# Green Spare Parts Evaluation for Hybrid Warehousing and On-Demand Manufacturing
## 4 Conclusions
Demand rates and supply lead times should not only be used to classify the criticality of spare parts and their location; they should also be heavily used to determine safety stock and order quantity. Several scenarios under demand fluctuations and more complex supply-time distributions should be further studied. In this study, SPIR engineers requested exploration of digital warehousing in normal conditions and with limited fluctuation (only an exponential distribution of 1.5 per month). The applied limited demand fluctuation was to allow for a fair comparison between the four scenarios. Therefore, it is recommended to perform further simulation experiments with demand fluctuations, especially for the best scenario (hybrid warehouse) to test it under several demand conditions. As for supply time distributions, specific supply time distributions (such as triangular distributions) may not be flexible enough to reflect the complexity of actual operations (such as seasonal demand changes, unexpected events, etc.). Thus, it is also recommended to consider more complex stochastic models, such as bimodal distributions, the Gaussian mixture model, kernel density estimation, the finite mixture model, Petri net, or state machine models, to improve the applicability of the models in the real world. The simulation model developed covers limited safety stock dynamics related to the order point and the batch size. They are almost considered fixed variables for the entire lifetime; however, in practice, they are dynamically adjusted to keep the safety stocks at the optimal level, considering seasonal operations and conditions. It is recommended to introduce and explore the dynamic inventory management strategy to adjust inventory levels based on real-time data to better respond to fluctuations in demand. It may end up with finding the most optimal hybrid scenario that also further minimizes the safety stock. Moreover, condition-based spare part management is promising, where a more dynamic inventory management strategy is required and should be explored. In comparing the economics of physical and digital warehouses, this study focuses on spare parts, holding, transportation expenses, and subscription fees, without estimating expenses related to unavailability (production losses due to supply delays) and emissions. In the context of offshore oil production, since the selected equipment and spare parts are classified as critical, the costs associated with unavailability are significantly higher than all other expenses, especially the emission costs. Emissions are important not only because of their direct costs but also because of their broader environmental and regulatory impacts and public perception. Therefore, it is important to prioritize availability and emissions as key performance indicators for comparison, while understanding that estimating their economic figures can be complex and highly context specific. For example, if the case is related to offshore wind farm, the associated costs of unavailability and emissions will be weighed totally differently. In this study, the focus was on conducting direct comparison scenarios, which had well-defined and fixed conditions. The primary interest was in the outcomes under specific conditions rather than in exploring the range of possible outcomes due to variations in inputs. Sensitivity analysis for demand rate, reorder point, and batch size will provide further insight, especially in uncertain environments such as increasing lead times due to harsh weather, increasing order rates during maintenance campaigns, and increases in emission tax rates. Another significant recommendation for future work is to perform a comprehensive economic analysis, where more cost categories are covered, and better and recent cost estimates are considered, based on the technical KPI results obtained in this study. Furthermore, it is also recommended that the scope of the case study be extended to cover different asset classes and other industries to verify the general applicability of the findings.