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The purpose of this paper is to document the outcome of a global three‐year long supply chain improvement initiative at a multi‐national producer of branded sporting goods that is transforming from a holding structure to an integrated company. The case company is comprised of seven internationally well‐known sport brands, which form a diverse set of independent sub‐cases, on which the same supply chain metrics and change project approach was applied to improve supply chain performance.

By using in‐depth case study and statistical analysis the paper analyzes across the brands how supply chain complexity (SKU count), supply chain type (make or buy) and seasonality affect completeness and punctuality of deliveries, and inventory as the change project progresses.

Results show that reduction in supply chain complexity improves delivery performance, but has no impact on inventory. Supply chain type has no impact on service level, but brands with in‐house production are better in improving inventory than those with outsourced production. Non‐seasonal business units improve service faster than seasonal ones, yet there is no impact on inventory.

The longitudinal data used for the analysis is biased with the general business trend, yet the rich data from different cases and three‐years of data collection enables generalizations to a certain level.

The in‐depth case study serves as an example for other companies on how to initiate a supply chain improvement project across business units with tangible results.

The seven sub‐cases with their different characteristics on which the same improvement initiative was applied sets a unique ground for longitudinal analysis to study supply chain complexity, type and seasonality.

The accurate measurement of demand variability amplification across different nodes in the supply chain, or “bullwhip effect,” is critical for firms to achieve more efficient inventory, production, and ordering planning processes. Building on recent analytical research that suggests that data aggregation tends to mask the bullwhip effect in the retail industry, the purpose of this paper is to empirically investigate whether different patterns of data aggregation influence its measurement.

Utilizing weekly, product-level order and sales data from three product categories of a consumer packaged goods manufacturer, the study uses hierarchical linear modeling to empirically test the effects of data aggregation on different measures of bullwhip.

The authors findings lend strong support to the masking effect of aggregating sales and order data along product-location and temporal dimensions, as well as the dampening effect of seasonality on the measurement of the bullwhip effect.

These findings indicate that inconsistencies found in the literature may be due to measurement aggregation and statistical techniques, both of which should be applied with care by academics and practitioners in order to preserve the fidelity of their analyses.

Using product-weekly level data that cover both seasonal and non-seasonal demand, this study is the first, to the author’s knowledge, to systematically aggregate data up to category and monthly levels to empirically examine the impact of data aggregation and seasonality on bullwhip measurement.

The banana is one food which is always readily available no matter what season it is. Its history and growth are fascinating and now its entry to UK is controlled by EEC restrictions on quantities and tariff controls.

The purpose of this paper is to study how variations in weather affect demand and supply chain performance in sport goods. The study includes several brands differing in supply chain structure, product variety and seasonality.

Longitudinal data on supply chain transactions and customer weather conditions are analysed. The underlying hypothesis is that changes in weather affect demand, which in turn impacts supply chain performance.

In general, an increase in temperature in winter and spring decreases order volumes in resorts, while for larger customers in urban locations order volumes increase. Further, an increase in volumes of non-seasonal products reduces delays in deliveries, but for seasonal products the effect is opposite. In all, weather affects demand, lower volumes do not generally improve supply chain performance, but larger volumes can make it worse. The analysis shows that the dependence structure between demand and delay is time varying and is affected by weather conditions.

The study concerns one country and leisure goods, which can limit its generalizability.

Well-managed supply chains should prepare for demand fluctuations caused by weather changes. Weekly weather forecasts could be used when planning operations for product families to improve supply chain performance.

The study focuses on supply chain vulnerability in normal weather conditions while most of the existing research studies major events or catastrophes. The results open new opportunities for supply chain managers to reduce weather dependence and improve profitability.

Aiming to address the forecasting dilemma of seasonal air quality, the authors design the novel self-adaptive seasonal adjustment factor to extract the seasonal fluctuation information about the air quality index. Based on the novel self-adaptive seasonal adjustment factor, the novel seasonal grey forecasting models are established to predict the air quality in China.

