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The influence of weather on business activities and human behaviour has been explored in several fields (e.g. finance and psychology), but little research about weather and retail sales is found in the retail or fashion literature. The purpose of the study is to analyse the relationship between temperature, one aspect of weather, and retail sales of seasonal garments.

The researchers collected sales data from a retailer of branded women's business wear in the Seoul‐Kyunggi area in South Korea. Along with the sales data for seasonal basic styles, corresponding daily and weekly average temperature data were collected and evaluated. The analysis for the study was drawn using descriptive statistics including graphical evaluations, correlation analysis and paired samples t‐test. Interviews with the retailer's merchandisers were used to supplement interpretation of the statistical data.

Results of this study provide strong evidence that fluctuations in temperature can impact sales of seasonal garments. During sales periods when drastic temperature changes occurred, more seasonal garments were sold. However, the temperature changes from day to day or week to week did not affect the number of garments sold for the whole season. Of the seasonal garments expected to sell within the same season, the selling periods of each product category differed depending on type of fabric and design. For some seasonal garments, the actual sales dates were one week to two weeks in variance from the merchandisers' forecasts.

Limitations in the sample (i.e. product category) and location of stores (i.e. geographic region) prevent the generalization of results to all seasonal garments or retailers. In spite of these limitations, this study can be a pilot study that supports the significant relationship between temperature and sales of seasonal basic products by quantifying the temperature effects on sales of particular products. Therefore, future studies are needed to establish generalized conclusions with a larger sample.

As little academic research is available about weather's effect on sales of garments, the present study contributes to the field of clothing and retail distribution by providing evidence of significant relationships between temperature and sales of seasonal clothing.

A family business founded by Chinese immigrants grew into a $133 million toy and costume maker by exploiting seasonal niche segments in the highly competitive, global toy industry. Sales of traditional toys stagnated when replaced by game consoles and electronic toys. Unable to compete in high tech toys, MegaToys moved instead toward seasonal products. In 2007, brothers Peter and Charlie Woo were about to pitch what they hoped would be $63 million in Easter basket sales to Wal-Mart. If Wal-Mart took the full order, it would come to represent over half of MegaToys' revenue.

The company was faced with the dilemma of how to grow, and at what pace. Charlie Woo knew that MegaToys could continue to grow as long as it was able to satisfy Wal-Mart's demands. Peter Woo wondered if this was the smartest way to grow the business. “Growth is a good thing as long as you don't sell your shirt to get it,” he noted. Should MegaToys continue to increase its sales to Wal-Mart, or would dependence on Wal-Mart eventually threaten the firm's success? Were there other, untapped opportunities for MegaToys that were well aligned with its strengths, resources, and capabilities?

Farmers’ markets in the United States are structured in various ways. Even those once‐or‐twice‐a‐week markets that remain outside of the mass production and distribution system by requiring that all goods sold be produced by the seller take two distinct forms. The varieties of produce sold, the number of choices offered customers, the prices charged, the age and income expectations of the sellers, the rules the sellers obey and the role of the sellers in writing and enforcing those rules are consistent within each type of informal, American farmers’ markets but are quite different between the two types.

Delivery punctuality is essential in supply chain management, yet the cost of untimely delivery is usually assumed to be given or based on intuition and not quantified by facts.

The authors used a data set containing detailed transaction data for a nine-year period on orders and deliveries of sport goods. The methodology is based on applying a polynomial distributed lag model to longitudinal data on supply chain transactions.

The results indicate that small delivery delays up to two weeks decrease the sales by maximum 10% during a period of 3–4 weeks. Longer delays, up to 45 days, have a larger negative effect on sales, which can also last longer. For this case company, the estimated lost sales due to late deliveries (=5 days) were 5.1% of the delivery value. The longer delays got, the large the cost was: delays at least 45 days long were the most costly causing almost 40% of the estimated lost sales.

This study offers a methodology for quantifying lost sales due to delivery delays and estimating how long the poor delivery performance affects retailers' order behaviour.

The results give a quantitative decision-making tool for supply chain managers to estimate the profitability of investments in the supply chain performance, especially on improving punctuality.

In an effort to win back consumer spending that is increasingly being diverted into areas not covered by the retail sector, such as housing, cars, entertainment and holidays, it is imperative that shops develop a clear and winning formula to retrieve the capricious shopper. The task is made harder by the growing competition in the retail sector itself and by the more sophisticated, discerning and demanding nature of today's potential customers. At a one‐day conference, “Retail Strategies for the 1980s”, organised by Mintel Publications and which took place at the Barbican Centre, London, in June, a wide group of retailers gave presentations outlining their strategy options and evaluating their prospects for success. RDM was there.

