Oil and Gas Inventory Dynamics: Analytics and Optimization

In a recent post, we highlighted the comparative standing of oil companies in terms of their supply chains. This underscored the significance of inventory as a key indicator of a company's efficiency. In this article, we further demonstrate various inventory management concepts that companies can adopt to gauge and enhance their inventory management.

Introduction

Inventory, accounts receivable, and cash constitute current assets for companies that necessitate ongoing vigilance. Oil companies face inventory challenges such as high safety stock levels, long lead times, inefficient floor utilization, and production planning issues. Traditional methods often result in excess inventory and higher work-in-progress (WIP). The primary goal for any oil company is to reduce product flow time, minimize WIP, enhance on-time delivery, decrease lead times, lower inventory levels, and improve inventory turns. Conversely, inconsistent practices, often stemming from incomplete or absent standard operating procedures, can contribute to inventory control problems.

Spare parts inventory and demand management is important due to the evolving technological innovations, responsiveness requirements, and the lumpy pattern of spare parts demand. In addition, Managing the spare parts inventory for drilling and production is challenging due to high costs and complexity compared to demand-dependent raw materials. Improving spare parts demand irregularities involves addressing supplier lead time variability and implementing age-based replacement in maintenance.

Linear Programming Approaches:

Lee et al. (1996) explored challenges encountered by oil companies in inventory management sourced from various channels. The authors suggested a mixed-integer programming model and a branch-and-bound approach to address the crude oil unloading issue. ?Relvas et al. (2006) concentrated on distribution center operations, employing a mixed-integer linear programming strategy to model pipeline scheduling and supply management.

Gupta et al. (2010) proposed an algorithm that incorporated uncertainty in crude oil inventory management, solving two mixed-integer linear programming models and a nonlinear model. ?Zhao et al. (2015) formulated an optimization model to integrate a refinery's production system with its utility system to minimize inventory and maximize total profit. The integrated model was then separated into a mixed-integer linear programming model and a nonlinear programming model, with a sequential method devised to solve the two models.

Analytics Case studies:

Leaven et al. (2017) investigated both Kanban and CONWIP methodologies in inventory, both recognized as "pull" systems. While both approaches fall under the pull category, their study revealed that implementing the CONWIP (CONstant Work In Progress) approach effectively reduced costs associated with raw material and finished goods inventory for a large oil company. The application of CONWIP led to a $3.1 million reduction in finished goods inventory and a $1.4 million reduction in raw material inventory.

Joseph et al. (2019) employed ANOVA tests and Spearman's Rank correlation coefficient to analyze the relationship between inventory and drilling operations based on a survey conducted in 26 drilling companies. Their study demonstrated a significant correlation (correlation value of 0.682, p-value = 0.001) between ineffective inventory management and downtime in oil and gas drilling operations. Additionally, they highlighted a significant correlation (correlation coefficient value of 0.788, p-value = 0.000) between continuous downtime in drilling operations and the income level of oil and gas drilling firms due to poorly managed inventory control.

Ali et al. (2020) demonstrated that adopting a stock-out cost approach led to an 8.88% increase in average service level and a 56.9% decrease in the company's average inventory investment. Similarly, the backorder cost approach resulted in a 7.77% increase in average service level and a 57% decrease in average inventory investment compared to the company's existing inventory management system.

References:

1. Ali, U., Salah, B., Naeem, K., Khan, A.S., Khan, R., Pruncu, C.I., Abas, M. and Khan, S., 2020. Improved MRO inventory management system in oil and gas company: Increased service level and reduced average inventory investment. Sustainability, 12(19), p.8027.
2. Gupta, S. and Zhang, N., 2010. Flexible scheduling of crude oil inventory management. Industrial & engineering chemistry research, 49(3), pp.1325-1332.
3. Leaven, L., Wang, S., Coley, L. and Udoka, S., 2017. Achieving Optimal Safety Inventory Levels for Oil Companies using the CONWIP Approach. International Journal of Supply Chain Management, 6(4), pp.17-21.
4. Lee, H., Pinto, J.M., Grossmann, I.E. and Park, S., 1996. Mixed-integer linear programming model for refinery short-term scheduling of crude oil unloading with inventory management. Industrial & Engineering Chemistry Research, 35(5), pp.1630-1641.
5. Omodero, C.O., 2019. Inventory control management and revenue generating capabilities of oil and gas drilling firms in nigeria. Annals of Spiru Haret University. Economic Series, 19(4), pp.75-94.
6. Relvas, S., Matos, H.A., Barbosa-Póvoa, A.P.F., Fialho, J. and Pinheiro, A.S., 2006. Pipeline scheduling and inventory management of a multiproduct distribution oil system. Industrial & Engineering Chemistry Research, 45(23), pp.7841-7855.
7. Zhao, H., Rong, G. and Feng, Y., 2015. Effective solution approach for integrated optimization models of refinery production and utility system. Industrial & Engineering Chemistry Research, 54(37), pp.9238-9250.
8. Dauzère-Pérès, S., Gershwin, S.B. and Sevaux, M., 2000. Models and solving procedures for continuous-time production planning. Iie Transactions, 32(2), pp.93-103.