AI can digitalize the oil and gas supply chain, lowering costs and emissions

Supply chains are the key to fighting against climate change. Why? Because they generate around 60% of all carbon emissions globally. For companies in most customer-facing sectors, end-to-end emissions are much higher than the direct emissions in their own operations (so-called Scope 1 and 2 emissions)

Digitalizing the supply chain can increase the visibility of emissions, allowing for the application of ESG metrics as well as optimizing the routes to lower emissions. Digital technologies, if brought to scale, could reduce carbon emissions by up to 20% by 2050 in manufacturing across the three highest-emitting sectors (energy, materials, and mobility), according to the World Economic Forum.

This transformation is cost-effective as well. Even with zero supply chain emissions, end-consumer costs would go up by 1–4% at the most in the medium term.

What is a supply chain?

Supply chain is a fancy word, often thrown around when talking about large corporations. I first encountered it during the pandemic, through headlines of ‘The modern supply chain is disrupted’. One of the most severe economic problems caused by the COVID-19 pandemic was damage to the supply chain. Its effects touched nearly every sector of the economy. Supplies of products of all kinds were delayed due to ever-changing restrictions at national borders and long backups in ports. At the same time, demand for products changed abruptly.

The supply chain is a dynamic and complex network of companies and people, that underlies business in the modern world. It facilitates the creation and distribution of goods to end consumers. It encompasses all the steps from getting raw materials to delivering the products to consumers. Here is the path a product takes when it is approved for manufacturing:

1. Raw materials are sourced by Suppliers. The raw materials, components, and services are allotted according to the projected customer demand. After identifying needs, organizations choose suppliers based on factors such as price, quality, reliability, and location. Contracts and agreements are often negotiated during this phase.
2. These raw materials are sent to Manufacturers to turn them into products. This step may involve various production processes, such as manufacturing, assembly, or construction. Manufacturing includes:
   a. Production planning to determine the quantity or schedule and manage resources like labor, machinery, and materials.
   b. Quality control including inspections, testing, and quality assurance, to make sure the raw material is up to standard.
   c. The actual Manufacturing itself through production and assembly lines
   d. Packaging to ensure protection, labeling, and presentation to end consumers.
3. Distributors and Wholesalers purchase produced goods in bulk to distribute them to retailers. It includes warehousing, order processing, inventory management, packing, and transportation.
4. Retailers receive products and display them in physical stores or online marketplaces. They may also engage in marketing and promotional activities to attract Customers, who can place orders either in-store or online.
5. The last step is returns and customer support! The disposed items also need to be recycled and processed.

In 2019, around 84% of global primary energy came from coal, oil and gas. Oil and gas are drilled all around the world and need to be shipped to consumers across major industries, forming a complex supply chain. Take, for example, Shell: a global group of energy and petrochemical companies that has offices in more than 70 countries.

The O&G Supply chain faces many risks from uncertainties. Mainly these challenges are:

1. Volatile costs and inflation
2. Labor and material supply uncertainty

According to McKinsey, minimizing these supply-chain risks could help oil and gas firms better secure their labor and materials while cutting costs by up to 15 percent. The labor and material supply uncertainty poses a huge risk as strikes for pay hikes and absences over working conditions have increased. This increases the amount of emergency work done which is more expensive. A high staff-absence rate of 6 to 8 percent has been observed and anecdotal evidence shows an estimated 5 to 10 percent no-show rate for flights to offshore sites. Lead times for both long-lead (12 to 18 months) and short-lead (two to six weeks) equipment have stretched, significantly impacting project-delivery schedules. Vessel and spare parts inventories are continually dropping, causing availability challenges.

One of the biggest challenges in the energy transition is overcoming the bottlenecks that are arising in the EV supply chain.

Thus, organizations need to safeguard their supply chains against material supply as well as inflation and market volatility risks to save 15% on costs. Conducting a risk assessment on availability, inflation, and supplier risk categories can help make these supply chains resilient. A holistic risk profile can be created by applying the above steps to different moving parts — consider commercial, operational, and execution risks to construct a complete picture. To compute risk, we need to have a strong data infrastructure and machine intelligence to find patterns and trends.

