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The purpose of this paper is to present a decision model for second-hand products to determine the optimal upgrade level, warranty period and preventive maintenance (PM) effort level which maximize the total expected profit generated by the dealer considering any given past age of the product and the effect of the sales volume.

A mathematical model is developed to derive the optimal triplet: upgrade level, warranty period and PM effort level, which maximize the total expected profit generated by the dealer for any second-hand product with a given past age. Numerical experimentations have been conducted to investigate the effectiveness of the proposed model and to explore the interactions among the model variables.

Numerical experimentations including a sensitivity analysis have been conducted on the model key parameters. The obtained results show that performing PM actions during the warranty period helps the dealers to provide extended warranty for older second-hand products without spending a significant effort on upgrade actions and therefore increase the volume of sales. Also, the interaction between the PM level and the profit margin threshold is demonstrated. Finally, the effect of the sales volume function parameters (the price and warranty elasticity parameters) on the optimal solution is characterized.

Given the complexity of the profit function to be maximized involving a considerable number of decision variables with different nature, the authors limited the study to the case where the past age of the second-hand product is known.

The proposed model aims to provide second-hand product dealers with a modeling framework that enables them to have a realistic estimation of the generated profit by integrating the marketing and engineering key parameters of the second-hand product.

Most of the existing literature dealing with the reliability improvement of second-hand products does not take into account the fact that a realistic estimation of the total profit generated by the dealer requires the consideration of the sales volume. The latter is closely related to the marketing parameters characterized by the warranty period length and the second-hand product selling price. The proposed model introduces the effect of the total sales volume on the total expected profit. The authors also introduce the concept of discrete upgrade levels for a better control of the restoration degree. The authors study the impact of warranty and price elasticity parameters on the optimal solution and the resultant interaction with the customer purchase decision and consequently the sales volume.

Many studies have examined the short‐term and long‐term effects of price promotions. This study adds to previous research by examining, in some depth, the effects of a massively successful price promotion in a consumer goods category. This study sought to determine if this large price promotion had any: longer‐term effect on brand volume; short‐term effect on total category volume for the retailer; short‐term effect on competing retailers; and longer‐term effect on category sales for the retailer that ran the promotion. The results showed that this promotion did not have any longer‐term (positive or negative) effect on the brand, but it did expand the total category for the retailer, albeit temporarily. Sales dropped slightly for one competing retailer at the time of the promotion, but not for the other two retailers in the market. Finally, the study found that the promotion was followed by a decline in total category volume for the retailer, suggesting some degree of purchase acceleration or stockpiling by consumers. The results suggest that the longer‐term negative effect on category volume cancelled out approximately two thirds of the gains of the price promotion to the retailer.

The conceptual problem associated with marketing productivity analysis is examined followed by an examination of currrent practice in marketing productivity in the following areas — on the product line, in advertising and promotional mix, in the salesforce, in distribution and in customer activity tracking. It provides UK companies with some guidance on how they can improve their performance measurement using marketing information systems and reorganising existing information for more effective marketing action. The research concentrates on 50 well‐known British companies in oil, chemicals, various engineering disciplines, food, pharmaceuticals, insurance, construction and chain‐store retailing. The findings are based on 28 viable responses, and a further 21 (different) responses from companies which were personally visited. Although the research techniques need to be refined they conclude that the management of resources invested in marketing activities can never be refined to the point where an incremental investment in any specific marketing application can be measured with great accuracy. Yet a great deal of measurement is possible and marketing managers can be well enough informed about the behaviour of marketing inputs so that allocation decisions in future periods will benefit.?

Key components of the logistics mix are described in an effort to create an understanding of the total logistics concept. Chapters include an introduction to logistics; the strategic role of logistics, customer service levels, channel relationships, facilities location, transport, inventory management, materials handling, interface with production, purchasing and materials management, estimating demand, order processing, systems performance, leadership and team building, business resource management.

Managers are constantly making decisions that affect profit. One of the decision‐making areas which is crucial to all managers concerns profit planning. Attempts to show how cost‐volume‐profit (CVP) analysis, aided by the computer spreadsheet, can be applied to the practical profit planning situation in the hospitality industry. Paradoxically, CVP analysis is one of the most widely referred to techniques in managerial accounting, but all too often it is not used to its full potential in the operating environment. Aims at encouraging greater use of the CVP approach to hospitality profit planning.

The purpose of this paper is to examine how installed base information could help servitizing original equipment manufacturers (OEMs) forecast and support their industrial service sales, and thus increase OEMs’ understanding regarding the dynamics of their customers lifetime values (CLVs).

This work constitutes a constructive research aiming to arrive at a practically relevant, yet scientific model. It involves a case study that employs statistical methods to analyze real-life quantitative data about sales and the global installed base.

The study introduces a forecasting model for industrial service sales, which considers the characteristics of the installed base and predicts the number of active customers and their yearly volume. The forecasting model performs well compared to other approaches (Croston’s method) suitable for similar data. However, reliable results require comprehensive, up-to-date information about the installed base.

