Forecasting of work in process quality using Holt-Winters method for missing observations

Since the pace of technological change is so great, and since new products and processes may be key to a company\u27s future plans, an increasing number of companies are emphasizing regular and complete technological forecasts affecting their industry. Those companies which have gone far in developing planning premises from their technological forecasts have tended to be high-technology enterprises. What has been done in these instances is to encourage members of their technical staffs to be alert to future developments; to think in terms of the impact of current scientific developments on the future state of technology; and to develop orderly forecasts of how these developments affect the company\u27s products, processes or markets. Many attempts have been made to accurately forecast future and some of the accurate and meaningful methods used to forecast the state of technology are the Delphi technique, the opportunity and goal oriented techniques.;The objective of this research was to develop a forecasting model using extension of Holt-Winters method for missing data. The variable of interest considered was the fraction non-conforming of a process. Initial values were generated using Beta distribution. Values of fraction non-conforming for future periods were generated using different processes such as Autoregressive and Autoregressive Moving Average process. Some of the values in each data set were assumed to be missing. The factors that were considered for forecasting using this method were the level, trend and the seasonal factor. Forecasting was done for at least one period ahead and at the most twelve periods ahead. The developed models were found to give acceptable results with as many as 40% of the total observations missing and this was validated by performing tracking signal analysis

How Feedback Can Improve Managerial Evaluations of Model-based Marketing Decision Support Systems

Marketing managers often provide much poorer evaluations of model-based marketing decision support systems (MDSSs) than are warranted by the objective performance of those systems. We show that a reason for this discrepant evaluation may be that MDSSs are often not designed to help users understand and internalize the underlying factors driving the MDSS results and related recommendations. Thus, there is likely to be a gap between a marketing manager’s mental model and the decision model embedded in the MDSS. We suggest that this gap is an important reason for the poor subjective evaluations of MDSSs, even when the MDSSs are of high objective quality, ultimately resulting in unreasonably low levels of MDSS adoption and use. We propose that to have impact, an MDSS should not only be of high objective quality, but should also help reduce any mental model-MDSS model gap. We evaluate two design characteristics that together lead model-users to update their mental models and reduce the mental model-MDSS gap, resulting in better MDSS evaluations: providing feedback on the upside potential for performance improvement and providing specific suggestions for corrective actions to better align the user's mental model with the MDSS. We hypothesize that, in tandem, these two types of MDSS feedback induce marketing managers to update their mental models, a process we call deep learning, whereas individually, these two types of feedback will have much smaller effects on deep learning. We validate our framework in an experimental setting, using a realistic MDSS in the context of a direct marketing decision problem. We then discuss how our findings can lead to design improvements and better returns on investments in MDSSs such as CRM systems, Revenue Management systems, pricing decision support systems, and the like.Learning;Feedback;Marketing Decision Models;Marketing Decision Support Systems;Marketing Information Systems

Adsorption Studies of Crystal Violet From Aqueous Solution Using Low Cost Material: Equilibrium and Kinetics Studies

Crystal violet (CV), one of the toxic dyes which are extensively used for dyestuffs, textile, paper and plastics industries. CV does not easily biodegrades in aqueous medium and show harmful effect on aquatic as well as human life. In the present work adsorption studies of CV onto husk powder of Red gram crop (Cajanuscajan) seed was examined in aqueous solution at 27.8ºC. The effects of initial concentration, adsorbent dose, temperature, and contact time etc were determined. Highest 81.49% adsorption efficiency recorded was for 50 mg/L solution concentration onto 2.5g of husk powder of Red gram crop seed. The applicability of Langmuir and Freundlich isotherm model was investigated, and the Langmuir adsorption isotherm model exhibited the best fit than Freundlich isotherm model with the experimental data. The adsorption follows pseudo-second-order kinetics

High Performance Liquid Chromatography-Size Exclusion Chromatography (Hplc-Sec) As an Efficient Tool for The Quantification of Polymers

Poly (lactide-co-glycolide acid) ( PLGA) is an extraordinary well-described polymer and has excellent pharmaceutical properties like high biocompatibility and good biodegradability. Hence, it is one of the most used materials for drug delivery and biomedical systems, also being present in several US Food and Drug Administration approved carrier systems and therapeutic devices. For both applications, the quantification of polymer is important. During the development of the production process, parameters like yield or loading efficacy are essential to be determined. Although PLGA is a well-defined biomaterial, it still lacks a sensitive and convenient quantification approach for PLGA-based systems. Thus, we present a new method for fast and precise quantification of PLGA by HPLC-SEC. The method includes a shorter run time of 20 minutes with a size exclusion column of 300mm x 8.0mm diameter, tetrahydrofuran as mobile phase and diluent, the detection was carried out using the refractive index detector. The developed method has a detection limit of 0.1 ppm, enabling the quantification of low amounts of PLGA. Compared to existing approaches, like gravimetric or nuclear magnetic resonance measurements, which are tedious or expensive, the developed method is fast, ideal for routine screening and it is selective since no interference. The developed method is validated in terms of selectivity, precision, linearity, accuracy and solution stability. Due to the high sensitivity and rapidity of the method, it is suitable for both, laboratory and industrial use  &nbsp