And this is where AI comes into play.

Enter AI, to build an agile and flexible supply chain

Most organizations currently use technology and software solutions, including Enterprise Resource Planning (ERP) systems, Supply Chain Management (SCM) software, and various data analytics tools to support decision-making, inventory control, demand forecasting, and process optimization.

The pandemic highlighted the need for visibility across the supply chain, which has prompted a digitalization revolution ever since. Market volatility, which has been exacerbated by the COVID-19 pandemic, has elevated the need for agility and flexibility. Increased attention to the environmental impact of supply chains is triggering regionalization and the optimization of flows. Sensors are increasingly being used to generate more data about lead times, inventory management, and supplier schedules. The vast amount of IoT data can be used by AI models to find patterns and propose decisions.

In a McKinsey Analysis, successfully implementing AI-enabled supply-chain management has enabled early adopters to improve logistics costs by 15 percent, inventory levels by 35 percent, and service levels by 65 percent, compared with slower-moving competitors.

Here are the ways AI can be implemented in the supply chain:

Marketing and Sales

1. Demand Forecasting by analyzing historical data and external factors to predict customer demand accurately. This helps in optimizing inventory levels, production schedules, and procurement strategies.
2. Customer Segmentation can segment customers based on various factors such as buying behavior, demographics, and preferences.
3. Dynamic Pricing: AI can predict sales trends, allowing for better inventory management and reducing the risk of overstock or stockouts. This can allow us to adjust product prices in real time based on factors like demand, competitor pricing, and inventory levels, maximizing profitability while remaining competitive.
4. To conduct a thorough risk assessment, NLP can be used to spot market inflation trends in news articles and government regulations. Contracts can be reviewed to determine the level of exposure to market inflation. Reviewing contractual mechanisms can mitigate the impact of inflation (such as risk-reward ratio, managed service options, and supplier consolidation), and can be compared against a view of market inflation across key industry indices, each tailored to different categories.

Planning and Network Optimization

Once data has been procured from suppliers, manufacturers, and distributors, it can aid in developing a comprehensive platform for planning.

1. Supply Chain Network Optimization: AI can model and simulate various supply chain configurations to determine the most cost-effective and efficient network design, including the location of warehouses, production facilities, and transportation routes.
2. Production Planning: AI can create production schedules by balancing production capacity, raw material availability, capacity planning, and demand forecasts. It can also adjust schedules in real time to accommodate changes.
3. Supplier Management: AI can evaluate supplier performance, predict supplier delays, and assess the risk associated with different suppliers, helping organizations make data-driven decisions regarding their supplier relationships and encouraging transparency. These risks can be evaluated on the metrics valued by the organization, making it personalized.
4. Risk Management: AI can assess risks associated with supply chain disruptions, such as natural disasters or geopolitical events, and provide recommendations to mitigate potential impacts.

Logistics and Distribution:

1. Route Optimization: AI can analyze real-time traffic data, weather conditions, and historical traffic patterns to optimize delivery routes. This reduces delivery times, fuel consumption, and transportation costs.
2. Predictive Maintenance: AI can predict when equipment, such as vehicles or machinery, is likely to fail, allowing for preventive maintenance to minimize downtime and unexpected repairs.
3. Delivery Scheduling: AI can create efficient schedules for deliveries and pick-ups, taking into account factors like time windows, driver availability, and traffic.

Digitalization is the next revolution in the Oil and Gas Industry

To remain competitive in a volatile market, oil and gas companies are striving to transform their operations, improving the reliability and availability of their assets while reducing costs and carbon emissions. Many CEOs have vouched for making digitalization a key priority. For example, Shell has launched their program for digitalization in energy where they are experimenting with implementing big data, IoT, and AI among other technologies to build systems for remote monitoring, predictive maintenance, etc.