The purpose of this article is to propose a planning process that takes into account that manufacturers of original equipment have products at different stages of the product‐life cycle, and utilizes sales and inventory information collected from distributors and retailers.

The research paper describes the construction and testing of a planning process.

Trials in a case company indicate that supply chain responsiveness can be improved in product launches using the proposed process. Supply chain efficiency in the maturity phase can also be improved.

The usefulness and effectiveness of the proposed process depend on the assumption that product mix changes can be modeled and point‐of‐sales and channel sell‐through data are available regularly and reliably.

Modeling and monitoring the variant mix on the total market level can be used to improve supply chain responsiveness to mix changes in product launches. The introduction of this planning process reduces the need for planning in the sales units.

The paper shows how the quality of variant forecasting for an original equipment manufacturer can be improved with access to channel visibility in the market introduction phase.

Seasonal fluctuation interference often affects the relational analysis of economic time series. The main purpose of this paper is to propose a new grey relational model for relational analysis of seasonal time series and apply it to identify and eliminate the influence of seasonal fluctuation of retail sales of consumer goods in China.

First, the whole quarterly time series is divided into four groups by data grouping method. Each group only contains the time series data in the same quarter. Then, the new series of four-quarters are used to establish the grey correlation model and calculate its correlation coefficient. Finally, the correlation degree of factors in each group of data was calculated and sorted to determine its importance.

The data grouping method can effectively reflect the correlation between time series in different quarters and eliminate the influence of seasonal fluctuation.

In this paper, the main factors influencing the quarterly fluctuations of retail sales of consumer goods in China are explored by using the grouped grey correlation model. The results show that the main factors are different from quarter to quarter: in the first quarter, the main factors are money supply, tax and per capita disposable income of rural residents. In the second quarter are money supply, fiscal expenditure and tax. In the third quarter are money supply, fiscal expenditure and per capita disposable income of rural residents. In the fourth quarter are money supply, fiscal expenditure and tax.

This paper successfully realizes the application of grey relational model in quarterly time series and extends the applicable scope of grey relational model.

The purpose of this paper is to show how a critical realist paradigmatic stance and its associated research methodology can contribute to supply‐chain research by providing explanations for specific supply‐chain‐ and logistics‐related dynamic phenomena.

Initially, the case for a critical realist research paradigm is made, and then a retroductive pluralistic research methodology is used for demonstrating its application. Starting from an observation in the distribution part of a seasonal goods supply chain, ethnographic‐like field research suggested deeper social structures as being responsible for the events observed. The operation of event‐generating mechanisms related to these structures was matched to existing behavioural theories using dynamic modelling and simulation.

The adoption of the critical realist perspective and its pluralistic research methodology can bring into surface the root causes of, and explain, complex supply chain phenomena. In the particular case presented, it provided an explanation for the inventories observed in a supply chain of perishable seasonal goods as results of two underlying interacting mechanisms: one related to the promotions bias of the manufacturer, and one related to the risk management attitude of resellers.

As far as underpinning philosophy and research methodology are concerned, the research presented is globally significant and valid. Nevertheless, any supply‐chain management policies derived in the demonstrative case as results of the explanation may be significant only to specific industries and geo‐historical contexts.

Results obtained by employing the critical realist perspective may be used in managers' education for developing intellectual frameworks to better understand the causes of complex supply chain phenomena. Of the same importance to practitioners is the methodology and inference process used for explaining real abnormal situations and intervening accordingly.

The paper shows how a critical realist perspective and its associated methodology can be used for extracting/researching deeper mechanisms responsible for observed behaviours in supply chains. Such an approach is in the opposite direction with respect to the hypothetico‐deductive approaches that dominate supply chain research. The paper demonstrates the adoption of the critical realist perspective in supply chain research using a real case.

The purpose of this paper was to investigate what types of supply chain strategies (SCS) are implemented within luxury fashion companies, according to the drivers that regulate competitiveness in this sector (brand positioning, distribution channel, type and line of product). Moreover, the objective was to define which key performance indicators (KPIs) should be measured according to the chosen strategy, and finally to evaluate the alignment of luxury fashion companies with the proposed indicators.

The literature review was the first step performed. Thereafter, a case study was conducted and the sample, composed of six companies, was selected, a questionnaire was then developed to guide the interviews, after which the data were collected. From the data, a primary case analysis was conducted, from which cross-case patterns were also researched.

From the results obtained, it was possible to state that companies involved in the case study adopted different SCS within the same company according to the drivers that regulate the sector competitiveness. As a result, the product line was shown to be the only driver that affected both the alignment between the expected and implemented SCS, respectively, and the alignment with the selected KPIs.

The paper provides valuable insights to companies that are trying to align SCS and KPIs. The close link between these aspects had not yet been explored previously. In particular, there were no indications about the KPIs that have to be measured for a specific SCS.