An aggregate sales model for consumer durables incorporating a time-varying mean replacement age

Forecasting category or industry sales is a vital component of a company's planning and control activities. Sales for most mature durable product categories are dominated by replacement purchases. Previous sales models which explicitly incorporate a component of sales due to replacement assume there is an age distribution for replacements of existing units which remains constant over time. However, there is evidence that changes in factors such as product reliability/durability, price, repair costs, scrapping values, styling and economic conditions will result in changes in the mean replacement age of units. This paper develops a model for such time-varying replacement behaviour and empirically tests it in the Australian automotive industry. Both longitudinal census data and the empirical analysis of the replacement sales model confirm that there has been a substantial increase in the average aggregate replacement age for motor vehicles over the past 20 years. Further, much of this variation could be explained by real price increases and a linear temporal trend. Consequently, the time-varying model significantly outperformed previous models both in terms of fitting and forecasting the sales data. Copyright (C) 2001 John Wiley & Sons, Ltd.

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.

Teleoperaattorin suurasiakasmyynnin myyntiprosessin kehittäminen

Diplomityön tavoitteena oli selvittää erään teleoperaattorina toimivan yrityksen sisäisen liiketoimintayksikön nykyisiä kumppanuusmalleja ja toimintatapoja sekä tehdä vaiheistettu ehdotus toiminnan tehostamisesta uudella mallilla. Tämä yksikkö toimii yrityksen suurasiakasmyyntiyksikkönä. Tavoitteena on luoda kehitettävistä uusista toimintamalleista uusi myyntiprosessi ja ottaa se käytäntöön vaiheistetusti. Tutkimuksen ensimmäisessä vaiheessa selvitettiin yrityksen suurasiakasmyyntiyksikön nykyiset kumppanuusmallit ja myyntiprosessi. Tässä keskityttiin myyntiyksikön kannalta merkittäviin toimintoihin. Yrityksen suurasiakasmyynnin toiminnassa havaittiin ongelmana myyntihenkilöstön ajankäyttö, jonka taustoja selvitettiin. Suurin osa myyjien ajasta kului päivittäisrutiinien suorittamiseen ja ongelmatilanteiden selvittämiseen. Tällaisia tilanteita olivat mm. laskutusepäselvyydet, asennustöiden viivästymiset ja tarjousten teknisten ratkaisuiden tekeminen. Nämä toiminnot veivät yli puolet myyjien ajasta, josta tavoitteellisesti yli 80 prosenttia pitäisi kulua asiakkaiden kanssa suoraan toimimiseen. Tutkimuksen teoriataustana käytettiin kahta prosessijohtamisen koulukuntaa; BPR:ää (Business Process Reengineering) ja TQM:ää (Total Quality Management). Niihin perehdyttiin kirjallisuuden ja artikkeleiden avulla ja tähän työhön niistä kirjoitettiin merkitykselliset osat. Yrityksen suurasiakasmyyntiyksikön uuden myyntiprosessin kehittäminen aloitettiin segmentoimalla sen asiakkaat avain-, kanta-, kasvu- ja arvoasiakkaisiin. Näille segmenteille kehitettiin omat myyntimallinsa, joihin liittyi niille suunnattava tarjooma (tuotevalikoima). Tämän jälkeen myyntimallit koulutettiin henkilöstölle ja samalla kerättiin informaatiota uuden myyntiprosessin luomista varten. Uusi myyntiprosessi jakautuu viiteen vaiheeseen. Pre sales –vaiheessa (1) keskitytään asiakkuuksien johtamiseen, yrityksen myyjien oman organisaation ja liiketoimintaympäristön tuntemukseen ja uusasiakashankintaan. Ehdotusvaiheessa (2) tehdään asiakkaalle ehdotus kehitysprojektista, jonka tähtäimenä on luoda asiakkaalle tarve hyödyntää tietoliikennettä omassa toiminnassaan. Tämän toiminnan tavoitteena on päästä mukaan mahdollisimman syvälle asiakkaan liiketoimintaan ja sitä kautta kasvattaa liikevaihtoa ja kannattavuutta. Myyntivaiheessa (3) asiakkaalta on saapunut tarjouspyyntö ja sen pohjalta valmistellaan tarjous. Tämän jälkeen käydään tarkentavia neuvotteluita ja pyritään saamaan suotuisa päätös ja sitä kautta tilaus asiakkaalta. Toimitusvaiheessa (4) myydyt tuotteet ja palvelut syötetään tilausjärjestelmiin ja toimitetaan asiakkaalle. Tämän jälkeen seuraa toimituksen kertalaskutus ja jälkimyynti/markkinointi, jolla jo kertaalleen myydyt tuotteet ja palvelut ikään kuin myydään asiakkaalle uudestaan. Viimeinen vaihe on after sales –vaihe (5), jossa varmistetaan myytyjen tuotteiden ja palveluiden ja niiden kausilaskutuksen toimivuudet, tehdään raportointia ja myydään asiakkaalle jo myytyjen tuotteiden lisäksi uusia lisäpalveluita.

