Sales Season Research Articles

USING PORTFOLIO THEORY TO PREDICT THE IMPACT OF REDUCTION IN PRODUCT WIDTH ON SALES

Modern portfolio theory was applied to research product portfolio diversification. Prior research studies claimed a positive relationship between the number and variety of products and sales smoothing. However, they have also argued the limitations of classical portfolio analysis, especially owing to the assumption of independence of portfolio shares and the static nature of the approach. To address this limitation we extend the model to fit the trend and seasonality of sales, and to cover their correlations. The model aims to predict the impact of product width reduction on sales and their volatility. We consider the reduction of a product category in portfolio. The model covers the impact of product category sales on portfolio sales but also on other product category sales. Finally, we verify the model using a wholesaler company’s data and compare results. Hence, we recommend the model for the prediction of changes in sales and their volatility due to product category reduction. The model covers the portfolio dynamic approach to product width under the assumption of price taking, unlike in product assortment and variety planning models.

Coordinated pricing and inventory decisions for perishable products

We investigate the problem of jointly determining the optimal pricing and inventory replenishment strategy for a deterministic perishable inventory system in which demand is time and price dependent. The inventory is also assumed to decay at a certain rate. The seller has the opportunity to adjust prices for a discrete number of times at a certain cost during the sales season to influence demand and to improve revenues. We develop a mathematical model to find the optimal times to change the prices, the optimal prices and the optimal order quantity. We present analytical results to find the optimal prices when the times of price changes are given and design heuristic algorithms to determine the optimal times to change the prices. We analyze the efficiency of multiple pricing strategy by comparing the profits obtained by single pricing case and also analyze the effect of different parameters on the optimal solution through numerical experiments.

Coordination of the traditional and the online channels for a short-life-cycle product

The study considers a retailer that sells a short-life-cycle product and uses the online channel as a supplement to its traditional channel. The inventory of the product is finite. The sales season is split into two periods: the premium period and the discount period. The study investigates how the retailer coordinates two channels with proper pricing strategies and channel mix. We introduce a new strategy to address channel conflict, in which two channels are introduced at different periods and the retailer employs different pricing mechanisms for two channels. We also explore the effects of return rate, service difference, and the retailer's risk attitude. Numerical examples are used to compare the solutions with other channel selection and pricing strategies, and further investigate the situations when some assumptions are not satisfied. The study suggests that the benefits of introducing the online channel are contingent on the potential demand of the online channel, the inventory level of the product, and the time of introducing the online channel. The retailer could adjust price difference between two channels to reduce channel conflict. Its risk attitude also influences the pricing strategies and sales revenue. Managerial implications of the study are discussed.

Dynamic Pricing Under Consumer's Sequential Search

We study a firm offering a line of vertically differentiated perishable products with fixed initial inventory over a finite sales season. Consumers arrive at the firm randomly and inspect products sequentially until they find a product to purchase (if any). Each consumer incurs a positive cost to inspect a product and hence may stop the sequential search without inspecting all the available items. Upon inspection, the utility of the product is known to the consumer, who then decides whether to continue the search by comparing the utility of the best product observed thus far (i.e., the product giving the highest utility) with his reservation utility, i.e., the maximum utility he might gain if he continues the search. The firm wishes to maximize the total expected revenue from the existing inventories by offering the best sequence in which to show the products to the consumers, as well as the best price for each product at each time. We formulate the firm's optimization problem as a Markov decision process and obtain the products' purchase probabilities, and their optimal prices, given their remaining inventories and time. We show that it is optimal to present products in a descending order of quality if the reservation utility is stationary or increasing. Surprisingly, we show that the optimal price can increase over time, which is contrary to the typical finding in the well-established literature on dynamic pricing.

Pricing strategy and coordination in a dual channel supply chain with a risk-averse retailer

This paper considers a dual channel supply chain consisting of a risk-neutral supplier and a risk-averse retailer, in which the market demand is uncertain and the supplier opens an e-channel, thus directly participating in the market. At the beginning of the sales season, the supplier and the retailer construct their initial stocks, and they follow a consistent pricing strategy. Under the Value-at-Risk (VaR) criterion and the Conditional Value-at-Risk (CVaR) criterion, we formulate the problem as a Stackelberg game model and obtain the equilibrium solutions in the decentralized and centralized situations. Based on the advantage of the CVaR measure, we further explore the effects of the retailer's risk indicator on the retail price, the ordering quantities of the two channels and the profits of the two members, and the total profits of the supply chain. Further, an improved risk-sharing contract is presented to coordinate the dual-channel supply chain and ensures that both supply chain members achieve a win–win outcome. In addition, we make an extension to the case of the inconsistent prices in the two channels, and we also verify that the dual-channel supply chain can be coordinated by the similar improved risk-sharing contract.