However, the oil and gas industry is typically a slow-moving business in adopting change. McKinsey research shows that, while almost every company has been running digitization projects across various parts of its operations, 70 percent of them have not moved beyond the pilot phase. Most of the time, the biggest barrier is not the technology itself but the cultural or organizational barriers.

The technology and use cases don’t always generate clear value if they are not solving the fundamental problems the bottom line faces. The project teams might not be effectively able to communicate the impact on the bottom line. This results in management not allocating enough funds for widespread deployment.

Challenges

1. Many players are involved, with varying data maturity: The complexity of the supply chain which adds to its efficiency and breadth is also the biggest challenge to managing it. Nowadays, many supply chains are global. The different suppliers manufacturers and distributors involved are new data sources with varying levels of data maturity. As the O&G industry was built on paper records and manuals, digitalizing the data is a huge hurdle in itself. The data quality is often not good and lots of it is missing, requiring human checks which are manual and time-consuming. This is a major hurdle that I observed when talking to executives of Microsoft, IBM, Kongsberg, Accenture, AWS, and C3.ai who are working to digitalize the Oil and gas and the energy industry, claiming it is one of the top concerns.
2. To combine the dispersed data, various systems have to be interconnected: “This is very hard as it requires collaborations, funds, processes, and ROI on what is invested. If we get it wrong, companies risk losing competitive advantage in the long run and potential brand erosion.”, says Francesco Melandri, a Managing Consultant at IBM.
3. Technology adoption across various levels is tricky: ‘Change Management is one of the trickiest challenges of digitalizing the Oil and Gas Industry’, says Karina Fernandez, General Manager of Emerging Digital Technologies at Shell, who is leading a team to implement remote monitoring systems in their assets in Qatar. New technologies require staff to develop new skills, adopt new processes, and change long-standing working practices. That doesn’t happen unless people receive the right incentives and support.
   The User Experience of the product is often forgotten but is crucial to designing the product according to the people who will use it i.e. technicians and operators. “Culture also has to be included in the perspective as the supply chains are stretched and what might work in the UK might not work in India or elsewhere,” says Francesco.

Next Steps

1. Focus on the value creation before the tech: Oil and gas CEOs should be clear about the business problems they wish to address and the results they want to achieve with their digitization programs.
   For example,

• Supply chain digitization, including a digital supply chain control tower and tactics such as dynamic scheduling and blending, linear programming and crude sourcing optimization, and trading excellence, could net 50 cents per BOE (Barrel of Oil Equivalent).
• Reliability excellence, such as through predictive-to-prescriptive (P2P) maintenance and digital workflow management, can reduce unplanned downtime by up to 30 percent.
• B2B commercial excellence via advanced analytics in pricing and margin management could achieve combined sales and margin increases of 1 to 2 percent.
• Digitally enabled B2C operations, including improved network planning and optimization, together with non-fuel-retail (NFR) excellence in space allocation, assortment, pricing, and promotions, could raise EBITDA by 5 or even 10 percent.

2. Set the right culture among employees: This starts by getting their managers and supervisors on board through clear communication behind the ‘Why’ of the transformation. Employees can be assisted with resources to help adopt the tools into their workflows. This can be achieved through strategic partnerships that can assist in the transition.

3. Build a strong data infrastructure: This can support the decision-making required to find trends and build visualizations. The technology infrastructure at most oil and gas companies is complex and fragmented. Organizations use a mix of modern and legacy IT and OT systems, and their business-critical data are distributed across those systems. Those data can be hard to access, difficult to interpret, and of inconsistent quality. Those are shaky foundations for a digital transformation, which is why successful organizations support their frontline digital initiatives with a comprehensive overhaul of their underlying infrastructure.

I hope you gained value from this article. Do clap, share, or comment under this article, and consider following me on Medium!

See you next time!

Until then, keep reading, keep learning!

Sources

How oil and gas companies can secure supply-chain resilience

Succeeding in the AI supply-chain revolution

Harnessing volatility: Technology transformation in oil and gas