This paper constructs a novel self-adaptive seasonal adjustment factor for quantifying the seasonal difference information of air quality. The novel self-adaptive seasonal adjustment factor reflects the periodic fluctuations of air quality. Therefore, it is employed to optimize the data generation of three conventional grey models, consisting of the GM(1,1) model, the discrete grey model and the fractional-order grey model. Then three novel self-adaptive seasonal grey forecasting models, including the self-adaptive seasonal GM(1,1) model (SAGM(1,1)), the self-adaptive seasonal discrete grey model (SADGM(1,1)) and the self-adaptive seasonal fractional-order grey model (SAFGM(1,1)), are put forward for prognosticating the air quality of all provinces in China .

The experiment results confirm that the novel self-adaptive seasonal adjustment factors promote the precision of the conventional grey models remarkably. Simultaneously, compared with three non-seasonal grey forecasting models and the SARIMA model, the performance of self-adaptive seasonal grey forecasting models is outstanding, which indicates that they capture the seasonal changes of air quality more efficiently.

Since air quality is affected by various factors, subsequent research may consider including meteorological conditions, pollutant emissions and other factors to perfect the self-adaptive seasonal grey models.

Given the problematic air pollution situation in China, timely and accurate air quality forecasting technology is exceptionally crucial for mitigating their adverse effects on the environment and human health. The paper proposes three self-adaptive seasonal grey forecasting models to forecast the air quality index of all provinces in China, which improves the adaptability of conventional grey models and provides more efficient prediction tools for air quality.

The self-adaptive seasonal adjustment factors are constructed to characterize the seasonal fluctuations of air quality index. Three novel self-adaptive seasonal grey forecasting models are established for prognosticating the air quality of all provinces in China. The robustness of the proposed grey models is reinforced by integrating the seasonal irregularity. The proposed methods acquire better forecasting precisions compared with the non-seasonal grey models and the SARIMA model.

Little research on the influence of external factors, such as weather and holiday periods, on retail sales on alcoholic beverages is available. This study aims to investigate how weekly retail sales of different alcoholic beverages vary in association with daily maximum temperatures and annual federal holidays across selected US counties in the years 2013 to 2015. The research provides information, which can contribute to better sales forecasts.

Secondary data of weekly retail sales (volume) of alcoholic beverages from 37,346 stores in 651 counties in the USA are analysed. The data cover on average 21% of all existing US counties and 12% of the total US off-trade retail sales of alcoholic beverages in the period studied (Euromonitor, 2017). Additional data of federal holidays and meteorological data are collated for each county in the sample. Seasonal autoregressive integrated moving average models with exogenous regressors (SARIMAX) are applied to develop forecasting models and to investigate possible relationships and effects.

The results indicate that off-trade retail sales of beer, liquor, red and white wine are temperature sensitive throughout the year, while contrary to expectations rosé, sparkling and other wines are not. Sales sensitivities to temperature also differ by geography. In the warmest regions, liquor and white wine sales do not respond to temperature changes, as opposed to the coolest regions, where they are responsive. Public holidays, particularly Easter, Thanksgiving, Christmas and New Year holidays, represent a constant influencing factor on short-term sales increases for all investigated alcoholic beverage categories.

This is the first large-scale study of weather and holiday-related sales variations over time, across geographies and different alcoholic beverage categories. Seasonal and non-seasonal short-term sales variations are important for retailers and manufacturers alike. Accounting for expected changes in demand accommodates efficiencies along the supply chain and has implications for retail management, as well as adjusting marketing efforts in competing categories.

This paper aims to propose a hybrid-forecasting model for long-term tourism demand forecasting. As such, it attempts to model the tourism demand in the Philippines, which is a relatively underrepresented area in the literature, despite its tourism sector’s growing economic progress.

A hybrid support vector regression (SVR) – seasonal autoregressive integrated moving averages (SARIMA) model is proposed to model the seasonal, linear and nonlinear components of the tourism demand in a destination country. The paper further proposes the use of multiple criteria decision-making (MCDM) approaches in selecting the best forecasting model among a set of considered models. As such, a preference ranking organization method for enrichment of evaluations (PROMETHEE) II is used to rank the considered forecasting models.

The proposed hybrid SVR-SARIMA model is the best performing model among a set of considered models in this paper using performance criteria that evaluate the errors of magnitude, directionality and trend change, of a forecasting model. Moreover, the use of the MCDM approach is found to be a relevant and prospective approach in selecting the best forecasting model among a set of models.

