Radiocarbon Dating: Analysis of the Burns Site

This document is a student report containing aims to outline the period of use of the Burns (8BR85) site by the Ais Native Americans and other earlier groups of Paleoindians with radiocarbon dating analysis

Formation of seasonal groups and application of seasonal indices

Estimating seasonal variations in demand is a challenging task faced by many organisations. There may be many stock-keeping units (SKUs) to forecast, but often data histories are short, with very few complete seasonal cycles. It has been suggested in the literature that group seasonal indices (GSI) methods should be used to take advantage of information on similar SKUs. This paper addresses two research questions: (1) how should groups be formed in order to use the GSI methods? and (2) when should the GSI methods and the individual seasonal indices (ISI) method be used? Theoretical results are presented, showing that seasonal grouping and forecasting may be unified, based on a Mean Square Error criterion, and K-means clustering. A heuristic K-means method is presented, which is competitive with the Average Linkage method. It offers a viable alternative to a company’s own grouping method or may be used with confidence if a company lacks a grouping method. The paper gives empirical findings that confirm earlier theoretical results that greater accuracy may be obtained by employing a rule that assigns the GSI method to some SKUs and the ISI method to the remainder

Reproducibility in forecasting research

The importance of replication has been recognised across many scientific disciplines. Reproducibility is a necessary condition for replicability, because an inability to reproduce results implies that the methods have not been specified sufficiently, thus precluding replication. This paper describes how two independent teams of researchers attempted to reproduce the empirical findings of an important paper, ‘‘Shrinkage estimators of time series seasonal factors and their effect on forecasting accuracy’’ (Miller & Williams, 2003). The two teams proceeded systematically, reporting results both before and after receiving clarifications from the authors of the original study. The teams were able to approximately reproduce each other’s results, but not those of Miller and Williams. These discrepancies led to differences in the conclusions as to the conditions under which seasonal damping outperforms classical decomposition. The paper specifies the forecasting methods employed using a flowchart. It is argued that this approach to method documentation is complementary to the provision of computer code, as it is accessible to a broader audience of forecasting practitioners and researchers. The significance of this research lies not only in its lessons for seasonal forecasting but also, more generally, in its approach to the reproduction of forecasting research

Supply chain forecasting when information is not shared

The operations management literature is abundant in discussions on the benefits of information sharing in supply chains. However, there are many supply chains where information may not be shared due to constraints such as compatibility of information systems, information quality, trust and confidentiality. Furthermore, a steady stream of papers has explored a phenomenon known as Downstream Demand Inference (DDI) where the upstream member in a supply chain can infer the downstream demand without the need for a formal information sharing mechanism. Recent research has shown that, under more realistic circumstances, DDI is not possible with optimal forecasting methods or Single Exponential Smoothing but is possible when supply chains use a Simple Moving Average (SMA) method. In this paper, we evaluate a simple DDI strategy based on SMA for supply chains where information cannot be shared. This strategy allows the upstream member in the supply chain to infer the consumer demand mathematically rather than it being shared. We compare the DDI strategy with the No Information Sharing (NIS) strategy and an optimal Forecast Information Sharing (FIS) strategy in the supply chain. The comparison is made analytically and by experimentation on real sales data from a major European supermarket located in Germany. We show that using the DDI strategy improves on NIS by reducing the Mean Square Error (MSE) of the forecasts, and cutting inventory costs in the supply chain

The origin of ultra diffuse galaxies: stellar feedback and quenching

We test if the cosmological zoom-in simulations of isolated galaxies from the FIRE project reproduce the properties of ultra diffuse galaxies. We show that stellar feedback-generated outflows that dynamically heat galactic stars, together with a passively aging stellar population after imposed quenching (from e.g. infall into a galaxy cluster), naturally reproduce the observed population of red UDGs, without the need for high spin halos or dynamical influence from their host cluster. We reproduce the range of surface brightness, radius and absolute magnitude of the observed z=0 red UDGs by quenching simulated galaxies at a range of different times. They represent a mostly uniform population of dark matter-dominated galaxies with M_star ~1e8 Msun, low metallicity and a broad range of ages. The most massive simulated UDGs require earliest quenching and are therefore the oldest. Our simulations provide a good match to the central enclosed masses and the velocity dispersions of the observed UDGs (20-50 km/s). The enclosed masses of the simulated UDGs remain largely fixed across a broad range of quenching times because the central regions of their dark matter halos complete their growth early. A typical UDG forms in a dwarf halo mass range of Mh~4e10-1e11 Msun. The most massive red UDG in our sample requires quenching at z~3 when its halo reached Mh ~ 1e11 Msun. If it, instead, continues growing in the field, by z=0 its halo mass reaches > 5e11 Msun, comparable to the halo of an L* galaxy. If our simulated dwarfs are not quenched, they evolve into bluer low-surface brightness galaxies with mass-to-light ratios similar to observed field dwarfs. While our simulation sample covers a limited range of formation histories and halo masses, we predict that UDG is a common, and perhaps even dominant, galaxy type around Ms~1e8 Msun, both in the field and in clusters.Comment: 20 pages, 13 figures; match the MNRAS accepted versio

Judgement and supply chain dynamics

Forecasting demand at the individual stock-keeping-unit (SKU) level often necessitates the use of statistical methods, such as exponential smoothing. In some organizations, however, statistical forecasts will be subject to judgemental adjustments by managers. Although a number of empirical and ‘laboratory’ studies have been performed in this area, no formal OR modelling has been conducted to offer insights into the impact such adjustments may have on supply chain performance and the potential development of mitigation mechanisms. This is because of the associated dynamic complexity and the situation-specific nature of the problem at hand. In conjunction with appropriate stock control rules, demand forecasts help decide how much to order. It is a common practice that replenishment orders may also be subject to judgemental intervention, adding further to the dynamic system complexity and interdependence. The system dynamics (SD) modelling method can help advance knowledge in this area, where mathematical modelling cannot accommodate the associated complexity. This study, which constitutes part of a UK government funded (EPSRC) project, uses SD models to evaluate the effects of forecasting and ordering adjustments for a wide set of scenarios involving: three different inventory policies; seven different (combinations of) points of intervention; and four different (combinations of) types of judgmental intervention (optimistic and pessimistic). The results enable insights to be gained into the performance of the entire supply chain. An agenda for further research concludes the paper

A New Brooklyn College Is Rising

Article in the New York Times about the building of the new Brooklyn College in Midwood written by President of the college, William Boylan