Reinventing the Energy Value Chain

Supply Chain Roadmaps for Digital Oilfields through Hydrogen Fuel Cells

David Jacoby, Alok R. Gupta

• Publisher: PennWell Books
• Year: 2021
• ISBN:
  9781955578011
• Topic: Business
• Subtopic: Industria energetica
• Index: Business

As the push for diversification of energy sources continues, this book provides a toolbox of techniques to enhance top-line as well as bottom-line results by successfully managing capital projects and operations & maintenance trade-offs across the value chain. Built on the foundations laid in Jacoby's previous books Optimal Supply Chain Management in Oil, Gas, and Power Generation and Guide to Supply Chain Management, it offers groundbreaking new ways to tap the power of supply chain management in conventional and emerging energy industries - from the small to the large project, and from solar to nuclear and everything in between.The organization of the book makes it a handy reference resource. It starts with a conceptual framework for value chain and supply chain management in the energy sector, laying out objectives, key business processes, and performance metrics that provide useful guideposts. It offers principles that should guide investments in the energy industry and explains how to organize the supply chain to maximize their results. Chapters on capital project and operations management explain tools and techniques that are relevant to energy value chains broadly speaking. Technology-specific chapters show how these concepts apply to ten energy domains: Hydrogen & Fuel Cells, Energy Storage, Wind, Solar, Biomass, Oil & Gas, Geothermal, Gas and Coal-Fired Power, Hydropower, Nuclear

Appendix 1

Bullwhip in the Oil and Gas Supply Chain: The Cost of Volatility

Filling an Important Research Gap

Since Jay Forrester described the bullwhip effect in 1958, a vast body of literature has examined how to avoid bullwhip in an industry-agnostic way. This literature has explored, among other topics, the effect of gaming and adjusting order placement mechanisms. For example, Kimbrough, Wu, and Zhong explored how artificial agents can dampen the bullwhip effect (Kimbrough, et al., 2002), and Ouyang tested four different ordering methods (order up-to, kanban, generalized kanban, and order-based (Ouyang, et al., 2006).

Many authors have explored evidence of this effect in consumer products and electronics. A consulting study assessed the potential savings from implementation of efficient consumer response (ECR) in that industry at $30 billion, or 12.5–25% of cost (Poirier, et al., 1996). Procter and Gamble discovered wide swings in the production and inventory of diapers despite relatively smooth demand (Lee et al., 2006). Italian pasta maker Barilla’s volume discounts and promotional pricing induced volatility in a system with inherently low end-user demand (Lee, Whang, 2006). HP measured the bullwhip effect by measuring the standard deviation of orders at the stores to the standard deviation of production at the upstream suppliers (Lee et al., 2006). Network systems manufacturer Cisco studied the impact of the right to cancel orders on the magnitude of the bullwhip effect (Lee, Whang, 2006).

Few published works have applied system dynamics to the oil and gas industry, and none has quantified the cost of volatility on the upstream oil and gas supply chain. The existing literature in oil and gas has focused on predicting the price of oil rather than analyzing the effect of volatility on the cost of the oil and gas supply chain. For example, Mashayekhi built a model of oil price drivers, showing that as demand increases, oil price rises, which causes producers to expand capacity, forcing prices down and depressing demand, in a feedback loop (Mashayekhi, 2001). John Sterman came the closest to studying the bullwhip effect in the oil and gas industry (exploration and production, or “E&P”) when he noted that “oil and gas drilling activity fluctuates about three times as much as production.” (Sterman, 2006).

This study shows evidence of the bullwhip effect in the upstream oil and gas supply chain and demonstrates that over time the cost of gasoline is 10% higher as oil producers, oil refiners, heavy equipment suppliers, and their component suppliers pass on the costs of inventory overages and shortages, poorly timed capacity investments, and inflationary prices.

Evidence of the Bullwhip Effect

The pattern of drilling activity and capital investment on the other hand is characterized by oscillation and amplitude magnification, and it appears that oil price shocks are the root cause. From 1949 until 1973, the average annual price of oil fluctuated within a 7% band, but from 1981 through 2008 the variation leapt to almost 10 times that amount. The 1973 and 1979 oil crises and the sharp escalation and crash of oil prices between 1998 and 2009 introduced a new and seemingly systemic unpredictability to oil prices.

Since the recent period of oil price volatility that began in 1998, oil drilling investment and activity has tracked the price of oil and has in some cases exaggerated the pattern set by the oil price.I The price of oil rose and fell by 52%II from its peak of $97 per barrel in 1980 to a low of $17 in 1998 and rose again to reach a new high of $97 in 2008. As the price of oil rose and fell between 1998 and 2008, capital expenditure for major oil companies rose and fell by 25–63%III and the total US rig count (oil and gas) rose and fell by 36%.

Regardless of the reason behind the initial shocks—some think the pattern is cyclical, and there is evidence that it could be chaoticIVV—the variation from a steady state historical demand clearly induced oscillating and increasing reverberations in production, capacity, and inventory from 1995 to 2009 in markets for oil & gas field machinery and equipment, including turbines & turbines generator sets, motors, electrical equipment, and iron castings.

Bullwhip effect is evident in six oil and gas supply markets between 1995 and 2009, as indicated by the measurements in Figure 83 below, where:

• Amplification Ratio = Variance [production] / Variance [demand]
• Amplification Difference = Variance [production] - Variance [demand]

and demand is measured by orders received and production (or supplier’s production capacity) by units manufactured. Bullwhip effect exists if the Amplification Ratio is greater than 1 and the Amplification Difference is positive.

Figure 83. Evidence of Bullwhip Effect in the Oil & Gas Equipment Industry

(Source: Boston Strategies International)

The swings in capital investment by oil companies caused even bigger swings in the equipment supply chain, causing oscillation in production, inventory, and backlog. While production of turbines and engines declined by 7% between 1998 and 2008, inventories rose by 24%, as shown in Figure 84. In an analogous period when new orders spiked three times in 12 years, the backlog of turbine generators tripled and then plummeted to nearly zero twice.VI

This bullwhip effect causes four types of economic inefficiency at oil companies and their heavy equipment suppliers:

1. 1. Oil companies pay higher prices that are set when markets are overheated and never rolled back when recession hits.
2. 2. Equipment manufact...