Performance of state space and ARIMA models for consumer retail sales forecasting

Forecasting future sales is one of the most important issues that is beyond all strategic and planning decisions in effective operations of retail businesses. For profitable retail businesses, accurate demand forecasting is crucial in organizing and planning production, purchasing, transportation and labor force. Retail sales series belong to a special type of time series that typically contain trend and seasonal patterns, presenting challenges in developing effective forecasting models. This work compares the forecasting performance of state space models and ARIMA models. The forecasting performance is demonstrated through a case study of retail sales of five different categories of women footwear: Boots, Booties, Flats, Sandals and Shoes. On both methodologies the model with the minimum value of Akaike's Information Criteria for the in-sample period was selected from all admissible models for further evaluation in the out-of-sample. Both one-step and multiple-step forecasts were produced. The results show that when an automatic algorithm the overall out-of-sample forecasting performance of state space and ARIMA models evaluated via RMSE, MAE and MAPE is quite similar on both one-step and multi-step forecasts. We also conclude that state space and ARIMA produce coverage probabilities that are close to the nominal rates for both one-step and multi-step forecasts.

A comparison of sales response predictions from demand models applied to store-level versus panel data

In order to generate sales promotion response predictions, marketing analysts estimate demand models using either disaggregated (consumer-level) or aggregated (store-level) scanner data. Comparison of predictions from these demand models is complicated by the fact that models may accommodate different forms of consumer heterogeneity depending on the level of data aggregation. This study shows via simulation that demand models with various heterogeneity specifications do not produce more accurate sales response predictions than a homogeneous demand model applied to store-level data, with one major exception: a random coefficients model designed to capture within-store heterogeneity using store-level data produced significantly more accurate sales response predictions (as well as better fit) compared to other model specifications. An empirical application to the paper towel product category adds additional insights. This article has supplementary material online.

Adaptation for tandem cold mill models

The ideal conditions for the operation of tandem cold mills are connected to a set of references generated by models and used by dynamic regulators. Aiming at the optimization of the friction and yield stress coefficients an adaptation algorithm is proposed in this paper. Experimental results obtained from an industrial cold rolling mill are presented. (C) 2008 Elsevier B.V. All rights reserved.

Adequacy of Cold Rolling Models to Upgrade Set-up Generation Systems

This paper presents two strategies for the upgrade of set-up generation systems for tandem cold mills. Even though these mills have been modernized mainly due to quality requests, their upgrades may be made intending to replace pre-calculated reference tables. In this case, Bryant and Osborn mill model without adaptive technique is proposed. As a more demanding modernization, Bland and Ford model including adaptation is recommended, although it requires a more complex computational hardware. Advantages and disadvantages of these two systems are compared and discussed and experimental results obtained from an industrial cold mill are shown.

Predictive models for diagnosis of pleural effusions secondary to tuberculosis or cancer

Background and objective: Tuberculosis (TB) and cancer are two of the main causes of pleural effusions which frequently share similar clinical features and pleural fluid profiles. This study aimed to identify diagnostic models based on clinical and laboratory variables to differentiate tuberculous from malignant pleural effusions. Methods: A retrospective study of 403 patients (200 with TB; 203 with cancer) was undertaken. Univariate analysis was used to select the clinical variables relevant to the models composition. Variables beta coefficients were used to define a numerical score which presented a practical use. The performances of the most efficient models were tested in a sample of pleural exudates (64 new cases). Results: Two models are proposed for the diagnosis of effusions associated with each disease. For TB: (i) adenosine deaminase (ADA), globulins and the absence of malignant cells in the pleural fluid; and (ii) ADA, globulins and fluid appearance. For cancer: (i) patient age, fluid appearance, macrophage percentage and presence of atypical cells in the pleural fluid; and (ii) as for (i) excluding atypical cells. Application of the models to the 64 pleural effusions showed accuracy higher than 85% for all models. Conclusions: The proposed models were effective in suggesting pleural tuberculosis or cancer.

Constructing good deals in discrete time arbitrage-free dynamic pricing models

Recent literature has proved that many classical pricing models (Black and Scholes, Heston, etc.) and risk measures (V aR, CV aR, etc.) may lead to “pathological meaningless situations”, since traders can build sequences of portfolios whose risk leveltends to −infinity and whose expected return tends to +infinity, i.e., (risk = −infinity, return = +infinity). Such a sequence of strategies may be called “good deal”. This paper focuses on the risk measures V aR and CV aR and analyzes this caveat in a discrete time complete pricing model. Under quite general conditions the explicit expression of a good deal is given, and its sensitivity with respect to some possible measurement errors is provided too. We point out that a critical property is the absence of short sales. In such a case we first construct a “shadow riskless asset” (SRA) without short sales and then the good deal is given by borrowing more and more money so as to invest in the SRA. It is also shown that the SRA is interested by itself, even if there are short selling restrictions.

