Across industries, value chains are facing increasing uncertainty from climatic anomalies, market volatility, 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.  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.

Value Chains In 2021

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.

Enter AI

Coping with the highly unpredictable 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.

Opportunities And Challenges In Uncertainty

Value-chain owners looking to increase efficiency and resilience must tackle the following tasks: <ul><li>Identifying constraints in the value chain and how they shift over time.</li><li>Determining the optimal value-chain setup under variable conditions.</li><li>Selecting the best way to schedule capacity across the end-to-end value chain.</li><li>Adopting the best value-maximizing production strategy and setting up the value chain to support this strategy.</li><li>Identifying which value chains are exposed to risk from factors such as the COVID-19 crisis or difficult geographies.</li></ul>

The Main Challenges

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.

Integrated Planning: Simulation, Optimization, And Real-Time Monitoring

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. The end result—a set of key performance indicators (KPIs) describing current challenges—helps companies build resilience in the current environment.

Simulations Of Reality

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. Given operational considerations, an optimization model finds the optimal plan. These considerations capture the current operational conditions, a detailed view of how the supply network functions, and can include uncertainties about the future.

Optimization For Execution

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.

Real-Time Supply-Chain Monitoring

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?

Enhancing Value-Chain Resilience With A Threefold Approach

Define which challenges each digital solution should address, who should be the primary user, and which process should benefit from the enabled technology.

Creating Value With AI: Value-Driven Solution Design

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.

Creating Value With AI: Enhanced Business Process And Capability Building

Begin by making the relevant data available. Ensure close collaboration among data scientists, operators, and stakeholders while building AI-enabled solutions. Develop user interfaces and user experiences (UI and UX) to support processes and integrate solutions into the organizational IT landscape.

Creating Value With AI: Seamless Technological Implementation

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.

AI Capabilities Put To Use

Value chains are facing increased uncertainty. A threefold approach underpinned by artificial intelligence can help companies adapt to rapidly changing markets and operational challenges.

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