# Green Spare Parts Evaluation for Hybrid Warehousing and On-Demand Manufacturing
## 1 Introduction
[PERSON] [24] proposed an interesting framework that bridges the areas of condition-based maintenance, outsourcing coordination, and joint decisions on reliability engineering and stockholding policies. Supply chain disruptions  
Figure 1: Traditional and green spare parts warehousing and supply. are crucial for ensuring the timely availability of spare parts and minimizing operational disruptions [25]. Although these advances represent significant progress in spare parts inventory analytics toward a more data-driven approach, challenges remain, such as data availability, accuracy, and quality. A model-based approach can effectively address situations where data are scarce or cases are novel, leveraging theoretical knowledge and domain expertise for informed predictions [26]. [PERSON] et al. [27], [PERSON] et al. [28], and [PERSON] et al. [29] applied Batch Deterministic and Stochastic Petri Nets (BDSPNs) into spare parts management to simulate inventory policies and optimize performance, incorporating batch tokens for information flow and operational policies. [PERSON] and [PERSON] [30] presented a generalized stochastic Petri net (GSPN) model to capture the complexities of a three-echelon repair structure and a two-echelon inventory system. [PERSON] et al. [31] proposed a hybrid simulation approach to investigate how the adoption of 3D printing technologies to manufacture spare parts for maintenance operations will improve operational efficiency and effectiveness [31]. On the other hand, [PERSON] [32] and [PERSON] [33] highlighted that digital warehouse and on-demand manufacturing may not always be cost-effective, especially for larger parts or when demand is highly variable. Faster processing times are expected to positively affect spare part supply, which can lead to better service levels and shorter lead times for customers [34]. Therefore, careful consideration of the technical capabilities and production constraints of on-demand manufacturing is required [35]. In addition, there is a lack of exploration of the hybrid warehouse option, in which physical and digital warehouses are considered. In the real world, choosing either a physical or digital warehouse might not ensure the highest or desired production availability, whereas combining both might lead to achieving the desired value at optimal cost. From an asset management point of view, spare parts evaluation is a strategic analysis that plans spare parts inventories and supplies for the commissioning phase and operational lifetime. Adapting the asset management approach means that the value of choosing or combining physical and digital warehouses should be explored throughout the lifecycle, together with the consequences of having or not having spare parts stocks and supply chain risks. There are several options or scenarios to explore; for example, spare parts could be supplied directly from global manufacturers in the best-cost countries (BCCs) or by having central warehouses or on-site stocks (e.g., on an oil platform). Moreover, digital warehousing and local on-demand manufacturing (additive and subtraction methods) offer either no warehouse stock options or minimal warehouse stock as a hybrid option (physical and digital warehousing). All these options have different consequences (production losses) and operating expenses (holding cost, CO\({}_{2}\) emission cost, transportation cost) because they have different supply lead times. Therefore, SPIR engineers need more insight-driven information to enable them to build a strategic spare part management plan rather than managing spare parts for short-term periods. The purpose of this paper is to upgrade the spare parts evaluation process to consider digital warehousing and to offer a strategic, insight-driven approach to SPIR engineers.