):  
B.I. Gwaivangmin
Keyword(s):  
Electrical Power ◽  
Electrical Energy ◽  
Electricity Supply ◽  
Energy Audit ◽  
Demand Forecast ◽  
Generating Sets ◽  
Load Demand ◽  
The Cost ◽  
The University

Electricity supply has been identified as the key constraint to industrialization and economic development in Nigeria. The unbundling of the power sector was aimed at boosting electricity supply, this effort has yielded some appreciable results, but not very significant. As a result of the low power generation and distribution, Nigeria’s federal government is working towards solving the prevailing problems of inadequate power in some key sectors by building power generating plants in some of the institutions of learning in the country. This paper looks at the determinants of electrical energy consumption and electrical energy audit, a case study of the University of Jos. The load profiles demand survey, load demand forecast and other important factors were investigated. The result revealed that there is available power of 22–23 hours from the national grid and the balance 1–2 hours of power is supplied by the generating sets, good savings in the cost of diesel and maintenance. An annual excess of 2,199,900 kWH is enjoyed by the university over the national per capita power consumption.

scholarly journals STOCKS MANAGEMENT THROUGH APPLICATION OF DEMAND FORECAST METHODS: A CASE STUDY

2016 ◽  
Vol 7 (5) ◽  
pp. 699-713
Author(s):  
Lucas Lopes Filholino Rodrigues ◽  
Igor Henrique Inácio de Oliveira ◽  
Maurílio Fagundes Alexandre ◽  
Rodrigo Rodrigues Castorani ◽  
Celso Jacubavicius
Keyword(s):  
Demand Forecast

scholarly journals Inland water transport development possibilities – case study of lower Vistula river

2015 ◽  
Vol 35 (3) ◽  
pp. 53-62 ◽  
Author(s):  
Tomasz Nowakowski ◽  
Jan Kulczyk ◽  
Emilia Skupień ◽  
Agnieszka Tubis ◽  
Sylwia Werbińska-Wojciechowska
Keyword(s):  
Water Transport ◽  
Low Cost ◽  
Environmentally Friendly ◽  
External Costs ◽  
Demand Forecast ◽  
Inland Water ◽  
Vistula River ◽  
Transportation Modes ◽  
Inland Water Transport

Among different transportation modes, inland water transport is recognized as a low-cost, environmentally friendly way of transporting. The use of this mode in Poland encounters many challenges. Thus, the investigation of development possibilities by analysing the revitalization profitability and navigability restoration of Lower Vistula river should be explored. Following this, the article includes the summary of obtained results of the project INWAPO carrying out and regards development of infrastructure and sea/river ports, demand forecast for transportation, external costs estimation and the main benefits from lower Vistula river revitalization. The main analysis is done with the assumption of IV (or higher) navigable class of the Vistula river.

scholarly journals Improving warehouse labour efficiency by intentional forecast bias

2018 ◽  
Vol 48 (1) ◽  
pp. 93-110 ◽  
Author(s):  
Thai Young Kim ◽  
Rommert Dekker ◽  
Christiaan Heij
Keyword(s):  
Capacity Planning ◽  
Labour Productivity ◽  
Forecast Model ◽  
Consumer Electronics ◽  
Demand Forecast ◽  
Content Type ◽  
Forecast Bias ◽  
Optimal Bias ◽  
Demand Forecasts

Purpose The purpose of this paper is to show that intentional demand forecast bias can improve warehouse capacity planning and labour efficiency. It presents an empirical methodology to detect and implement forecast bias. Design/methodology/approach A forecast model integrates historical demand information and expert forecasts to support active bias management. A non-linear relationship between labour productivity and forecast bias is employed to optimise efficiency. The business analytic methods are illustrated by a case study in a consumer electronics warehouse, supplemented by a survey among 30 warehouses. Findings Results indicate that warehouse management systematically over-forecasts order sizes. The case study shows that optimal bias for picking and loading is 30-70 per cent with efficiency gains of 5-10 per cent, whereas the labour-intensive packing stage does not benefit from bias. The survey results confirm productivity effects of forecast bias. Research limitations/implications Warehouse managers can apply the methodology in their own situation if they systematically register demand forecasts, actual order sizes and labour productivity per warehouse stage. Application is illustrated for a single warehouse, and studies for alternative product categories and labour processes are of interest. Practical implications Intentional forecast bias can lead to smoother workflows in warehouses and thus result in higher labour efficiency. Required data include historical data on demand forecasts, order sizes and labour productivity. Implementation depends on labour hiring strategies and cost structures. Originality/value Operational data support evidence-based warehouse labour management. The case study validates earlier conceptual studies based on artificial data.

scholarly journals Landslide early warning based on failure forecast models: the example of the Mt. de La Saxe rockslide, northern Italy

2015 ◽  
Vol 15 (7) ◽  
pp. 1639-1644 ◽  
Author(s):  
A. Manconi ◽  
D. Giordan
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Early Warning Systems ◽  
Northern Italy ◽  
Surface Displacements ◽  
Forecast Models ◽  
Landslide Early Warning ◽  
Unstable Slope ◽  
Critical Phases

Abstract. We apply failure forecast models by exploiting near-real-time monitoring data for the La Saxe rockslide, a large unstable slope threatening Aosta Valley in northern Italy. Starting from the inverse velocity theory, we analyze landslide surface displacements automatically and in near real time on different temporal windows and apply straightforward statistical methods to obtain confidence intervals on the estimated time of failure. Here, we present the result obtained for the La Saxe rockslide, a large unstable slope located in Aosta Valley, northern Italy. Based on this case study, we identify operational thresholds that are established on the reliability of the forecast models. Our approach is aimed at supporting the management of early warning systems in the most critical phases of the landslide emergency.

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