Estimating the SCAN*PRO model of store sales: HB, FM or just OLS?

In this paper we investigate whether consideration of store-level heterogeneity in marketing mix effects improves the accuracy of the marketing mix elasticities, fit, and forecasting accuracy of the widely-applied SCAN*PRO model of store sales. Models with continuous and discrete representations of heterogeneity, estimated using hierarchical Bayes (HB) and finite mixture (FM) techniques, respectively, are empirically compared to the original model, which does not account for store-level heterogeneity in marketing mix effects, and is estimated using ordinary least squares (OLS). The empirical comparisons are conducted in two contexts: Dutch store-level scanner data for the shampoo product category, and an extensive simulation experiment. The simulation investigates how between- and within-segment variance in marketing mix effects, error variance, the number of weeks of data, and the number of stores impact the accuracy of marketing mix elasticities, model fit, and forecasting accuracy. Contrary to expectations, accommodating store-level heterogeneity does not improve the accuracy of marketing mix elasticities relative to the homogeneous SCAN*PRO model, suggesting that little may be lost by employing the original homogeneous SCAN*PRO model estimated using ordinary least squares. Improvements in fit and forecasting accuracy are also fairly modest. We pursue an explanation for this result since research in other contexts has shown clear advantages from assuming some type of heterogeneity in market response models. In an Afterthought section, we comment on the controversial nature of our result, distinguishing factors inherent to household-level data and associated models vs. general store-level data and associated models vs. the unique SCAN*PRO model specification.

Breakage model development and application with CFD for predicting breakage of whey protein precipitate particles

Particle breakage due to fluid flow through various geometries can have a major influence on the performance of particle/fluid processes and on the product quality characteristics of particle/fluid products. In this study, whey protein precipitate dispersions were used as a case study to investigate the effect of flow intensity and exposure time on the breakage of these precipitate particles. Computational fluid dynamic (CFD) simulations were performed to evaluate the turbulent eddy dissipation rate (TED) and associated exposure time along various flow geometries. The focus of this work is on the predictive modelling of particle breakage in particle/fluid systems. A number of breakage models were developed to relate TED and exposure time to particle breakage. The suitability of these breakage models was evaluated for their ability to predict the experimentally determined breakage of the whey protein precipitate particles. A "power-law threshold" breakage model was found to provide a satisfactory capability for predicting the breakage of the whey protein precipitate particles. The whey protein precipitate dispersions were propelled through a number of different geometries such as bends, tees and elbows, and the model accurately predicted the mean particle size attained after flow through these geometries. © 2005 Elsevier Ltd. All rights reserved.

Managing enterprises and ERP systems:a contingency model for the enterprization of operations

Purpose: The purpose of this paper is to investigate enterprise resource planning (ERP) systems development and emerging practices in the management of enterprises (i.e. parts of companies working with parts of other companies to deliver a complex product and/or service) and identify any apparent correlations. Suitable a priori contingency frameworks are then used and extended to explain apparent correlations. Discussion is given to provide guidance for researchers and practitioners to deliver better strategic, structural and operational competitive advantage through this approach; coined here as the "enterprization of operations". Design/methodology/approach: Theoretical induction uses a new empirical longitudinal case study from Zoomlion (a Chinese manufacturing company) built using an adapted form of template analysis to produce a new contingency framework. Findings: Three main types of enterprises and the three main types of ERP systems are defined and correlations between them are explained. Two relevant a priori frameworks are used to induct a new contingency model to support the enterprization of operations; known as the dynamic enterprise reference grid for ERP (DERG-ERP). Research limitations/implications: The findings are based on one longitudinal case study. Further case studies are currently being conducted in the UK and China. Practical implications: The new contingency model, the DERG-ERP, serves as a guide for ERP vendors, information systems management and operations managers hoping to grow and sustain their competitive advantage with respect to effective enterprise strategy, enterprise structure and ERP systems. Originality/value: This research explains how ERP systems and the effective management of enterprises should develop in order to sustain competitive advantage with respect to enterprise strategy, enterprise structure and ERP systems use. © Emerald Group Publishing Limited.

Hydrogen production by steam reforming of DME in a large scale CFB reactor. Part I:computational model and predictions

This study presents a computational fluid dynamic (CFD) study of Dimethyl Ether steam reforming (DME-SR) in a large scale Circulating Fluidized Bed (CFB) reactor. The CFD model is based on Eulerian-Eulerian dispersed flow and solved using commercial software (ANSYS FLUENT). The DME-SR reactions scheme and kinetics in the presence of a bifunctional catalyst of CuO/ZnO/Al2O3+ZSM-5 were incorporated in the model using in-house developed user-defined function. The model was validated by comparing the predictions with experimental data from the literature. The results revealed for the first time detailed CFB reactor hydrodynamics, gas residence time, temperature distribution and product gas composition at a selected operating condition of 300 °C and steam to DME mass ratio of 3 (molar ratio of 7.62). The spatial variation in the gas species concentrations suggests the existence of three distinct reaction zones but limited temperature variations. The DME conversion and hydrogen yield were found to be 87% and 59% respectively, resulting in a product gas consisting of 72 mol% hydrogen. In part II of this study, the model presented here will be used to optimize the reactor design and study the effect of operating conditions on the reactor performance and products.