Hyper Spectral Analysis of Soil Iron Oxide using PLSR Method: A Review

Spectroscopy is a rapid, simple, non-destructive and analytical technique, which provides a good alternative that may be used to replace conventional methods of soil analysis. Soil iron oxides occur in almost all type�s soils and they re?ect different environmental conditions by the high variability of their mineralogy and concentration. Soil iron oxide, being an important pedogenic indicator of the soil, measurement of Iron Oxide content can be used as an index of soil fertility. Analytical Spectral Device (ASD) Field Spec 4 Spectroradiometer is used which has 350-2500 nm spectral wavelength range to estimate iron oxide content from the soil sample. The Vis-NIR reflectance spectroscopy requires less effort and it is quick innovation to predict the soil iron oxide content. For collecting the soil iron oxide content from spectral data we are utilizing PLSR which is statistical regression method. This paper states the work that is done on different soil types at different places to observe the iron oxide content in soil

DEVELOPMENT AND VALIDATION OF A GRADIENT HPLC METHOD FOR QUANTIFICATION OF EDETATE DISODIUM IN LYOPHILIZED INJECTABLE DRUG PRODUCT

Objective: The present study was aimed to validate a developed reversed phase gradient high-performance liquid chromatography method for the quantitative determination of Edetate Disodium in the lyophilized injectable drug product. Methods: The amount of total Edetate disodium was analysed by HPLC assay using Edetate disodium USP as a reference standard. Injectable product was dissolved in acetone and Edetate disodium is separated out from API and then dissolved in water. Analysis was carried out using ferric chloride as a precolumn derivatizing reagent and YMC Pack ODS-A, 5 µm column with mobile phase as a mixture of tetrabutylammonium bromide buffer pH 2.8 and acetonitrile as the solvent, water used as diluent. The Edetate disodium quantified by U. V. wavelength at 254 nm. Results: The method was suitably validated with respect to specificity, linearity, precision, accuracy and solution stability, using this method the average recovery from spike sample is 98.2%, with a relative standard deviation of<3%. The minimal quantifiable level was 1.5 µg/ml. The results show that the procedure is accurate, precise and reproducible. Conclusion: In the present study an attempt has been made to develop and validate the analytical method for lyophilised injectable formulations and to generate the scientific database for formulation and evaluation of various lyophilised injectable containing Edetate disodium

How Feedback Can Improve Managerial Evaluations of Model-based Marketing Decision Support Systems

Marketing managers often provide much poorer evaluations of model-based marketing decision support systems (MDSSs) than are warranted by the objective performance of those systems. We show that a reason for this discrepant evaluation may be that MDSSs are often not designed to help users understand and internalize the underlying factors driving the MDSS results and related recommendations. Thus, there is likely to be a gap between a marketing manager’s mental model and the decision model embedded in the MDSS. We suggest that this gap is an important reason for the poor subjective evaluations of MDSSs, even when the MDSSs are of high objective quality, ultimately resulting in unreasonably low levels of MDSS adoption and use. We propose that to have impact, an MDSS should not only be of high objective quality, but should also help reduce any mental model-MDSS model gap. We evaluate two design characteristics that together lead model-users to update their mental models and reduce the mental model-MDSS gap, resulting in better MDSS evaluations: providing feedback on the upside potential for performance improvement and providing specific suggestions for corrective actions to better align the user's mental model with the MDSS. We hypothesize that, in tandem, these two types of MDSS feedback induce marketing managers to update their mental models, a process we call deep learning, whereas individually, these two types of feedback will have much smaller effects on deep learning. We validate our framework in an experimental setting, using a realistic MDSS in the context of a direct marketing decision problem. We then discuss how our findings can lead to design improvements and better returns on investments in MDSSs such as CRM systems, Revenue Management systems, pricing decision support systems, and the like

From academic research to marketing practice: Exploring the marketing science value chain

We aim to investigate the impact of marketing science articles and tools on the practice of marketing. This impact may be direct (e.g., an academic article may be adapted to solve a practical problem) or indirect (e.g., its contents may be incorporated into practitioners' tools, which then influence marketing decision making). We use the term "marketing science value chain" to describe these diffusion steps, and survey marketing managers, marketing science intermediaries (practicing marketing analysts), and marketing academics to calibrate the value chain.In our sample, we find that (1) the impact of marketing science is perceived to be largest on decisions such as the management of brands, pricing, new products, product portfolios, and customer/market selection, and (2) tools such as segmentation, survey-based choice models, marketing mix models, and pre-test market models have the largest impact on marketing decisions. Exemplary papers from 1982 to 2003 that achieved dual - academic and practice - impact are Guadagni and Little (1983) and Green and Srinivasan (1990). Overall, our results are encouraging. First, we find that the impact of marketing science has been largest on marketing decision areas that are important to practice. Second, we find moderate alignment between academic impact and practice impact. Third, we identify antecedents of practice impact among dual impact marketing science papers. Fourth, we discover more recent trends and initiatives in the period 2004-2012, such as the increased importance of big data and the rise of digital and mobile communication, using the marketing science value chain as an organizing framework

Exploiting Anti-monotonicity of Multi-label Evaluation Measures for Inducing Multi-label Rules

Exploiting dependencies between labels is considered to be crucial for multi-label classification. Rules are able to expose label dependencies such as implications, subsumptions or exclusions in a human-comprehensible and interpretable manner. However, the induction of rules with multiple labels in the head is particularly challenging, as the number of label combinations which must be taken into account for each rule grows exponentially with the number of available labels. To overcome this limitation, algorithms for exhaustive rule mining typically use properties such as anti-monotonicity or decomposability in order to prune the search space. In the present paper, we examine whether commonly used multi-label evaluation metrics satisfy these properties and therefore are suited to prune the search space for multi-label heads.Comment: Preprint version. To appear in: Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2018. See http://www.ke.tu-darmstadt.de/bibtex/publications/show/3074 for further information. arXiv admin note: text overlap with arXiv:1812.0005