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

MCKINSEY

Building value-chain resilience with AI

Building value-chain resilience with AI

mckinsey.com

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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 inc...

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.

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 reski...

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 sche...

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...

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...

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 ca...

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 ...

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 con...

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 capabil...

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 landscap...

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 o...

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