Comparative study of holt-winters triples exponential smoothing and seasonal Arima: Forecasting short term seasonal car sales in South Africa

In this paper, both Seasonal ARIMA and Holt-Winters models are developed to predict the monthly car sales in South Africa using data for the period of January 1994 to December 2013. The purpose of this study is to choose an optimal model suited for the sector. The three error metrics; mean absolute error, mean absolute percentage error and root mean square error were used in making such a choice. Upon realizing that the three forecast errors could not provide concrete basis to make conclusion, the power test was calculated for each model proving Holt-Winters to having about 0.3% more predictive power. Empirical results also indicate that Holt-Winters model produced more precise short-term seasonal forecasts. The findings also revealed a structural break in April 2009, implying that the car industry was significantly affected by the 2008 and 2009 US financial crisis.

Inventory policy for deteriorating seasonal products with price and ramp-type time dependent demand

In this paper, we investigate the problem of simultaneously determining the order quantity and optimal prices for deteriorating products with price and ramp-type time dependent demand. We assume that a retailer has the opportunity to adjust prices before the end of the sales season to increase demand, decrease deterioration, and improve revenues. A mathematical model is developed to jointly optimize the order quantity, time interval for any two successive price changes, and the corresponding prices. An algorithm is provided to find the optimal solution to the proposed model. Finally, we use a numerical example to verify the availability of this model.

Revenue management under randomly evolving economic conditions

AbstractWe consider a dynamic pricing model in which the instantaneous rate of the demand arrival process is dependent on not only the current price charged by the concerned firm, but also the present state of the world. While reflecting the current economic condition, the state evolves in a Markovian fashion. This model represents the real‐life situation in which the sales season is relatively long compared to the fast pace at which the outside environment changes. We establish the value of being better informed on the state of the world. When reasonable monotonicity conditions are met, we show that better present economic conditions will lead to higher prices. Our computational study is partially calibrated with real data. It demonstrates that the benefit of heeding varying economic conditions is on par with the value of embracing randomness in the demand process. © 2015 Wiley Periodicals, Inc. Naval Research Logistics 66:73–89,2019

Dynamic Pricing and Inventory Management Under Network Externality

We study the impact of network externalities upon a firm’s pricing and inventory policy under demand uncertainty. The firm sells a product associated with an online service or communication network, which is formed by (part of) the customers who have purchased the product. The product exhibits network externalities, i.e., a customer’s willingness-to-pay and, thus, the potential demand are increasing in the size of the associated network. We show that a network-size-dependent base-stock/list-price policy is optimal. Moreover, the inventory dynamics of the firm do not influence the optimal policy as long as the initial inventory is below the initial base-stock level. Hence, we can reduce the the dynamic program to characterize the optimal policy to one with a single-dimensional state-space (the network size). Network externalities give rise to the tradeoff between generating current profits and inducing future demands, thus having several important implications upon the firm’s operations decisions. Compared with the benchmark case without network externalities, the firm under network externalities sets a higher base-stock level, and charges a lower [higher] sales price when the network size is small [large]. When the market is stationary, the firm adopts the introductory price strategy, i.e., it charges a lower price at the beginning of the sales season to induce higher future demands. The price discrimination and network expanding promotion strategies can effectively leverage network externalities and improve the firm's profit. Both strategies facilitate the firm to (partially) separate generating current profits and inducing future demands through network externalities. Finally, we perform extensive numerical studies to demonstrate the significant profit loss of ignoring network externalities. We also propose near-optimal heuristic policies that leverage network externalities by balancing generating current profits and inducing demands in the near future.

An Analysis of Lost Sales

The purpose of this manuscript is to shed light on problems associated with lost sales and the incurring of cost associated with lost sales. An investigation is made to determine if seasonality in sales and lost sales have effects on the efficient operations of supply chains. Optimization is always a goal of management supply chains, but cost increases due to insufficient inventory, low-quality product and the like lead to customers not returning. These are lost sales that occur for many reasons. We study a data set to determine if the ignoring of time series component also has an effect on the variation in lost sales. If so, can we measure the magnitude of the effects of seasonal variation in lost sales, and what are their consequences?

Forecasting Demand in Supply Chain Using Machine Learning Algorithms

Managing inventory in a multi-level supply chain structure is a difficult task for big retail stores as it is particularly complex to predict demand for the majority of the items. This paper aims to highlight the potential of machine learning approaches as effective forecasting methods for predicting customer demand at the first level of organization of a supply chain where products are presented and sold to customers. For this purpose, we utilize Artificial Neural Networks (ANNs) trained with an effective second order algorithm, and Support Vector Machines (SVMs) for regression. We evaluated the effectiveness of the proposed approach using public data from the Netflix movie rental online DVD store in order to predict the demand for movie rentals during an especially critical for sales season, which is the Christmas holiday season. In our analysis we also integrated data from two other sources of information, namely an aggregator for movie reviews (Rotten Tomatoes), and a movie oriented social network (Flixster). Consequently, the approach presented in this paper combines the integration of data from various sources of information and the power of advanced machine learning algorithms for lowering the uncertainty barrier in forecasting supply chain demand.

