David BECK analyzes technological issues from a political, economic and societal perspective.

Building value-chain resilience with AI

David BECK Academic - Geo economics & Tech

Across industries, value chains are facing increasing uncertainty from climatic anomalies, geopolitical tensions, and the COVID-19 pandemic, among other factors. Industries as diverse as agriculture, oil and gas, and mining face essentially the same problem: they need the ability to both run with increased efficiency and recover quickly from unforeseen or unexpected challenges. These two goals often conflict.

If companies simply increase production levels, they’ll inevitably run into bottlenecks — and if failures occur that worsen those bottlenecks, the entire network can slow down and become less resilient. Resolving this conflict presents several challenges.

Organizations often make local decisions in silos without considering downstream implications, which requires downstream operators to manually request adjustments upstream as they discover challenges. Although digital technologies can improve resilience by assisting decision making, achieving transparency across the value chain requires the alignment of many stakeholders and diverse expectations about what the technologies should deliver.

Increasingly sophisticated artificial intelligence (AI) technologies — such as advanced analytics-based forecasting, digital-twin supply-chain simulations, and supply-chain optimization tools — can help companies improve the resilience-efficiency balance of their value chains. Three key factors of success in implementing and adopting digital supply-chain technologies could be:

  • value-driven solution design
  • enhanced business process and capability building
  • and seamless technological implementation.

Opportunities and challenges in uncertainty

In any industry, value-chain conditions change every day. Coping with this dynamic in an intrinsically uncertain environment can be difficult. The probabilistic nature of value chains must be accounted for in any strategic and operational initiative.
Further complicating matters, processes must be continually updated and people reskilled. AI technologies can effectively deal with complexity, helping organizations keep up with changes, mitigate risk, and create value.

AI technologies can effectively deal with complexity, helping organizations keep up with changes, mitigate risk, and create value.

Value-chain resilience is the ability to recover quickly and gracefully from challenges. It can be achieved by improving end-to-end processes, deploying asset capacities across the value chain (such as adding warehouses and trucks as necessary), and deploying planning capabilities that incorporate these uncertainties (a single source of truth and AI-powered optimization models).

Often, the challenge is to align players across the value chain on end-to-end trade-offs in a fact-based way. On this point, value-chain owners looking to increase efficiency and resilience must tackle the following tasks:

  • identifying constraints in the value chain and how they shift over time
  • determining the optimal value-chain setup under variable conditions
  • selecting the best way to schedule capacity across the end-to-end value chain
  • adopting the best value-maximizing production strategy and setting up the value chain to support this strategy
  • identifying which value chains are exposed to risk from factors such as the COVID-19 crisis or difficult geographies

Integrated planning: Simulation, optimization, and real-time monitoring

The next generation of end-to-end planning processes will be fundamentally different from today’s. AI can help balance efficiency and resilience by incorporating uncertainty into realistic simulations of reality to help organizations evaluate scenarios and identify risks. AI can also find optimal plans for different time horizons. In doing so, it can recommend operational decisions that balance cost with revenue and provide real-time end-to-end visibility to better anticipate and react to supply-chain disruption.

In agriculture, for example, an AI-based forecasting process can evaluate cost, revenue, service, and inventory implications at the outset of the crop-growth process. From there, a digital twin (a virtual representation of the value chain) can help identify risks and increase crop protection. Global suppliers can use AI-planning optimization to extract active ingredients from crops and deliver these ingredients to formulation facilities, where they are combined to create products.

Afterward, an optimized supply-chain network can distribute these products to distributors and retailers in the most efficient way. Finally, each step can be monitored in real time with control towers to respond to any unforeseen disruptions or constraints — such as extreme weather events or supply limitations — and ensure smooth product deliveries.

Simulations of reality

Simulations are descriptive analytics tools. In a simulation, a digital twin is constructed with uncertainty measures and operational inputs. Next, a number of simulations of the future are performed using this digital twin and a summary is presented to the user for analysis.

In this sense, simulations can help identify and analyze root causes of bottlenecks. This is particularly important when responding to choke points in the system that vary over time, including changing operating conditions, maintenance downtime, weather conditions, or third-party dependencies.

Optimization for execution

Optimization models are prescriptive analytics tools. The main output is an optimal execution plan for the current environment, in which decisions are made accounting for the end-to-end impact.

For example, a mining company faced operational challenges across the mine-to-market value chain. Its siloed operating model lacked an end-to-end optimization engine, which would have proposed an optimal production plan, vessel schedule, product blend, and train schedule, as well as stockpile decisions. The company deployed an optimization AI that enabled operators to analyze and implement granular, actionable suggestions across the entire value chain and subsequently improve throughput.

Real-time supply-chain monitoring

Most companies have a control tower serving as an operating nerve center. However, fully digital end-to-end control towers can increase resilience by expanding the scope of real-time monitoring to anticipate and respond to upcoming or potential supply-chain disruption. In this way, AI allows for enhanced end-to-end planning, giving historically isolated functions the benefit of full transparency.
AI can also improve communication between partners across the value chain, enabling improved decision making and earlier detection and response to disruptions. Such transparency highlights the value of flexible demand: the more flexibility partners and internal stakeholders agree to, the more options the value chain has in reacting to challenges. Finally, the operating model can then be tailored to accommodate any new operational insights.

Enhancing value-chain resilience with a threefold approach

Enhancing end-to-end decision making with AI requires companies to think in terms of the concrete answers they want to obtain. For example, do you want a step-by-step action plan to help run your value chain? Is the goal to produce a go-no-go decision for investment based on normal conditions? Or are you primarily looking to ease communication and centralize decision making?

Determining the answers to these questions — and aligning them with stakeholders and operators — creates a shared strategy for the development phase. On this point, deploying value-chain AI requires a threefold approach encompassing:

  • Define which challenges each digital solution should address, who should be the primary user, and which process should benefit from the enabled technology.
  • Redesign or enhance the business processes expected to benefit from new AI-enabled solutions. Improve data governance to facilitate high-quality data for enabling these AI solutions. Bring in expected end users to codesign AI solutions while upskilling them with the required technological capabilities.
  • Begin by making the relevant data available. Ensure close collaboration among data scientists, operators, and stakeholders while building AI-enabled solutions.

Deploying the right AI capabilities to look deeply into an organization’s value chain can enable teams to quickly pivot while balancing efficiency and resilience. Armed with the right processes, organizations can create and adjust plans for the entire organization while keeping everyone aligned on risks and rewards.
Operationally, AI capabilities can enable fact-based discussions across the entire value chain, which not only affects business processes but also results in better alignment across different business units.

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