The role of learning on industrial simulation design and analysis

The capability of modeling real-world system operations has turned simulation into an indispensable problemsolving methodology for business system design and analysis. Today, simulation supports decisions ranging from sourcing to operations to finance, starting at the strategic level and proceeding towards tactical and operational levels of decision-making. In such a dynamic setting, the practice of simulation goes beyond being a static problem-solving exercise and requires integration with learning. This article discusses the role of learning in simulation design and analysis motivated by the needs of industrial problems and describes how selected tools of statistical learning can be utilized for this purpose

Predicting with sparse data

It is well known that effective prediction of project cost related factors is an important aspect of software engineering. Unfortunately, despite extensive research over more than 30 years, this remains a significant problem for many practitioners. A major obstacle is the absence of reliable and systematic historic data, yet this is a sine qua non for almost all proposed methods: statistical, machine learning or calibration of existing models. In this paper we describe our sparse data method (SDM) based upon a pairwise comparison technique and Saaty's Analytic Hierarchy Process (AHP). Our minimum data requirement is a single known point. The technique is supported by a software tool known as DataSalvage. We show, for data from two companies, how our approach — based upon expert judgement — adds value to expert judgement by producing significantly more accurate and less biased results. A sensitivity analysis shows that our approach is robust to pairwise comparison errors. We then describe the results of a small usability trial with a practising project manager. From this empirical work we conclude that the technique is promising and may help overcome some of the present barriers to effective project prediction

The Project Scheduling Problem with Non-Deterministic Activities Duration: A Literature Review

Purpose: The goal of this article is to provide an extensive literature review of the models and solution procedures proposed by many researchers interested on the Project Scheduling Problem with nondeterministic activities duration. Design/methodology/approach: This paper presents an exhaustive literature review, identifying the existing models where the activities duration were taken as uncertain or random parameters. In order to get published articles since 1996, was employed the Scopus database. The articles were selected on the basis of reviews of abstracts, methodologies, and conclusions. The results were classified according to following characteristics: year of publication, mathematical representation of the activities duration, solution techniques applied, and type of problem solved. Findings: Genetic Algorithms (GA) was pointed out as the main solution technique employed by researchers, and the Resource-Constrained Project Scheduling Problem (RCPSP) as the most studied type of problem. On the other hand, the application of new solution techniques, and the possibility of incorporating traditional methods into new PSP variants was presented as research trends. Originality/value: This literature review contents not only a descriptive analysis of the published articles but also a statistical information section in order to examine the state of the research activity carried out in relation to the Project Scheduling Problem with non-deterministic activities duration.Peer Reviewe

Wage Determination in Russia: An Econometric Investigation

Using a firm level dataset from four regions of Russia covering 1996/97, an investigation was carried out into how the surplus created within the firm is divided between profits and wages. An efficient bargaining framework based on the work of Svejnar (1986) is employed which takes into account the alternative wage or outside option available to employees in the firm as well as the value added per employee. Statistical differences in the share of the surplus taken by employees employed in state, private and mixed forms of firms are found. In addition, the results prove sensitive to the presence of outliers and influential observations. A variety of diagnostic methods are employed to identify these influential observations and robust methods are employed to lessen the influence of them. Whereas in practice some of the diagnostic and robust methods utilised proved incapable of identifying or accommodating the gross outlier(s) in the data, the more successful methods included robust regression, Winsorising, the Hadi and Siminoff algorithm, Cook's Distance and Covratio.http://deepblue.lib.umich.edu/bitstream/2027.42/39679/3/wp295.pd

Internal organization and performances of saving and loan associations: Evidence from rural Tanzania

Subsistence farmers in rural areas of developing countries are usually outside the current reach of banks and formal microfinance institutions. They do not have access to savings accounts, insurance products, and agricultural credit facilities, limiting those farmers’ investment in agriculture. Being at the outreach of those institutions, those farmers established, so-called savings and loan associations, self-managed groups of 20-30 individuals meeting regularly to provide its members a safe place to save and obtain emergency aid and small loans. When efficiently organized, those associations may provide a secure platform to save and access loans to invest in climate-smart agriculture and mitigate income shocks. The objective of this study is to identify the role of the associations in financing agriculture, major bottlenecks and organizational characteristics that might explain their financial performances. We use survey data from 48 savings and loan associations in rural Tanzania with members trained for adopting climate-smart agricultural practices. We identify that 45% of the loans of associations are distributed for agricultural investment purposes and the major bottleneck is to low savings and participation rates, and late repayment or defaults of loans. We find that the size of associations and record-keeping matters. The average amount of loan received per member approximately doubled for associations with twice as many members, and default rates decrease with the accurate financial recording practices. Our findings suggest that savings and loan associations could strengthen the financial resilience of its members by empowering their members through financial record keeping training. At the same time, they can add new members to the associations

Using the partial least squares (PLS) method to establish critical success factor interdependence in ERP implementation projects

This technical research report proposes the usage of a statistical approach named Partial Least squares (PLS) to define the relationships between critical success factors for ERP implementation projects. In previous research work, we developed a unified model of critical success factors for ERP implementation projects. Some researchers have evidenced the relationships between these critical success factors, however no one has defined in a formal way these relationships. PLS is one of the techniques of structural equation modeling approach. Therefore, in this report is presented an overview of this approach. We provide an example of PLS method modelling application; in this case we use two critical success factors. However, our project will be extended to all the critical success factors of our unified model. To compute the data, we are going to use PLS-graph developed by Wynne Chin.Postprint (published version

Energetic and environmental benefits of co-digestion of food waste and cattle slurry: a preliminary assessment

The research evaluated the feasibility of centralised pre-processing and pasteurisation of source-separated domestic food waste followed by transport to farms for anaerobic co-digestion with dairy cattle slurry. Data from long-term experiments on the co-digestion of these two substrates was used to predict gross energy yields; net yields were then derived from full system analysis using an energy modelling tool. The ratio of cattle slurry to food waste in the co-digestion was based on the nutrient requirements of the dairy farm and was modelled using both nitrogen and phosphorous as the limiting factor. The model was run for both medium-size and large farms in which the cattle were housed either all year round or for only 50% of the year. The results showed that the addition of food waste improved energy yields per digester unit volume, with a corresponding increased potential for improving farm income by as much as 50%. Data for dairy farms in the county of Hampshire UK, which has a low density of dairy cattle and a large population, was used as a stringent test case to verify the applicability of the concept. In this particular case the nutrient requirements of the larger farms could be satisfied, and further benefits were gained from the reduction in greenhouse gas emissions avoided through improved manure management and fertiliser imports. The results indicated that this approach offered major advantages in terms of resource conservation and pollution abatement when compared to either centralised anaerobic digestion of food waste or energy recovery from thermal treatmen