68 research outputs found
Investigating the Demand for Short-shelf Life Food Products for SME Wholesalers
Accurate forecasting of fresh produce demand is one the challenges faced by Small Medium Enterprise (SME) wholesalers. This paper is an attempt to understand the cause for the high level of variability such as weather, holidays etc., in demand of SME wholesalers. Therefore, understanding the significance of unidentified factors may improve the forecasting accuracy. This paper presents the current literature on the factors used to predict demand and the existing forecasting techniques of short shelf life products. It then investigates a variety of internal and external possible factors, some of which is not used by other researchers in the demand prediction process. The results presented in this paper are further analysed using a number of techniques to minimize noise in the data. For the analysis past sales data (January 2009 to May 2014)
from a UK based SME wholesaler is used and the results presented are limited to product ‘Milk’ focused on café’s in derby. The correlation analysis is done to check the dependencies of variability factor on the actual demand. Further PCA analysis is done to understand the significance of factors identified using correlation. The PCA results suggest that the cloud cover, weather summary and temperature are the most significant factors that can be used in forecasting the demand. The correlation of the above three factors increased relative to monthly and becomes more stable compared to the weekly and daily demand
Retail forecasting: research and practice
This paper first introduces the forecasting problems faced by large retailers, from the strategic to the operational, from the store to the competing channels of distribution as sales are aggregated over products to brands to categories and to the company overall. Aggregated forecasting that supports strategic decisions is discussed on three levels: the aggregate retail sales in a market, in a chain, and in a store. Product level forecasts usually relate to operational decisions where the hierarchy of sales data across time, product and the supply chain is examined. Various characteristics and the influential factors which affect product level retail sales are discussed. The data rich environment at lower product hierarchies makes data pooling an often appropriate strategy to improve forecasts, but success depends on the data characteristics and common factors influencing sales and potential demand. Marketing mix and promotions pose an important challenge, both to the researcher and the practicing forecaster. Online review information too adds further complexity so that forecasters potentially face a dimensionality problem of too many variables and too little data. The paper goes on to examine evidence on the alternative methods used to forecast product sales and their comparative forecasting accuracy. Many of the complex methods proposed have provided very little evidence to convince as to their value, which poses further research questions. In contrast, some ambitious econometric methods have been shown to outperform all the simpler alternatives including those used in practice. New product forecasting methods are examined separately where limited evidence is available as to how effective the various approaches are. The paper concludes with some evidence describing company forecasting practice, offering conclusions as to the research gaps but also the barriers to improved practice
Forecasting aggregate retail sales : the case of South Africa
Forecasting aggregate retail sales may improve portfolio investors‟ ability to predict movements
in the stock prices of the retailing chains. Therefore, this paper uses 26 (23 single and 3
combination) forecasting models to forecast South Africa‟s aggregate seasonal retail sales. We
use data from 1970:01 – 2012:05, with 1987:01-2012:05 as the out-of-sample period. Unlike, the
previous literature on retail sales forecasting, we not only look at a wider array of linear and
nonlinear models, but also generate multi-steps-ahead forecasts using a real-time recursive
estimation scheme over the out-of-sample period, to mimic better the practical scenario faced by
agents making retailing decisions. In addition, we deviate from the uniform symmetric quadratic
loss function typically used in forecast evaluation exercises, by considering loss functions that
overweight forecast error in booms and recessions. Focusing on the single models alone, results
show that their performances differ greatly across forecast horizons and for different weighting
schemes, with no unique model performing the best across various scenarios. However, the
combination forecasts models, especially the discounted mean-square forecast error method
which weighs current information more than past, produced not only better forecasts, but were
also largely unaffected by business cycles and time horizons. This result, along with the fact that
individual nonlinear models performed better than linear models, led us to conclude that
theoretical research on retail sales should look at developing dynamic stochastic general
equilibrium models which not only incorporates learning behaviour, but also allows the
behavioural parameters of the model to be state-dependent, to account for regime-switching
behaviour across alternative states of the economy.http://www.elsevier.com/locate/ijpehb201
RFID Technology in Intelligent Tracking Systems in Construction Waste Logistics Using Optimisation Techniques
Construction waste disposal is an urgent issue
for protecting our environment. This paper proposes a
waste management system and illustrates the work
process using plasterboard waste as an example, which
creates a hazardous gas when land filled with household
waste, and for which the recycling rate is less than 10%
in the UK. The proposed system integrates RFID
technology, Rule-Based Reasoning, Ant Colony
optimization and knowledge technology for auditing
and tracking plasterboard waste, guiding the operation
staff, arranging vehicles, schedule planning, and also
provides evidence to verify its disposal. It h relies on
RFID equipment for collecting logistical data and uses
digital imaging equipment to give further evidence; the
reasoning core in the third layer is responsible for
generating schedules and route plans and guidance, and
the last layer delivers the result to inform users. The
paper firstly introduces the current plasterboard
disposal situation and addresses the logistical problem
that is now the main barrier to a higher recycling rate,
followed by discussion of the proposed system in terms
of both system level structure and process structure.