The study contributes to the servitization literature by introducing a new method for utilizing installed base information and, thus, a novel approach for improving business profitability.

OEMs can use the forecasting model to predict the demand for – and measure the performance of – their industrial services. To-the-point predictions can help OEMs organize field services and service production effectively and identify potential customers, thus managing their CLV accordingly. At the same time, the findings imply new requirements for managing the installed base information among the OEMs, to understand and realize the industrial service business potential. However, the results have their limitations concerning the design and use of the statistical model in comparison with alternative approaches.

The study presents a unique method for employing installed base information to manage the CLV and supplement the servitization literature.

The article seeks to discuss how and why it is possible to motivate a sales force to follow the guidelines of a consistent pricing policy that promotes profitability and increases margin, and which is beneficial to both providers and consumers of products and services.

Theories and examples stemming from the authors' research and experience with the subject matter are stated. The sales incentive theory in particular is supported by a specific formula that determines the sales credit someone would earn for making a sale based on profit contribution rather than price.

While employing flexible pricing policies in competitive markets is enticing for suppliers, it can ultimately lead to price erosion and falling margins. A sound pricing policy lets customers know that the price they are paying is related to the value they receive, and keeps salespeople from dealing with long, complicated negotiation processes. While many managers worry that their sales force won't accept such a change, most salespeople will adopt it if the new policy is implemented well. The key is to recompense salespeople for driving profitability instead of just revenue and sales volume.

This article addresses an ongoing problem that many companies face as they try to win and maintain business using flexible pricing policies to cut customer‐specific deals. It discusses how and why it is possible to change this behavior using sales incentives that reward profitable sales rather than just sales volume.

The rapid development of e-commerce has brought not only great convenience to people but a great challenge to online stores. Phenomenon such as out of stock and slow sales has been common in recent years. These issues can be managed only when the occurrence of the sales volume is predicted in advance, and sufficient warnings can be executed in time. Thus, keeping in mind the importance of the sales prediction system, the purpose of this paper is to propose an effective sales prediction model and make digital marketing strategies with the machine learning model.

Based on the consumer purchasing behavior decision theory, we discuss the factors affecting product sales, including external factors, consumer perception, consumer potential purchase behavior and consumer traffic. Then we propose a sales prediction model, M-GNA-XGBOOST, using the time-series prediction that ensures the effective prediction of sales about each product in a short time on online stores based on the sales data in the previous term or month or year. The proposed M-GNA-XGBOOST model serves as an adaptive prediction model, for which the instant factors and the sales data of the previous period are the input, and the optimal computation is based on the proposed methodology. The adaptive prediction using the proposed model is developed based on the LSTM (Long Short-Term Memory), GAN (Generative Adversarial Networks) and XGBOOST (eXtreme Gradient Boosting). The model inherits the advantages among the algorithms with better accuracy and forecasts the sales of each product in the store with instant data characteristics for the first time.

The analysis using Jingdong dataset proves the effectiveness of the proposed prediction method. The effectiveness of the proposed method is enhanced and the accuracy that instant data as input is found to be better compared with the model that lagged data as input. The root means squared error and mean absolute error of the proposed model are found to be around 11.9 and 8.23. According to the sales prediction of each product, the resource can be arranged in advance, and the marketing strategy of product positioning, product display optimization, inventory management and product promotion is designed for online stores.

The paper proposes and implements a new model, M-GNA-XGBOOST, to predict sales of each product for online stores. Our work provides reference and enlightenment for the establishment of accurate sales-based digital marketing strategies for online stores.

In contrast to rents which are prescribed by contract to remain fixed or constant during the term of a lease, the expression variable rents simply implies that the rent to be paid during the tenure of occupancy will be subject to change with the passage of time.

The purpose of this paper is to analyze the status of gross and net working capital and their association with sales of Andhra Pradesh Paper Mills Ltd, with reference to the Indian paper industry over a decade, from 1999 to 2008.

The research is mainly based on secondary financial data obtained from the Centre for Monitoring Indian Economy (CMIE). It focused on the size, character, and annual growth rates of gross and net working capital of the company. In addition, it analyzed the growth trends of gross and net working capital of the company in relation to sales. With the help of the Karl Pearson's correlation model, the inter‐relationship between sales and working capital has been identified. Then the strength and significance of such a relationship has been tested with the use of other statistical tools such as coefficient of determination and Student's t‐test.

The major findings of the research showed that while there was an increase in sales positively, strongly, and significantly associated with an increase in gross working capital for both the company and the industry, its association with net working capital was negative, poorly related, weak, and insignificant for the company under study.

There is a dearth of studies in the world literature that discuss the relationship that exists between sales and working capital in India's paper industry, in general and Andhra Pradesh Paper Mills Ltd in particular, and therefore this research is expected to add significant value to exploring the said linkage.