The influence of organizational and national culture on new product performance

The authors conduct a meta-analysis on the combined influence of organizational and national culture on new product performance. For this purpose, they refer to the effectiveness of value congruency and develop a conceptual model describing the fit between organizational culture types as suggested by the competing values framework and national culture, as described by Hofstede's cultural dimensions. The meta-analysis is based on 489 effect sizes taken from 123 manuscripts. The findings show that organizations with a market culture show the highest new product performance, while hierarchy-type organizations show the lowest performance. The influence of national culture variables supports the effect of value congruency, and shows that in individualistic cultures the impact of a clan culture decreases, the impact of an adhocracy culture type decreases with uncertainty avoidance, and the influence of a hierarchy culture type increases with power distance. The superior effect of a market culture type can be matched by other organizational orientations, but in particular national cultures only. The combined findings underline the importance for firms that seek to improve the success rate of new products on international markets to consider the fit of a national culture with a firm's organizational culture.

Decision-making in product quality based on failure knowledge

Decision-making in product quality is an indispensable stage in product development, in order to reduce product development risk. Based on the identification of the deficiencies of quality function deployment (QFD) and failure modes and effects analysis (FMEA), a novel decision-making method is presented that draws upon a knowledge network of failure scenarios. An ontological expression of failure scenarios is presented together with a framework of failure knowledge network (FKN). According to the roles of quality characteristics (QCs) in failure processing, QCs are set into three categories namely perceptible QCs, restrictive QCs, and controllable QCs, which present the monitor targets, control targets and improvement targets respectively for quality management. A mathematical model and algorithms based on the analytic network process (ANP) is introduced for calculating the priority of QCs with respect to different development scenarios. A case study is provided according to the proposed decision-making procedure based on FKN. This methodology is applied in the propeller design process to solve the problem of prioritising QCs. This paper provides a practical approach for decision-making in product quality. Copyright © 2011 Inderscience Enterprises Ltd.

A new VRPPD model and a hybrid heuristic solution approach for e-tailing

We analyze a business model for e-supermarkets to enable multi-product sourcing capacity through co-opetition (collaborative competition). The logistics aspect of our approach is to design and execute a network system where “premium” goods are acquired from vendors at multiple locations in the supply network and delivered to customers. Our specific goals are to: (i) investigate the role of premium product offerings in creating critical mass and profit; (ii) develop a model for the multiple-pickup single-delivery vehicle routing problem in the presence of multiple vendors; and (iii) propose a hybrid solution approach. To solve the problem introduced in this paper, we develop a hybrid metaheuristic approach that uses a Genetic Algorithm for vendor selection and allocation, and a modified savings algorithm for the capacitated VRP with multiple pickup, single delivery and time windows (CVRPMPDTW). The proposed Genetic Algorithm guides the search for optimal vendor pickup location decisions, and for each generated solution in the genetic population, a corresponding CVRPMPDTW is solved using the savings algorithm. We validate our solution approach against published VRPTW solutions and also test our algorithm with Solomon instances modified for CVRPMPDTW.

Experimental validation of a predictive pyrolysis model in AspenPlus®

A novel simulation model for pyrolysis processes oflignocellulosicbiomassin AspenPlus (R) was presented at the BC&E 2013. Based on kinetic reaction mechanisms, the simulation calculates product compositions and yields depending on reactor conditions (temperature, residence time, flue gas flow rate) and feedstock composition (biochemical composition, atomic composition, ash and alkali metal content). The simulation model was found to show good correlation with existing publications. In order to further verify the model, own pyrolysis experiments in a 1 kg/h continuously fed fluidized bed fast pyrolysis reactor are performed. Two types of biomass with different characteristics are processed in order to evaluate the influence of the feedstock composition on the yields of the pyrolysis products and their composition. One wood and one straw-like feedstock are used due to their different characteristics. Furthermore, the temperature response of yields and product compositions is evaluated by varying the reactor temperature between 450 and 550 degrees C for one of the feedstocks. The yields of the pyrolysis products (gas, oil, char) are determined and their detailed composition is analysed. The experimental runs are reproduced with the corresponding reactor conditions in the AspenPlus model and the results compared with the experimental findings.

