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This study aims to investigate the pricing dynamics within a triple-channel supply chain. The publisher can sell printed books (p-books) through bookstores or online direct sales, and electronic books (e-books) are sold directly through the internet. The primary objectives include determining optimal wholesale and final prices for p-books, assessing the profitability of introducing e-books, comparing profits across channels and supply chain modes and identifying optimal demand volumes.

The research uses first-order derivatives and the Stackelberg game to analyze the pricing strategies. Two supply chain modes, centralized and decentralized, are considered, and various parameters are examined to understand their impact on prices, demand volumes and final sales profit.

The results indicate that the e-book is either not published or is introduced simultaneously with the printed version in both modes. In the decentralized mode, the wholesale price of a p-book is equivalent to the final price in the bookstore channel in the centralized mode. One channel among the three selling channels is used to maximize the total profit in the centralized supply chain, whereas all demand should be fulfilled through either online direct sales or e-book channels in the decentralized mode.

This paper introduces a comprehensive triple-channel book supply chain model, considering cross-price sensitivities and lag times for e-books. The study provides insights into the dynamics of the book industry and compares them with existing literature, contributing to a broader understanding of the pricing strategies in a triple-channel context.

The purpose of this paper is to further develop the Decision Making Grid (DMG) proposed by Ashraf Labib (e.g. Labib, 1998, 2004; Fernandez et al., 2003; Aslam-Zainudeen and Labib, 2011; Stephen and Labib, 2018; Seecharan et al., 2018) by proposing an innovative solution for determining proactive maintenance tactics based on mean time between failures (MTBF) and mean time to repair (MTTR) indicators.

First, the influence of MTTR and MTBF indicators on proactive maintenance tactics was computed. The tactics included risk-based maintenance (RBM), reliability-centered maintenance (RCM), total productive maintenance (TPM), design out maintenance (DOM), accessibility-centered maintenance (ACM) and business-centered maintenance (BCM). Then, the tactics were allocated to the cells of a DMG with MTTR and MTBF axes. The proposed approach was examined on 32 pieces of equipment of the Esfahan Steel Company and appropriate maintenance tactics were consequently determined.

The findings indicate that the DOM, BCM, RBM and ACM tactics with weights of 0.86, 0.94, 0.68 and 1.00 are located at the corners of the DMG, respectively. The two remaining tactics of TPM and RCM are located at the middle corners. Also, the results indicate that the share of tactics per spotted equipment in the grid as 62, 22 and 16 percent for RCM, DOM and BCM, respectively.

While reactive and preventive maintenance strategies include corrective, prospective, predetermined, proactive and predictive policies, the focus of this study was merely on the tactics of proactive maintenance policy. The advantage of the developed DMG over Labib’s DMG lies in its application for equipment with the unique condition of the bathtub curve.

While the basic DMG has been mostly used regardless of the type of maintenance policies, this study provides a DMG for a specific application regarding the proactive policy. In addition, the heuristic approach proposed for the development of DMG distinguishes this study from other studies.