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

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.

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

Archives of Transport ◽

10.5604/08669546.1185192 ◽

2015 ◽

Vol 35 (3) ◽

pp. 53-62 ◽

Cited By ~ 2

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.

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Improving warehouse labour efficiency by intentional forecast bias

International Journal of Physical Distribution & Logistics Management ◽

10.1108/ijpdlm-10-2017-0313 ◽

2018 ◽

Vol 48 (1) ◽

pp. 93-110 ◽

Cited By ~ 3

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.

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Agricultural Machinery Spare Parts Demand Forecast Based on BP Neural Network

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.635-637.1822 ◽

2014 ◽

Vol 635-637 ◽

pp. 1822-1825 ◽

Cited By ~ 4

Author(s):

Yao Guang Hu ◽

Shuo Sun ◽

Jing Qian Wen

Keyword(s):

Neural Network ◽

Bp Neural Network ◽

Rapid Development ◽

Spare Parts ◽

Agricultural Machinery ◽

Demand Forecast ◽

The Third ◽

Neural Network Prediction ◽

Network Prediction

With the rapid development of agricultural machinery, forecasting the demand for spare parts is essential to ensure timely maintenance of agricultural machinery. Based on features of spare parts, BP neural network is chosen to forecast the demand. First, this paper analyzes factors that affect the demand for spare parts. Second, steps and processes of neural network prediction are described. The third part of this paper is case study based on certain brand of agricultural machinery spare parts. BP neural network turns out suitable for forecasting the demand for spare parts.

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Service Demand Forecast for an Inland Waterway Service Area: A Case Study on the Grand Canal, China

ICTE 2015 ◽

10.1061/9780784479384.041 ◽

2015 ◽

Author(s):

Peng Liao

Keyword(s):

Service Area ◽

Demand Forecast ◽

Service Demand ◽

Grand Canal ◽

Inland Waterway ◽

The Grand Canal

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Unconstrained Estimation of Multitype Car Rental Demand

Applied Sciences ◽

10.3390/app11104506 ◽

2021 ◽

Vol 11 (10) ◽

pp. 4506

Author(s):

Yazao Yang ◽

Avishai (Avi) Ceder ◽

Weiyong Zhang ◽

Haodong Tang

Keyword(s):

Stated Preference ◽

Revealed Preference ◽

Repair Process ◽

Discrete Choice Models ◽

Difficult Problem ◽

Average Error ◽

Demand Forecast ◽

Car Rental ◽

Future Demand

The unconstrained demand forecast for car rentals has become a difficult problem for revenue management due to the need to cope with a variety of rental vehicles, the strong subjective desires and requests of customers, and the high probability of upgrading and downgrading circumstances. The unconstrained demand forecast mainly includes repairing of constrained historical demand and forecasting of future demand. In this work, a new methodology is developed based on multiple discrete choice models to obtain customer choice preference probabilities and improve a known spill model, including a repair process of the unconstrained demand. In addition, the linear Holt–Winters model and the nonlinear backpropagation neural network are combined to predict future demand and avoid excessive errors caused by a single method. In a case study, we take advantage of a stated preference and a revealed preference survey and use the variable precision rough set to obtain factors and weights that affect customer choices. In this case study and based on a numerical example, three forecasting methods are compared to determine the car rental demand of the next time cycle. The comparison with real demand verifies the feasibility and effectiveness of the hybrid forecasting model with a resulting average error of only 3.06%.

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Demand Forecast of NFC Mobile Users – A Case Study of Serbian Market

PROMET - Traffic&Transportation ◽

10.7307/ptt.v30i5.2683 ◽

2018 ◽

Vol 30 (5) ◽

pp. 513-524

Author(s):

Bojan Stanivuković ◽

Valentina Radojičić ◽

Dejan Marković ◽

Mladenka Blagojević

Keyword(s):

Near Field ◽

Communication Technologies ◽

Radio Communication ◽

Demand Forecast ◽

Business Services ◽

Communication Infrastructure ◽

Novel Approach ◽

Range Type ◽

Bass Diffusion Model

Near Field Communication (NFC) is a very short-range type of radio communication that is compatible with other contactless communication technologies. It provides enormous possibilities, particularly given that it does not require any particular communication infrastructure. NFC technology has found possible application in contactless cards and mobile phone devices as a communication infrastructure which provides a platform for the development of NFC-based business services. This paper proposes a novel approach to forecasting the number of new users of NFC mobile phones based on fuzzy logic and the Norton-Bass diffusion model. The proposed approach is demonstrated through the case study.

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