The novelty of this paper lies in several aspects. First, this paper pioneers the demonstration of the SVR-SARIMA model’s capability in forecasting long-term tourism demand. Second, this paper is the first to have proposed and demonstrated the use of an MCDM approach for performing model selection in forecasting. Finally, this paper is one of the very few papers to provide lenses on the current status of Philippine tourism demand.

Discusses the development and evaluation of a forecasting model for inventory management in an advanced technology batch production environment. Traditional forecasting and inventory management do not adequately address issues relating to a short life cycle and to non‐seasonal products with a relatively long lead time. Limited historical data (fewer than 100 observations) is also a problem in predicting short‐term dynamic or unstable time series. A Bayesian dynamic linear time series model is proposed as an alternative technique for forecasting demand in a dynamically changing environment. Provides details of the important characteristics and development process of the forecasting model. A case study is then presented to illustrate the application of the model based on data from a multinational company in Singapore. It also compares the Bayesian dynamic linear time series model with a classical forecasting model (auto‐regressive integrated moving average (ARIMA) model).

Internal migration has grown intensively in India in the present decades, far greater than international migration, though the latter has received far more attention in literature and public policy. Among internal migrants, seasonal movement is another growing phenomenon in India which has received the least attention till now. The purpose of the study is to show the intensities of short-term morbidity and major morbidity among the rural and urban internal migrants and how such disease burdens have affected the health of regular/permanent and temporary/seasonal migrants.

This present paper has been developed on the basis of data of India Human Development Survey-II (IHDS-II), 2011–2012, has been availed to find out the intensities of short-term morbidity and major morbidity among the rural and urban migrants as well as the health condition of the seasonal migrants. For the analysis of regular or permanent migrants, a total of 3,288 migrants (of which 1,136 rural migrants and 2,152 urban migrants) were surveyed in IHDS-II, 2011–2012, regarding the persistence of different types of short-term morbidity among the migrant class. Two-sample (rural migrants and urban migrants) “t” test for mean difference with unequal variances with null hypothesis – H0: diff = 0, and alternate hypothesis – Ha: diff < 0; Ha: diff > 0 where diff = mean (rural) – mean (urban) has been executed. For the seasonal migrants a sample of 41,424 migrants of which 2,691 seasonal migrant workers and 38,733 non-seasonal migrant workers were surveyed in IHDS-II, 2011–2012, to find out their health condition. OLS regression on the number of medical treatments undertaken in a month on the nature of migrant workers has been conducted. Socio-economic factors (like adult literacy) and basic amenities required for a healthy living (like indoor piped drinking water, separate kitchen in the household, household having a flush toilet, household having electricity and intake of meals everyday) are taken as control variables in the regression analysis.

The results of morbidity analysis in this paper show that the morbidity patterns among the migrants vary with the geographical differences. The short-term morbidity and that of the major morbidity show different proneness to ill health for rural and urban migrants. However, seasonal migrants are more susceptible to ill health than the regular migrants and are also potential for generating health risks. Also lack of provision of basic services creates negative health impact on seasonal migrants.

The paper is based on secondary data and hence lacks numerous relevant health issues of migrants in rural and urban sectors which could have been possible through primary data survey.

Migration and migrants are a relevant issue both internationally and nationally. Economic development of a country like India depends to a greater extent on the contributions of migrant labourers as majority of the labourers in India belong to informal sector of which most of the workers are from migrant class.

Migrants contribution to economic development depend on their productive capacity and hence health of these section of people is a relevant issue. This study is based on the morbidity pattern of migrants both regular and seasonal migrants and their susceptibility in various geographical locations and provision of basic amenities.

This work is original research study by the author.

The purpose of this paper is to consider the issue of forecasting hotel room rate with data from 2004 onwards and the forecast horizons of 91 observations.

This study employs a set of time series data (91 observations) on average monthly hotel room rates to generate an Autoregressive Integrated Moving Average Models (ARIMA) forecasting model.

Through the employment of 74 percent observations, with 26 percent withhold for evaluation checking, six best models are identified from 50 models under study. The final model reports a high level of predictive accuracy and provides useful insights for hoteliers to form business strategies.

This research provides a well-defined model to forecast the room rate of three-star hotels in the city. The research findings provide good ground for strategic management of the industry, in which there is an imbalance between demand and supply of hotel accommodations. In addition, being the first of its kind in one of the largest gaming revenue generation city in the world, this research provides valuable information for further research of its kind in the future.