Supply and demand uncertainties combined with very short lifetime of blood platelets has led to significant wastage of the total blood collected from the donors. Conversely, great shortage of platelets may be obtained due to the limited number of donors and emergency demands. Therefore, it is of utmost importance to develop appropriate inventory management model to simultaneously minimize both shortage and wastage along the blood supply chain. To achieve this purpose, this paper presents an Inventory Management model for Age- differentiated platelets under supply/demand Uncertainties (IMAU) for Blood Supply Chains with Lateral Transshipment (BSCLT), resulting a new model named IMAU-BSCLT. The proposed model is solved using whale optimization algorithm considering the costs of ordering from blood centers and lateral transshipment, transportation, inventory holding, shortage, and wastage. In order to validate the proposed methodology, a case study of blood supply chain is used to show the usability of the proposed model and claim its benefits over existing models. Simulation results demonstrate that lateral transshipment between different demand nodes has a major impact on load balancing leads to simultaneously reduce both shortage and wastage costs. According to the obtained results, shortage rate (total shortage per total demands) and wastage rate (total wastage per total supply) of the proposed method are 3.4 % and 4.8 %, respectively.
Keywords: Blood supply chain | Inventory management | Blood platelet | Lateral transshipment | Fuzzy uncertainty

The paper explores the influence of online review adoption on supply chain profitability under the presence of a capacity constraint. Nowadays, customers increasingly rely on online reviews for decision making, and online retailers regard reviews as a norm. Although online reviews have been extensively examined in marketing disciplines, little research has been conducted to investigate their influence from a supply chain perspective. In addition, previous research has largely focused on how online review information can influence customer purchase behaviours, but ignores the more basic decision: whether and when companies should adopt reviews. This paper examines the online review adoption decision from a capacitated supply chain perspective through mathematical modelling and simulation. The simulation considers the influence of variables including online review adoption decisions, capacity constraint level, lost sales penalty level and product quality estimation on supply chain profitability. Generally, we find that online reviews can bring more profit to the supply chain than without online reviews, although such influence is moderated by the other three variables. The findings reveal the complexity of the contextual variable impacts on online review adoption, and demonstrate that decisions concerning the adoption of online reviews should take all supply-chain-related variables into consideration rather than only aiming for increasing customer orders.
Keywords: Online reviews | supply chain | capacity constraint | simulation | inventory management

The efficiency of inventory management determines the customers’ buying experience, so a supply chain network with a shorter replenishment time is needed. The supply chain network is hoped to be robust to the stock-out of some distributors in the network under random customer demands. At the same time, replenishment path optimization method with the objective of minimizing the replenishment time is required. After reviewing previous work done in the field of supply network topology, scale-free network is proven to be efficient when it was used to model supply network. In addition, multi-agent based collaborative replenishment model is smarter. But, there is rare research on multi-agent based collaborative replenishment in the supply chain modelled by scale-free network. In this study, we proposed a spare parts supply chain network model based on a novel scale- free network. In this network growth process, the connection probability function of connecting new distributor to the existing distributors in the network, is constructed considering the connection number (for an existing distributor, its connection number means the number of other distributors which have collaborative relationship with it) and inventory capacity of the existing distributors and the transit time between new distributor and existing distributors. The connection probability function is built from the standpoints of both new distributor and the existing distributors. Furthermore, different selection policies are discussed in the network growth process to improve the efficiency. Unlike other replenishment path optimization methods, Q learning takes the advantage of interacting with the environment to make a dynamic decision. So, Q learning is selected to optimize the replenishment path in supply chain network. In the experiment, network static and dynamic performance is analyzed using the indicators: degree distribution, clustering coefficient, centrality and response time. Experi- mental results showed that the replenishment time of supply chain network which are optimized by Q learning is reduced by approximately 40%. So, the shorter replenishment time of the supply chain network is verified.
Keywords: Spare parts supply chain network | Scale-free network | Q learning algorithm | Random customer demands

Unprecedented competition and emergent technologies have posed a challenge to many traditional retailers in recent years. Yet within this competitive environment, emerging innovative business models have thrived and successfully disrupted the industry. We analyze the nature of disruptive business-model innovations and the ways they disrupt the fashion retail industry. To that end, we examine three disruptors in the industry: born-digital brands, AI-enabled demand forecasting and product design, and collaborative consumption. After introducing the concept of disruptive business-model innovation, we discuss the three disruptors’ effects on the fashion industry. We find that all of these models keenly answer fundamental needs unmet by current business models, such as offering quality products at a competitive price, curated services, and sustainable consumption. At the same time, all three disruptors suggest effective operation models for handling demand uncertainty, inventory management, and timely responses to the market, all of which are inherent issues for current push supply chains and forecast-based, inventory-driven systems. Based on this analysis, we discuss important implications for both academics and industry practitioners.
KEYWORDS: Disruptive innovation | Business model | Inventory management | Demand forecasting | Fashion industry | Online retail

Assortment planning deserves much attention from practitioners and academics due to its direct impact on retailers’ commercial success. In this paper we focus on the increasingly popular retail practice to use combined product assortments with both “standard”and more fashionable and short-lived “variable”products for building up store traffic of “loyal”and “non-loyal”heterogeneous customers and enlarging the sales due to the potential cross-selling effect. Addressing the assortment planning as a bilevel optimization problem, we focus on decision-dependent uncertainties: the retailer’s binary decision about product inclusion influences the distribution of the product’s demand. Furthermore, our model accounts for customers’ optimal purchase quantities, which depend on budget constraints limiting the basket that a customer is able to purchase. We propose iterative heuristics using optimal quantization of demand and customers budget distributions to define the total assortment and the inventory level per product. These heuristics provide lower bounds on the optimal value. We conduct a comparison to other existing lower bounds and we formulate upper bounds via linear (LP) and semidefinite (SDP) relaxations for the performance evaluation of the heuristics and for an efficient numerical solution in high-dimensional cases. For managerial insights, we compare the proposed approach with three assortment planning strategies: (1) the retailer does not carry variable products; (2) the retailer ignores the cross-selling effect; and (3) the maximum space allocated to each product is fixed. Our results suggest that variable assortment boosts the retailers profits if the cross-selling effect is not neglected in the decision about products quan- tities.
Keywords: OR in marketing | Assortment planning | Inventory management | Bilevel optimization | Stochastic optimization