Supply Chain Managers

Select transportation routes to maximize economy by combining shipments or consolidating warehousing and distribution.

AI: Fully automatable - Advanced optimization algorithms and transportation‑management systems (as of 2025) can autonomously select cost‑efficient routes and consolidation plans across shipments and warehouses in most routine scenarios.

Analyze inventories to determine how to increase inventory turns, reduce waste, or optimize customer service.

AI: Fully automatable - AI/ML tools can analyze inventory data, identify opportunities to increase turns, reduce waste, and optimize service levels, and can generate executable recommendations that automate much of the work.

Analyze information about supplier performance or procurement program success.

AI: Fully automatable - By 2025 AI systems can ingest supplier and procurement data, perform trend and root-cause analysis, and produce actionable reports end-to-end without human-only intervention.

Monitor forecasts and quotas to identify changes and predict effects on supply chain activities.

AI: Fully automatable - AI is well capable of monitoring forecasts and quotas, detecting deviations, and forecasting downstream impacts using time‑series and causal models in an automated fashion.

Forecast material costs or develop standard cost lists.

AI: Fully automatable - With sufficient historical and market data, AI models can reliably forecast material costs and produce standard cost lists automatically.

Diagram supply chain models to help facilitate discussions with customers.

AI: Fully automatable - AI can create and iterate supply chain diagrams from inputs and templates automatically, enabling fully automated diagram generation to facilitate customer discussions.

Conduct or oversee the conduct of life cycle analyses to determine the environmental impacts of products, processes, or systems.

AI: Fully automatable - When provided with requisite inventory, process, and emissions data, AI tools can run life cycle analysis models and produce LCA reports automatically.

Investigate or review the carbon footprints and environmental performance records of current or potential storage and distribution service providers.

AI: Fully automatable - AI can analyze emissions data, apply emissions factors and standards, and generate assessments and recommendations given access to providers' records.

Determine appropriate equipment and staffing levels to load, unload, move, or store materials.

AI: Partial - AI can model workloads and recommend equipment and staffing levels using data and simulations, but on‑site judgment, safety, and labor relations require human oversight.

Manage activities related to strategic or tactical purchasing, material requirements planning, controlling inventory, warehousing, or receiving.

AI: Partial - AI can handle tactical purchasing, MRP, inventory control, and routine warehousing tasks, but strategic purchasing decisions and exception management still need human negotiation and judgment.

Define performance metrics for measurement, comparison, or evaluation of supply chain factors, such as product cost or quality.

AI: Partial - AI can propose, calculate, and benchmark candidate supply‑chain metrics, but defining which metrics matter requires alignment with business strategy and stakeholder judgment.

Implement new or improved supply chain processes to improve efficiency or performance.

AI: Partial - AI can design, simulate, and automate many process improvements and orchestrate execution, but full implementation requires change management, stakeholder buy‑in, and cross‑functional leadership that remain human responsibilities.

Develop procedures for coordination of supply chain management with other functional areas, such as sales, marketing, finance, production, or quality assurance.

AI: Partial - AI can draft and simulate coordination procedures and suggest handoffs between functions, but developing workable cross‑functional procedures requires negotiation and organizational context that humans provide.

Confer with supply chain planners to forecast demand or create supply plans that ensure availability of materials or products.

AI: Partial - AI systems can produce high‑accuracy demand forecasts and generate supply plans, but the act of conferring with planners—resolving tradeoffs and exceptions collaboratively—means the task is only partially automatable.

Negotiate prices and terms with suppliers, vendors, or freight forwarders.

AI: Partial - AI can automate routine, rules‑based negotiations and provide strategy and proposals, but complex supplier relationships and high‑stakes contract negotiations still require human negotiators.

Monitor suppliers' activities to assess performance in meeting quality or delivery requirements.

AI: Partial - AI can continuously monitor digital signals (shipments, QC metrics, EDI) and flag issues, but full assessment often requires human validation, on-site audits, or qualitative context.

Design or implement supply chains that support business strategies adapted to changing market conditions, new business opportunities, or cost reduction strategies.

AI: Partial - AI can generate optimized supply‑chain designs and scenario analyses, but strategic design and implementation aligned to evolving business strategy still require significant human judgment and cross‑functional leadership.

Meet with suppliers to discuss performance metrics, to provide performance feedback, or to discuss production forecasts or changes.

AI: Partial - AI can prepare briefing materials, suggest talking points, and even participate in routine virtual calls, but supplier relationship management and negotiation remain primarily human responsibilities.

Participate in the coordination of engineering changes, product line extensions, or new product launches to ensure orderly and timely transitions in material or production flow.

AI: Partial - AI can coordinate schedules, predict material impacts, and surface risks for engineering changes and launches, but final coordination across teams and execution still needs human project management.

Identify or qualify new suppliers in collaboration with other departments, such as procurement, engineering, or quality assurance.

AI: Partial - AI can shortlist and preliminarily qualify suppliers via data analysis, RFP parsing, and risk scoring, but full qualification typically requires cross‑departmental review and audits.

Design or implement plant warehousing strategies for production materials or finished products.

AI: Partial - AI can design warehouse layouts, inventory policies, and automation strategies, yet implementing physical layout changes and stakeholder buy‑in means humans remain essential.

Design, implement, or oversee product take back or reverse logistics programs to ensure products are recycled, reused, or responsibly disposed.

AI: Partial - AI can model reverse‑logistics networks, optimize routing and sorting, and support compliance tracking, but program implementation and regulatory/partner negotiations need human oversight.

Develop or implement procedures or systems to evaluate or select suppliers.

AI: Partial - AI can develop and deploy evaluation systems (scoring, automation, dashboards) to select suppliers, but governance, policy decisions, and final contracting still require human control.

Document physical supply chain processes, such as workflows, cycle times, position responsibilities, or system flows.

AI: Partial - AI can generate documentation from data, templates, and descriptions but accurate capture of physical workflows, cycle times, and role responsibilities still requires human observation and validation.

Evaluate and select information or other technology solutions to improve tracking and reporting of materials or products distribution, storage, or inventory.

AI: Partial - AI can analyze requirements, compare technologies, and produce vendor shortlists and recommendations, but final selection requires organizational judgment, stakeholder alignment, and hands-on testing.

Identify opportunities to reuse or recycle materials to minimize consumption of new materials, minimize waste, or to convert wastes to by-products.

AI: Partial - AI can analyze material flows and lifecycle data to identify reuse and recycling opportunities, but validating feasibility and implementing changes requires on-the-ground testing and cross-functional decisions.

Review or update supply chain practices in accordance with new or changing environmental policies, standards, regulations, or laws.

AI: Partial - AI can parse new regulations and suggest updates to practices, yet legal interpretation, formal compliance sign-off, and organizational implementation need human oversight.

Design or implement supply chains that support environmental policies.

AI: Partial - AI can design sustainable supply chain models and optimize for environmental objectives, but real-world implementation, supplier negotiation, and change management require human leadership.

Locate or select biodegradable, non-toxic, or other environmentally friendly raw materials for manufacturing processes.

AI: Partial - AI can search databases and recommend candidate environmentally friendly materials, but final selection requires material testing, supplier qualification, and process compatibility verification.

Appraise vendor manufacturing capabilities through on-site observations or other measurements.

AI: Partial - While AI can analyze remote sensor data, photos, or video to assess vendor capabilities, comprehensive appraisal via on-site observation and measurement typically requires human inspectors.