Forecasting aggregate retail sales: The case of South Africa

Forecasting aggregate retail sales may improve portfolio investors׳ ability to predict movements in the stock prices of retail chains. This paper uses 26 (23 single and 3 combination) forecasting models to forecast South Africa׳s aggregate seasonal retail sales. We use data from 1970:01–2012:05, with 1987:01–2012:05 as the out-of-sample period. Unlike the previous literature on retail sales forecasting, we not only look at a wide array of linear and nonlinear models, but also generate multi-step-ahead forecasts using a real-time recursive estimation scheme over the out-of-sample period, to better mimic the practical scenario faced by economic agents making retailing decisions. In addition, we deviate from the uniform symmetric quadratic loss function typically used in forecast evaluation exercises, by considering loss functions that overweight the forecast error in booms and recessions. Focusing on the results of single models alone shows that their performances differ greatly across forecast horizons and for different weighting schemes, with no unique model performing the best across various scenarios. However, combination forecast models, especially the discounted mean-square forecast error method, which weighs current information more than past, not only produced better forecasts, but were also largely unaffected by business cycles and time horizons. This result, along with individual nonlinear models performing better than linear models, led us to conclude that theoretical research on retail sales should look at developing dynamic stochastic general equilibrium models that not only incorporate learning behavior, but also allow the behavioral parameters of the model to be state dependent, to account for regime-switching behavior across alternative states of the economy.

Developing and Applying a Disaggregated Retail Location Model with Extended Retail Demand Estimations

The spatial interaction model (SIM) is an important tool for retail location analysis and store revenue estimation, particularly within the grocery sector. However, there are few examples of SIM development within the literature that capture the complexities of consumer behavior or discuss model developments and extensions necessary to produce models which can predict store revenues to a high degree of accuracy. This article reports a new disaggregated model with more sophisticated demand terms which reflect different types of retail consumer (by income or social class), with different shopping behaviors in terms of brand choice. We also incorporate seasonal fluctuations in demand driven by tourism, a major source of non‐residential demand, allowing us to calibrate revenue predictions against seasonal sales fluctuations experienced at individual stores. We demonstrate that such disaggregated models need empirical data for calibration purposes, without which model extensions are likely to remain theoretical only. Using data provided by a major grocery retailer, we demonstrate that statistically, spatially, and in terms of revenue estimation, models can be shown to produce extremely good forecasts and predictions concerning store patronage and store revenues, including much more realistic behavior regarding store selection. We also show that it is possible to add a tourist demand layer, which can make considerable forecasting improvements relative to models built only with residential demand.

The impact of a supermarket nutrition rating system on purchases of nutritious and less nutritious foods.

The current study examines the impact of a nutrition rating system on consumers' food purchases in supermarkets. Aggregate sales data for 102 categories of food (over 60 000 brands) on a weekly basis for 2005-2007 from a supermarket chain of over 150 stores are analysed. Change in weekly sales of nutritious and less nutritious foods, after the introduction of a nutrition rating system on store shelves, is calculated, controlling for seasonality and time trends in sales. One hundred and sixty-eight supermarket stores in the north-east USA, from January 2005 to December 2007. Consumers purchasing goods at the supermarket chain during the study period. After the introduction of the nutrition ratings, overall weekly food sales declined by an average of 3637 units per category (95 % CI -5961, -1313; P<0·01). Sales of less nutritious foods fell by 8·31 % (95 % CI -13·50, -2·80 %; P=0·004), while sales of nutritious foods did not change significantly (P=0·21); as a result, the percentage of food purchases rated as nutritious rose by 1·39 % (95 % CI 0·58, 2·20 %; P<0·01). The decrease in sales of less nutritious foods was greatest in the categories of canned meat and fish, soda pop, bakery and canned vegetables. The introduction of the nutrition ratings led shoppers to buy a more nutritious mix of products. Interestingly, it did so by reducing purchases of less nutritious foods rather than by increasing purchases of nutritious foods. In evaluating nutrition information systems, researchers should focus on the entire market basket, not just sales of nutritious foods.