Development of a technological innovation capability assessment model:a case study of manufacturing SMEs in Sialkot, Pakistan

Small and Medium Enterprises (SMEs) play an important part in the economy of any country. Initially, a flat management hierarchy, quick response to market changes and cost competitiveness were seen as the competitive characteristics of an SME. Recently, in developed economies, technological capabilities (TCs) management- managing existing and developing or assimilating new technological capabilities for continuous process and product innovations, has become important for both large organisations and SMEs to achieve sustained competitiveness. Therefore, various technological innovation capability (TIC) models have been developed at firm level to assess firms‘ innovation capability level. These models output help policy makers and firm managers to devise policies for deepening a firm‘s technical knowledge generation, acquisition and exploitation capabilities for sustained technological competitive edge. However, in developing countries TCs management is more of TCs upgrading: acquisitions of TCs from abroad, and then assimilating, innovating and exploiting them. Most of the TIC models for developing countries delineate the level of TIC required as firms move from the acquisition to innovative level. However, these models do not provide tools for assessing the existing level of TIC of a firm and various factors affecting TIC, to help practical interventions for TCs upgrading of firms for improved or new processes and products. Recently, the Government of Pakistan (GOP) has realised the importance of TCs upgrading in SMEs-especially export-oriented, for their sustained competitiveness. The GOP has launched various initiatives with local and foreign assistance to identify ways and means of upgrading local SMEs capabilities. This research targets this gap and developed a TICs assessment model for identifying the existing level of TIC of manufacturing SMEs existing in clusters in Sialkot, Pakistan. SME executives in three different export-oriented clusters at Sialkot were interviewed to analyse technological capabilities development initiatives (CDIs) taken by them to develop and upgrade their firms‘ TCs. Data analysed at CDI, firm, cluster and cross-cluster level first helped classify interviewed firms as leader, follower and reactor, with leader firms claiming to introduce mostly new CDIs to their cluster. Second, the data analysis displayed that mostly interviewed leader firms exhibited ‗learning by interacting‘ and ‗learning by training‘ capabilities for expertise acquisition from customers and international consultants. However, these leader firms did not show much evidence of learning by using, reverse engineering and R&D capabilities, which according to the extant literature are necessary for upgrading existing TIC level and thus TCs of firm for better value-added processes and products. The research results are supported by extant literature on Sialkot clusters. Thus, in sum, a TIC assessment model was developed in this research which qualitatively identified interviewed firms‘ TIC levels, the factors affecting them, and is validated by existing literature on interviewed Sialkot clusters. Further, the research gives policy level recommendations for TIC and thus TCs upgrading at firm and cluster level for targeting better value-added markets.

A value-oriented model for managing service supply chains

Service supply chain (SSC) has attracted more and more attention from academia and industry. Although there exists extensive product-based supply chain management models and methods, they are not applicable to the SSC as the differences between service and product. Besides, the existing supply chain management models and methods possess some common deficiencies. Because of the above reasons, this paper develops a novel value-oriented model for the management of SSC using the modeling methods of E3-value and Use Case Maps (UCMs). This model can not only resolve the problems of applicability and effectiveness of the existing supply chain management models and methods, but also answer the questions of ‘why the management model is this?’ and ‘how to quantify the potential profitability of the supply chains?’. Meanwhile, the service business processes of SSC system can be established using its logic procedure. In addition, the model can also determine the value and benefits distribution of the entire service value chain and optimize the operations management performance of the service supply.

Large volume metrology process model:measurability analysis with integration of metrology classification model and feature-based selection model

Aircraft manufacturing industries are looking for solutions in order to increase their productivity. One of the solutions is to apply the metrology systems during the production and assembly processes. Metrology Process Model (MPM) (Maropoulos et al, 2007) has been introduced which emphasises metrology applications with assembly planning, manufacturing processes and product designing. Measurability analysis is part of the MPM and the aim of this analysis is to check the feasibility for measuring the designed large scale components. Measurability Analysis has been integrated in order to provide an efficient matching system. Metrology database is structured by developing the Metrology Classification Model. Furthermore, the feature-based selection model is also explained. By combining two classification models, a novel approach and selection processes for integrated measurability analysis system (MAS) are introduced and such integrated MAS could provide much more meaningful matching results for the operators. © Springer-Verlag Berlin Heidelberg 2010.

A computational model of hydrogen production by steam reforming of dimethyl ether in a large scale CFB reactor. Part II:parametric analysis

This study presents a computational parametric analysis of DME steam reforming in a large scale Circulating Fluidized Bed (CFB) reactor. The Computational Fluid Dynamic (CFD) model used, which is based on Eulerian-Eulerian dispersed flow, has been developed and validated in Part I of this study [1]. The effect of the reactor inlet configuration, gas residence time, inlet temperature and steam to DME ratio on the overall reactor performance and products have all been investigated. The results have shown that the use of double sided solid feeding system remarkable improvement in the flow uniformity, but with limited effect on the reactions and products. The temperature has been found to play a dominant role in increasing the DME conversion and the hydrogen yield. According to the parametric analysis, it is recommended to run the CFB reactor at around 300 °C inlet temperature, 5.5 steam to DME molar ratio, 4 s gas residence time and 37,104 ml gcat -1 h-1 space velocity. At these conditions, the DME conversion and hydrogen molar concentration in the product gas were both found to be around 80%.