And finally, an example scenario will be given to
illustrate the system’s utilization
Consumer Perception Studies on the Safety of Food Packaging - Final Report of WP7 of the EU Project "Foodmigrosure" QLK1-CT2002-2390
Between March 2003 and September 2006 the FOODMIGROSURE project, contract number QLK-CT2002-2390, was carried out by 9 European project partners with the intention to develop an ¿into-food¿ migration model tool which should enable prediction of mass transfer of constituents from plastics food contact materials into foodstuffs in support of calculations/estimations of the exposure of consumers towards food packaging constituents. A further objective was to investigate the social acceptance of migration modelling versus chemical measurements, and its implications for exposure estimation. This was achieved by several approaches including focus group (as qualitative approach), and questionnaires with a large polling bas as quantitative approach from citizens. A test trial was run on consumer associations and the experiment was then conducted on citizens during a JRC Open Day. Questionnaires and comments were colleted for 700 units which represented about 1400 visitors to the food contact activities. In the last phase, a more specific technical questionnaire was directed to end-user of modelling, which was mailed to a variety of stakeholders such as National Reference Laboratories, commercial laboratories, industries, EFSA, CEN members etc.
Globally, people in the overwhelming majority -both for the questionnaire approach and for the focus group approach- felt reassured regarding the safety of packaging simply from the fact that they did not previously know that such research and controls existed. Many citizens also clearly expressed the wish to have this type of research much more visible at the level of both consumer associations and consumers themselves. The responses were echoing quite interestingly between the different approaches directed at consumers/citizens. Although obtained by completely different methodologies, both focus groups and quantitative citizen polling questionnaires showed many similarities even in the specifics. There is a fundamental trust from the public in the scientists to distinguish and understand safety issues. The consumer wants sincerely to be approached and informed by scientists for this reason and is also ready to favour new approaches such as migration modelling if it can be an additional tool for better consumer protection. The benefits of packaging are recognised, and the presence of migrants is considered similarly to the presence of food additives in foods. Modelling is viewed as a additional helping tool to assist the scientist as first and foremost raison d¿être, and was found to have its strongest value as pointing the worst cases that could occur. The consumers or citizens made no mention of environmental or worker health effects benefits. However, the consumer especially in the context of the focus group remarked justly that one needs to be sure that at the root for use of these models are experimental data which demonstrate the applicability of the model.JRC.I.5-Nanobioscience
CORPORATE SOCIAL RESPONSIBILITY IN ROMANIA
The purpose of this paper is to identify the main opportunities and limitations of corporate social responsibility (CSR). The survey was defined with the aim to involve the highest possible number of relevant CSR topics and give the issue a more wholesome perspective. It provides a basis for further comprehension and deeper analyses of specific CSR areas. The conditions determining the success of CSR in Romania have been defined in the paper on the basis of the previously cumulative knowledge as well as the results of various researches. This paper provides knowledge which may be useful in the programs promoting CSR.Corporate social responsibility, Supportive policies, Romania
Gene expression programming for Efficient Time-series Financial Forecasting
Stock market prediction is of immense interest to trading companies and buyers due to
high profit margins. The majority of successful buying or selling activities occur close
to stock price turning trends. This makes the prediction of stock indices and analysis a
crucial factor in the determination that whether the stocks will increase or decrease the
next day. Additionally, precise prediction of the measure of increase or decrease of
stock prices also plays an important role in buying/selling activities. This research
presents two core aspects of stock-market prediction. Firstly, it presents a Networkbased
Fuzzy Inference System (ANFIS) methodology to integrate the capabilities of
neural networks with that of fuzzy logic. A specialised extension to this technique is
known as the genetic programming (GP) and gene expression programming (GEP) to
explore and investigate the outcome of the GEP criteria on the stock market price
prediction.
The research presented in this thesis aims at the modelling and prediction of short-tomedium
term stock value fluctuations in the market via genetically tuned stock market
parameters. The technique uses hierarchically defined GP and gene-expressionprogramming
(GEP) techniques to tune algebraic functions representing the fittest
equation for stock market activities. The technology achieves novelty by proposing a
fractional adaptive mutation rate Elitism (GEP-FAMR) technique to initiate a balance
between varied mutation rates between varied-fitness chromosomes thereby improving
prediction accuracy and fitness improvement rate. The methodology is evaluated
against five stock market companies with each having its own trading circumstances
during the past 20+ years. The proposed GEP/GP methodologies were evaluated based
on variable window/population sizes, selection methods, and Elitism, Rank and Roulette
selection methods. The Elitism-based approach showed promising results with a low
error-rate in the resultant pattern matching with an overall accuracy of 95.96% for
short-term 5-day and 95.35% for medium-term 56-day trading periods. The
contribution of this research to theory is that it presented a novel evolutionary
methodology with modified selection operators for the prediction of stock exchange
data via Gene expression programming. The methodology dynamically adapts the
mutation rate of different fitness groups in each generation to ensure a diversification
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balance between high and low fitness solutions. The GEP-FAMR approach was
preferred to Neural and Fuzzy approaches because it can address well-reported
problems of over-fitting, algorithmic black-boxing, and data-snooping issues via GP
and GEP algorithmsSaudi Cultural Burea
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