Forecasting With Exponential Smoothing Whats The Right Smoothing Constant?

This paper examines exponential smoothing constants that minimize summary error measures associated with a large number of forecasts. These forecasts were made on numerous time series generated through simulation on a spreadsheet. The series varied in length and underlying nature no trend, linear trend, and nonlinear trend. Forecasts were made using simple exponential smoothing as well as exponential smoothing with trend correction and with different kinds of initial forecasts. We found that when initial forecasts were good and the nature of the underlying data did not change, smoothing constants were typically very small. Conversely, large smoothing constants indicated a change in the nature of the underlying data or the use of an inappropriate forecasting model. These results reduce the confusion about the role and right size of these constants and offer clear recommendations on how they should be discussed in classroom settings

Determining The Optimal Values Of Exponential Smoothing Constants – Does Solver Really Work?

A key issue in exponential smoothing is the choice of the values of the smoothing constants used.  One approach that is becoming increasingly popular in introductory management science and operations management textbooks is the use of Solver, an Excel-based non-linear optimizer, to identify values of the smoothing constants that minimize a measure of forecast error like Mean Absolute Deviation (MAD) or Mean Squared Error (MSE).  We point out some difficulties with this approach and suggest an easy fix. We examine the impact of initial forecasts on the smoothing constants and the idea of optimizing the initial forecast along with the smoothing constants.  We make recommendations on the use of Solver in the context of the teaching of forecasting and suggest that there is a better method than Solver to identify the appropriate smoothing constants

Determining The Optimal Values Of Exponential Smoothing Constants Does Solver Really Work?

A key issue in exponential smoothing is the choice of the values of the smoothing constants used.One approach that is becoming increasingly popular in introductory management science and operations management textbooks is the use of Solver, an Excel-based non-linear optimizer, to identify values of the smoothing constants that minimize a measure of forecast error like Mean Absolute Deviation (MAD) or Mean Squared Error (MSE).We point out some difficulties with this approach and suggest an easy fix. We examine the impact of initial forecasts on the smoothing constants and the idea of optimizing the initial forecast along with the smoothing constants.We make recommendations on the use of Solver in the context of the teaching of forecasting and suggest that there is a better method than Solver to identify the appropriate smoothing constants

ABC Analysis For Inventory Management: Bridging The Gap Between Research And Classroom

ABC analysis is a well-established categorization technique based on the Pareto Principle for determining which items should get priority in the management of a company’s inventory.  In discussing this topic, today’s operations management and supply chain textbooks focus on dollar volume as the sole criterion for performing the categorization.  The authors argue that today’s businesses and supply chains operate in a world where the ability to deliver the right products rapidly to very specific markets is key to survival.  With suppliers, intermediaries, and customers all over the globe, and product lives decreasing rapidly, this focus on a single criterion is misplaced.  The large body of research was summarized based on multiple criteria ABC analysis that has accumulated since the 1980s and recommend that textbooks incorporate their key findings and methods into their discussions of this topic.  Suggestions are offered on how this discussion might be structured.

Experimental Validation of the Predicted Binding Site of Escherichia coli K1 Outer Membrane Protein A to Human Brain Microvascular Endothelial Cells: Identification of Critical Mutations That Prevent E. coli Meningitis

Escherichia coli K1, the most common cause of meningitis in neonates, has been shown to interact with GlcNAc1–4GlcNAc epitopes of Ecgp96 on human brain microvascular endothelial cells (HBMECs) via OmpA (outer membrane protein A). However, the precise domains of extracellular loops of OmpA interacting with the chitobiose epitopes have not been elucidated. We report the loop-barrel model of these OmpA interactions with the carbohydrate moieties of Ecgp96 predicted from molecular modeling. To test this model experimentally, we generated E. coli K1 strains expressing OmpA with mutations of residues predicted to be critical for interaction with the HBMEC and tested E. coli invasion efficiency. For these same mutations, we predicted the interaction free energies (including explicit calculation of the entropy) from molecular dynamics (MD), finding excellent correlation (R^2 = 90%) with experimental invasion efficiency. Particularly important is that mutating specific residues in loops 1, 2, and 4 to alanines resulted in significant inhibition of E. coli K1 invasion in HBMECs, which is consistent with the complete lack of binding found in the MD simulations for these two cases. These studies suggest that inhibition of the interactions of these residues of Loop 1, 2, and 4 with Ecgp96 could provide a therapeutic strategy to prevent neonatal meningitis due to E. coli K1

Vector Coding Optical Wireless Links

The quasi-static nature of the optical wireless channel means that the channel state information (CSI) can be readily available at the transmitter and receiver prior to data transmission. This implies that electrically band-limited optical wireless communication (OWC) systems can make use of optimal channel partitioning or vector coding based multi-channel modulation (MCM) to achieve high throughput by mitigating the non-linearities arising from the optical and electrical channel. This paper proposes a pulse amplitude modulation (PAM) based DC-biased optical vector coding (DCO-VC) MCM scheme for OWC. The throughput performance of DCO-VC is evaluated and compared to the well known DC-biased optical orthogonal frequency division multiplexing (DCO-OFDM) over hybrid (line-of-sight and diffuse) and diffuse (non line-of-sight only) visible light communication (VLC) channels with additive white Gaussian noise. For the completeness of the VLC physical layer, the performance comparison is based on an uncoded and a forward error correction transmission mode using well-known convolutional codes with Viterbi decoder. The results show that the coded DCO-VC outperforms DCO-OFDM system by achieving up to 2 and 3 dB signal to noise ratio gains over hybrid and diffuse VLC channels, respectively

Synthesis, Spectral Characterization and Antimicrobial Studies of Co(II) Complexes with Tetradentate Schiff bases Derived from Ortho-Phthalaldehyde

A series of cobalt (II) complexes have been synthesized with Schiff bases derived from ortho-phthalaldehyde and various amines in aqueous methanol solution. The newly synthesized Schiff bases and their Co (II) complexes have been characterized  by elemental analysis, magnetic susceptibility, thermal, conductance measurements, mass, IR, electronic, 1H,13C-NMR spectral techniques. These ligands act as tetradentate species and coordinate to the metal center through the different potential donor atoms such as N, O and S. The probable octahedral structures have been assigned to these complexes. All the synthesized Schiff base ligands and Co(II) metal complexes have also been screened for their antimicrobial activities and metal complexes found to be more active than respective Schiff-base ligands