Master Manufacturing & Supply Chain - end to end

It is a practitioner-led program covering manufacturing and supply chain end to end - plant operations, planning and procurement, logistics and warehousing, Industry 4.0, and analytics and AI optimization - organized into five deep tracks with hands-on labs.

It suits supply-chain, planning, procurement, logistics, and manufacturing professionals, data engineers and scientists, IIoT and automation engineers, consultants, and graduates entering the field.

No. The program builds from how manufacturing and supply chains work to advanced analytics and AI. Prerequisites per track are shared on enquiry.

Manufacturing Fundamentals & Plant Operations; Supply Chain Planning & Procurement; Logistics, Warehousing & Distribution; Digital Manufacturing & Industry 4.0; and Analytics, AI & Supply Chain Optimization.

On completion you receive a Yukti Certified Supply Chain Professional credential - a badge and transcript. Where a track maps to an external certification, aligned preparation is included.

Both. Self-paced access and mentor-led cohorts are available, along with private corporate delivery tailored to your team.

Yes. Every track can be delivered as a private corporate cohort, tailored to your systems, data, and objectives. Contact us to scope a program.

It runs from product design and sourcing through procurement, inbound logistics, manufacturing, quality, inventory, warehousing, planning, distribution, transportation, fulfilment, and reverse logistics - with analytics and optimization across all of it. The program traces this lifecycle explicitly.

A BOM lists the components and quantities needed to make a product, forming the backbone of manufacturing planning and costing.

Discrete manufacturing assembles distinct items (e.g. cars, electronics); process manufacturing produces formulated or continuous output (e.g. chemicals, food).

A Manufacturing Execution System captures and controls what happens on the shop floor, bridging plant operations and enterprise systems.

Overall Equipment Effectiveness measures manufacturing productivity as the product of availability, performance, and quality.

Statistical Process Control uses statistical methods to monitor and control process quality and detect variation.

Demand forecasting predicts future demand to drive planning, procurement, and inventory decisions, increasingly using machine learning.

Material Requirements Planning calculates the materials and timing needed to meet production plans, based on BOMs and demand.

Sales and Operations Planning aligns demand and supply plans across functions, balancing service, inventory, and cost.

Inventory optimization sets stock and safety-stock levels to balance availability against holding cost and obsolescence across the network.

A Warehouse Management System controls warehouse operations - receiving, put-away, picking, and shipping - often with automation and RFID.

A Transportation Management System plans and executes freight movement, optimizing cost, mode, and service.

A third-party logistics provider (3PL) executes logistics services; a fourth-party (4PL) orchestrates and manages the broader logistics network.

Reverse logistics manages returns, repairs, and recycling - the flow of goods back through the supply chain.

Industry 4.0 is the digitization of manufacturing through connected machines, data, and AI - IIoT, digital twins, and predictive analytics.

The Industrial Internet of Things connects machines and sensors to capture and act on operational data in real time.

OPC-UA and MQTT are protocols for industrial and IoT communication, enabling machines and systems to exchange data reliably.

A digital twin is a virtual model of a physical asset or process, used to simulate, monitor, and optimize its real-world counterpart.

Predictive maintenance uses sensor data and models to predict equipment failure before it happens, reducing downtime and cost.

A control tower provides end-to-end visibility and orchestration across the supply chain, enabling faster, data-driven response to disruption.

Demand sensing uses near-real-time signals to detect short-term demand changes faster than traditional forecasting.

Linear programming (LP), mixed-integer programming (MILP), heuristics, and tools like OR-Tools solve network, inventory, and routing problems.

SQL and data modelling, Python for analysis and optimization, familiarity with Snowflake, Databricks, Spark, and Kafka, and BI tools like Power BI.

AI powers forecasting, demand sensing, inventory and network optimization, predictive maintenance, ETA prediction, and increasingly generative planning assistants.

Control-tower KPIs measure service, inventory, cost, and disruption across the chain, orchestrated into a single operational view.

Network optimization designs the optimal footprint of plants, warehouses, and flows to minimize cost and meet service targets.

Projects span a supply-chain data lake, OEE dashboards, demand forecasting, inventory and route optimization, predictive maintenance, digital twins, a control tower, supplier and logistics analytics, and an AI planning assistant.

Roles include supply-chain and logistics analyst, demand and supply planner, procurement analyst, manufacturing and IIoT engineer, optimization analyst and data scientist, consultant, solution and enterprise architect, and Chief Supply Chain Officer.

Yes. Supply chains are being reshaped by AI, Industry 4.0, and resilience demands, sustaining strong demand for professionals who combine domain knowledge with data and optimization skills.

It depends on the track mix and delivery mode. Self-paced learners progress at their own pace; cohorts follow a structured schedule. Timelines are shared on enquiry.

Yes. The program addresses global supply chains with attention to the USA, Canada, UK, Europe, the Middle East, Singapore, India, and Australia, and to the networks and regulations specific to each.

Cold chain logistics maintains temperature-controlled conditions for perishable or sensitive goods throughout transport and storage.

Supplier performance management tracks and improves supplier reliability, quality, cost, and risk across the supply base.

Condition monitoring continuously tracks equipment health signals to detect degradation and inform maintenance.

Edge analytics processes machine data close to the source, enabling low-latency decisions without sending everything to the cloud.

The program integrates with our data engineering, cloud platform, data science, and AI governance tracks, giving you both domain depth and technical skills.

Process maps and templates for ERP/MES/WMS integration, optimization notebooks (OR-Tools), Databricks examples and Power BI dashboards, a capstone PoV and executive pack, and the Yukti Certified Supply Chain Professional credential.

A distribution network is the set of facilities and flows - plants, DCs, and routes - that move product from source to customer.

Safety stock is extra inventory held to buffer against demand and supply variability, protecting service levels.

Lead-time analysis studies the time from order to delivery across the chain, a key input to inventory and planning decisions.

Use the contact form to tell us whether you want individual enrolment or corporate delivery, and a senior practitioner will respond to scope the right next step.

It combines genuine domain depth across manufacturing, planning, logistics, and Industry 4.0 with the data, optimization, and AI skills that now run through the supply chain - organized into five practitioner-led tracks, reinforced with labs and portfolio projects.

A resilient supply chain can absorb and recover from disruption through visibility, flexibility, diversified sourcing, and buffers - a growing priority after recent global shocks.