Osteoporotic hip fractures: Bisphosphonates sales and observed turning point in trend. A population-based retrospective study

The aim is to examine the temporal trends of hip fracture incidence in Portugal by sex and age groups, and explore the relation with anti-osteoporotic medication. From the National Hospital Discharge Database, we selected from 1st January 2000 to 31st December 2008, 77,083 hospital admissions (77.4% women) caused by osteoporotic hip fractures (low energy, patients over 49 years-age), with diagnosis codes 820.x of ICD 9-CM. The 2001 Portuguese population was used as standard to calculate direct age-standardized incidence rates (ASIR) (100,000 inhabitants). Generalized additive and linear models were used to evaluate and quantify temporal trends of age specific rates (AR), by sex. We identified 2003 as a turning point in the trend of ASIR of hip fractures in women. After 2003, the ASIR in women decreased on average by 10.3 cases/100,000 inhabitants, 95% CI (− 15.7 to − 4.8), per 100,000 anti-osteoporotic medication packages sold. For women aged 65–69 and 75–79 we identified the same turning point. However, for women aged over 80, the year 2004 marked a change in the trend, from an increase to a decrease. Among the population aged 70–74 a linear decrease of incidence rate (95% CI) was observed in both sexes, higher for women: − 28.0% (− 36.2 to − 19.5) change vs − 18.8%, (− 32.6 to − 2.3). The abrupt turning point in the trend of ASIR of hip fractures in women is compatible with an intervention, such as a medication. The trends were different according to gender and age group, but compatible with the pattern of bisphosphonates sales.

Spatial Interactions in Hedonic Pricing Models: The Urban Housing Market of Aveiro, Portugal

Spatial heterogeneity, spatial dependence and spatial scale constitute key features of spatial analysis of housing markets. However, the common practice of modelling spatial dependence as being generated by spatial interactions through a known spatial weights matrix is often not satisfactory. While existing estimators of spatial weights matrices are based on repeat sales or panel data, this paper takes this approach to a cross-section setting. Specifically, based on an a priori definition of housing submarkets and the assumption of a multifactor model, we develop maximum likelihood methodology to estimate hedonic models that facilitate understanding of both spatial heterogeneity and spatial interactions. The methodology, based on statistical orthogonal factor analysis, is applied to the urban housing market of Aveiro, Portugal at two different spatial scales.

Sales restriction, quality selection and the mode of competition

A regulator imposing “sales restrictions” on firms competing in oligopolistic markets may enhance quality provision by the firms. Moreover, for most restrictions levels, the impact on quality selection is invariant to the mode of competition

Reprisals remembered: German-Greek conflict and car sales during the Euro crisis

During the Greek debt crisis after 2010, the German government insisted on harshausterity measures. This led to a rapid cooling of relations between the Greekand German governments. We compile a new index of public acrimony betweenGermany and Greece based on newspaper reports and internet search terms. Thisinformation is combined with historical maps on German war crimes during theoccupation between 1941 and 1944. During months of open conflict between Germanand Greek politicians, German car sales fell markedly more than those of cars fromother countries. This was especially true in areas affected by German reprisals duringWorldWar II: areas where German troops committed massacres and destroyed entirevillages curtailed their purchases of German cars to a greater extent during conflictmonths than other parts of Greece. We conclude that cultural aversion was a keydeterminant of purchasing behavior, and that memories of past conflict can affecteconomic choices in a time-varying fashion. These findings are compatible withbehavioral models emphasizing the importance of salience for individual decision-making.

Factors Affecting the Forecasting Accuracy of Diffusion Models

Tutkielman tavoitteena oli tarkastella innovaatioiden leviämismallien ennustetarkkuuteen vaikuttavia tekijöitä. Tutkielmassa ennustettiin logistisella mallilla matkapuhelinliittymien leviämistä kolmessa Euroopan maassa: Suomessa, Ranskassa ja Kreikassa. Teoriaosa keskittyi innovaatioiden leviämisen ennustamiseen leviämismallien avulla. Erityisesti painotettiin mallien ennustuskykyä ja niiden käytettävyyttä eri tilanteissa. Empiirisessä osassa keskityttiin ennustamiseen logistisella leviämismallilla, joka kalibroitiin eri tavoin koostetuilla aikasarjoilla. Näin tehtyjä ennusteita tarkasteltiin tiedon kokoamistasojen vaikutusten selvittämiseksi. Tutkimusasetelma oli empiirinen, mikä sisälsi logistisen leviämismallin ennustetarkkuuden tutkimista otosdatan kokoamistasoa muunnellen. Leviämismalliin syötettävä data voidaan kerätä kuukausittain ja operaattorikohtaisesti vaikuttamatta ennustetarkkuuteen. Dataan on sisällytettävä leviämiskäyrän käännöskohta, eli pitkän aikavälin huippukysyntäpiste.

Comparative Analysis of Solar PV Business Models

The goal of the thesis is to analyze the strengths and weaknesses of solar PV business model and point out key factors that affect the efficiency of business model, the results are expected to help in creating new business strategy. The methodology of case study research is chosen as theoretical background to structure the design of the thesis indicating how to choose the right research method and conduction of a case study research. Business model canvas is adopted as the tool for analyzing the case studies of SolarCity and Sungevity. The results are presented through the comparison between the cases studies. Solar services and products, cost in customer acquisition, intellectual resource and powerful sales channels are identified as the major factors for TPO model.