Supply chain leaders today are bombarded with noise—constant chatter about AI, rising complexity, uncertainty, disruptions, trade policies, and geopolitics. Simultaneously, the landscape of supply chain planning is shifting due to both rapid advancements in technology and workforce changes. This evolving reality often makes supply chain management feel like an impossible balancing act, where leaders must play both firefighter and superhero at the same time.
Where should you focus your attention to drive the most meaningful impact? How do you strike a balance between your most pressing priorities today while delivering on the promise of digital transformation for tomorrow–all while doing more with less? This is why targeted automation is so important.
Automation in supply chain planning is not new, but traditional automation efforts have often been narrowly focused on cost reduction and efficiency. While these goals remain important, supply chain leaders must now think bigger, broader, and bolder about automation to be able to find and drive business value.
With a targeted and value-centric approach to automation, you can move beyond simply cutting costs to now create new opportunities for business growth, build resilience, and create competitive differentiation.
Instead of treating automation as a checkbox item, organizations must embed it into their core strategy, leveraging it across various dimensions of planning. This includes augmenting human decision-making, enhancing data-driven insights, and ensuring that automation delivers measurable improvements to key business objectives.
Make Automation a Strategic Priority
Supply chain planning is often hindered by manual processes and siloed operations, making it difficult to orchestrate decisions and effectively scale. Intelligent automation, powered by AI and machine learning, transforms these processes enabling companies to:
- Level Up Planners: By automating repetitive and labor-intensive tasks, businesses can reallocate their workforce to higher-value activities.
- Quickly Analyze Large Data Sets: AI can process and compare vast amounts of information in real time, identifying patterns and optimizing decision-making in ways humans simply cannot.
- Reduce Bias in Planning Decisions: Algorithms provide objective insights based on data rather than gut instinct, leading to better accuracy and consistency.
- Boost Productivity with Decision Automation: Explainable and prescriptive recommendations help teams act with confidence while reducing manual intervention.
Augment and Automate with the User in Mind
Companies must accelerate their automation efforts by identifying where AI can drive stakeholder value while ensuring ease of use and accessibility.
Consider the example of a large consumer goods manufacturer and distributor managing more than 80,000 locations. This company leverages AI and automation across multiple layers of its supply chain, including forecasting, replenishment, and transportation logistics. By integrating data from point-of-sale (POS) systems, IoT sensors, traffic, and weather forecasts, the company can:
- Automate Demand Sensing: AI dynamically adjusts order recommendations in response to demand fluctuations.
- Minimize Manual Forecasting Adjustments: For this manufacturer, planners don’t touch the forecast, focusing instead on strategic oversight.
- Optimize Transportation Routes: AI-powered analytics suggest alternative delivery routes to minimize delays and reduce costs.
- Enhance Decision-Making: By combining machine learning insights with prescriptive recommendations, the company reduces waste and improves supply chain efficiency.
In this use case example, the manufacturer harnesses targeted automation at the forecasting, replenishment and transportation layer. This demonstrates how targeted automation is helping to go beyond optimizing individual processes to transform the entire supply chain network, creating a seamless, intelligent ecosystem that continually adapts to real-world changes.
Demystifying AI for Better Adoption and Decision-Making
Despite the clear benefits of automation, many organizations struggle with adoption. To ensure success, supply chain leaders must prioritize transparency and usability. The explainability of AI predictions is crucial to building trust in automated recommendations. AI in supply chain should not operate as a black box; instead, it must offer clear, interpretable insights that allow planners to understand the rationale behind each decision. Increased transparency and explainability enables trusts which leads to higher adoption rates across organizations.
Performance tracking is another critical element in fostering adoption. Organizations should continuously monitor key metrics such as automation adoption rates, decision quality, and user engagement. These value-add metrics help teams assess the impact of automation while providing opportunities for continuous improvement, ensuring that the technology evolves in alignment with business needs.
By taking these steps, companies can foster greater trust in AI and advanced automation to ensure that supply chain teams fully leverage the potential of these technologies.
As the supply chain landscape continues to rapidly evolve, companies must rethink their approach to automation focusing on where it can bring value. It is no longer just about incremental improvements—automation must be embedded into the fabric of supply chain operations to drive end-to-end orchestration and long-term business resilience.
The call to action is clear: Think broader, bigger, and bolder about automation. In an era where doing more with less is critical, targeted automation is the key to unlocking sustained value and maintaining a competitive edge.
About the Author:
Matt Hoffman is the Vice President of Product and Industry Solutions at John Galt Solutions. Matt specializes in delivering transformational from analysis through execution across a diverse range of clients in manufacturing, distribution, and retail. Matt is committed to ensuring that processes drive solution adoption, resulting in measurable outcomes. Throughout his career, Matt has successfully led software implementations utilizing best-in-class supply chain planning systems, execution systems, and merchandising planning systems.
