# Green Spare Parts Evaluation for Hybrid Warehousing and On-Demand Manufacturing
## 1 Introduction  
Spare parts evaluation studies and the Spare Parts and Interchangeability Record (SPIR) ensure spare part recommendations for projects, modifications, and operations. The SPIR process consists of activities from operators, contractors, and suppliers and must be delivered to support the maintenance strategy for equipment units. Several standards, such as NORSOK Z-008 [1], highlight the evaluation of spare parts as a strategic decision that should be studied during the project phase. It recommends that the evaluation be based on the consequence of the unavailability and demand rates of spare parts. Most of these SPIR analyses and related standards are dominant for physical storage [2]. The advent of Industry 4.0 has significantly transformed spare part management and warehousing and introduced new challenges and novel spare part inventory cases [3, 4, 5, 6, 7]. Additive manufacturing, due to its shorter lead time, offers another way to stock physical spare parts. It offers to stock 3D files instead of physical parts and manufactures them on demand. The storage of 3D files and the manufacture on demand is often referred to in the literature as a digital warehouse [8]. Both additive manufacturing technology and digital warehouse concepts offer a new way for industries to manage and maintain their spareparts inventory by enabling on-demand production, reducing lead times, allowing customization, simplifying inventory, and enhancing supply chain resilience [9]. The chemical industry [10] and the oil and gas sector [11; 12; 13] are expected to have great potential to use additive manufacturing and digital storage. For example, the offshore oil and gas sector aims to take advantage of additive manufacturing and digital storage, mainly by reducing on-site stock, stock in local warehouses, and transportation and emissions related to the global supply process. Figure 1 illustrates the green spare parts storage and supply option compared to transnational physical storage and supply. Therefore, it is necessary to upgrade the current SPIR process to consider both physical and digital storage options to allow further green spare parts storage and supply. There are significant contributions to helping SPIR engineers select spare parts suitable for additive manufacturing [14; 15]. These spare parts selection models shall also be supported with models to help SPIR engineers design the entire spare parts inventory system. In the latest review paper on the management of spare parts inventory [16], around 86 studies were reviewed where different deterministic, heuristic and simulation models were explored. Spare parts inventory analysis has evolved with a variety of advanced methodologies and models aimed at optimizing spare parts inventory to reduce costs and improve system availability. These approaches integrate predictive [17; 18], optimization [19; 20], and prescriptive analytics [21; 22] to address the complexities of spare part management, which is crucial for maintenance and operational efficiency. [PERSON] et al. [17] introduced a Hybrid Memetic Algorithm that optimizes preventive maintenance scheduling and inventory costs. [PERSON] et al. [19] highlight the use of (s,S) policies and genetic algorithms to minimize management expenses and maximize service levels within a variable periodic demand inventory model. [PERSON] and [PERSON] [21] applied multi-agent hierarchical reinforcement learning to dynamically control inventory levels, offering greater efficiency in calculation time compared to neural network-based algorithms. [PERSON] et al. [20] used Bayesian degradation modeling to minimize expected costs by ensuring that prior information about component degradation is accurate. [PERSON] et al. [18] applied Long Short-Term Memory (LSTM) to predict spare parts inventory, addressing the challenges posed by large data and short replacement cycles. Statistical models, including the frailty model and the Schoenfeld residual test, capture the variability in failure and repair data due to environmental factors [23].