Possible source term of high concentrations of mecoprop-p in leachate and water quality: impact of climate change, public use and disposal

Mecoprop-p herbicide is often found in wells and water abstractions in many areas around Europe, the UK inclusive. There is a growing environmental and public health concern about mecoprop-p herbicide pollution in ground and surface water in England. Reviews suggest that extensive work has been carried out on the contribution of mecoprop-p herbicides from agricultural use whilst more work needs to be carried out on the contribution of mecoprop-p herbicide from non-agricultural use. The study covers two landfill sites in Weaver/Gowy Catchment. Mecoprop-p herbicide concentrations in the leachate quality range between 0.06 and 290 μg l−1 in cells. High concentration of mecoprop-p herbicide in the leachate quality suggests that there is a possible source term in the waste stream. This paper addresses the gap by exploring possible source terms of mecoprop-p herbicide contamination on landfill sites and evaluates the impact of public purchase, use and disposal alongside climate change on seasonal variations in mecoprop-p concentrations. Mecoprop-p herbicide was found to exceed the EU drinking water quality standards at the unsaturated zone/aquifer with observed average concentrations ranging between 0.005 and 7.96 μg l−1. A route map for mecoprop-p herbicide source term contamination is essential for mitigation and pollution management with emphasis on both consumer and producer responsibility towards use of mecoprop-p product. In addition, improvement in data collection on mecoprop-p concentrations and detailed seasonal herbicide sales for non-agricultural purposes are needed to inform the analysis and decision process.

Pricing to accelerate demand learning in dynamic assortment planning for perishable products

Retailers, from fashion stores to grocery stores, have to decide what range of products to offer, i.e., their product assortment. Frequent introduction of new products, a recent business trend, makes predicting demand more difficult, which in turn complicates assortment planning. We propose and study a stochastic dynamic programming model for simultaneously making assortment and pricing decisions which incorporates demand learning using Bayesian updates. We show analytically that it is profitable for the retailer to use price reductions early in the sales season to accelerate demand learning. A computational study demonstrates the benefits of such a policy and provides managerial insights that may help improve a retailer’s profitability.

Managing Long Queues in Seasonal Sales Shopping

Long queues in seasonal sales shopping are seemingly undesirable for shoppers and retailers. Yet, this paper proposes that shoppers’ impatience can be a virtue for retailers. It shows that, when shoppers do queue, it is desirable under some conditions for retailers to strategically keep the queues longer rather than reducing them. This is due to two positive effects that shoppers’ queues have on retailers’ profitability: (1) to deliver seasonal promotions to low-time-cost shoppers who buy more when waiting longer in line; and (2) to turn away high-time-cost shoppers who choose to purchase at a regular time and pay the full price. Accounting for the two merits of long queues, this paper provides a numerical solution to optimizing retailers’ promotional and operational decisions related to seasonal promotions.

GENERAL ANALYSIS OF TWO PRODUCTS INVENTORY SYSTEM WITH SEASONAL PRODUCTION AND SALES

In this paper we discuss a two different products inventory system where the production has the impact of seasonal demand and sales. We consider the following model in which the production and sales of k pairs of two different products are studied subject to seasonal requirements. The production of the two different products is done alternately. The production times of the products have general distribution and the seasonal duration is exponential. The double Laplace Transform of production and sale time and their means are obtained. Numerical examples are presented.

Identifying seasonal variations in store‐level visitor grocery demand

Purpose – The purpose of this paper is to understand the contribution of visitor demand to the seasonal sales variations experienced at grocery retailers in Cornwall, South West England.Design/methodology/approach – Working collaboratively with a major UK retailer provides access to store trading information and customer data from a popular loyalty card scheme. The authors use spatial analysis to identify revenue originating from outside the store catchment, and explore the spatial and temporal nature of the visitor demand recorded in‐store.Findings – The paper demonstrates the significant degree of seasonality experienced around stores in terms of their revenue generated from out‐of‐catchment visitors, and highlights implications for store location planning. Most notably, visitor expenditure tends to demonstrate far more spatial and temporal clustering than residential demand. The authors argue that it is essential for retailers to ensure that their location planning makes full use of all available consu...

Pricing and Lot Sizing for Seasonal Products in Price Sensitive Environment

Some seasonal products have limited sales season, and the demand of such products over the sales season is of increasing-steady-decreasing type. Customers are highly sensitive to the prices of the products. In such situation, adjustment of unit selling price is needed to accelerate inventory depletion rate and for determining order quantity for the sales season. In this paper, we focus on the issue by jointly determining optimal unit selling prices and optimal lot size over the sales season. Unlike the conventional inventory models with pricing strategy, which were restricted to prespecified pricing cycle lengths, that is, fixed number of price changes over the time horizon, we allow the number of price changes to be a decision variable. The mathematical model is developed and existence of optimal solution is verified. A solution procedure is developed to determine optimal prices, optimal number of pricing cycles, and optimal lot size. The model is illustrated by a numerical example. Sensitivity analysis of the model is also carried out.