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In What Order Should You Automate Your Supply Chain? A Practical Strategy Framework

By Simon Kish, AVP, Consulting Services 9 min read
Warehouse floor with stacked inventory boxes and a single highlighted package at center

Quick answer. Winning supply chain automation starts with automating the right things, in the right order. That means defining the objective and naming one accountable owner first, building the data and warehouse management foundation second, and adding physical automation third. Most programs get that sequence backwards, and that is where they stall.

At a Glance

The investment

  • Industrial companies surveyed by McKinsey expect automation to account for roughly a quarter of capital spending over the following five years, with logistics and fulfillment at 30% or more, the highest share of any segment. (McKinsey, 2022 Global Industrial Robotics Survey)
  • 76% of supply chain and logistics leaders surveyed report notable workforce shortages in their operations. (Descartes, 2024)

The potential

  • AI-enabled distribution operations can see inventory reductions of 20 to 30%, logistics cost reductions of 5 to 20%, and procurement savings of 5 to 15%. (McKinsey, 2024)
  • Gartner projects that by 2030, half of new warehouses in developed markets will be designed as human-optional facilities. Interact Analysis projects that only 13% of warehouses will have deployed even one fulfillment AMR by that same year. (Gartner, 2026; Interact Analysis, 2025)

A winning automation strategy is about sequencing the right investments, in the right order, against a clear objective. Here's what that actually looks like.

Why Automation Is Now a Capital Allocation Conversation

Supply chain leaders have been fighting the same three battles for years: rising operating costs, tight labor markets, and customers who expect faster, more flexible fulfillment. None of those pressures are going away.

What's changed is the scale of investment leaders are willing to put behind automation. McKinsey's 2022 Global Industrial Robotics Survey found that industrial companies expect automated systems to account for roughly a quarter of capital spending over the following five years. For logistics and fulfillment, the share is 30% or more, the highest of any segment surveyed. That's not a pilot-program budget. That's a structural shift in where capital goes.

Labor is the clearest driver. Descartes' 2024 research found workforce shortages most acute in transportation operations (61%) and warehouse operations (56%). Automation isn't replacing a workforce that exists. In many operations, it's filling a gap that hiring alone can't close.

For a broader look at how leading retailers are approaching this, see our earlier piece on agentic AI readiness, which covers the data and operating-model foundations that make automation investments deliver.

What Winning Actually Means

Winning in supply chain automation does not mean automating everything. It means automating the right things, in the right order, with the right integration. A warehouse that automates picking but still relies on manual inventory updates has not won. It has simply made one step faster while leaving the upstream bottleneck unchanged. A supply chain that automates transportation but cannot connect that data to inventory and fulfillment planning has created a faster silo.

The benchmark is not how many processes are automated. It is whether the automation improves end-to-end flow, reduces cost per unit handled, and reduces the error rate across the network.

Everest Technologies defines winning across three dimensions: accuracy of inventory and order data, speed of fulfillment from customer order to delivery, and adaptability of the network to demand shifts. Automation that improves only one of these three without affecting the others is incomplete.

Three dimensions of a winning automation strategy Diagram of the three dimensions of supply chain automation success: accuracy of inventory and order data, speed of fulfillment from order to delivery, and adaptability to demand shifts. Two triangles, one corner per dimension, compare automation that improves all three together, which is winning, with automation that improves only one of the three, which is incomplete. THREE DIMENSIONS OF WINNING Accuracy Speed Adaptability of inventory andorder data of fulfillment, fromorder to delivery of the network todemand shifts ALL THREE IMPROVE ONLY ONE IMPROVES Accuracy Speed Adaptability Accuracy Speed Adaptability Winning Incomplete Automation that improves only one of the three, without affecting the others, is incomplete. Three dimensions of a winning automation strategy Diagram of the three dimensions of supply chain automation success: accuracy of inventory and order data, speed of fulfillment from order to delivery, and adaptability to demand shifts. Two triangles, one corner per dimension, compare automation that improves all three together, which is winning, with automation that improves only one of the three, which is incomplete. THREE DIMENSIONS OF WINNING Accuracy Speed Adaptability of inventory and order data of fulfillment, from order to delivery of the network to demand shifts ALL THREE IMPROVE ONLY ONE IMPROVES Accuracy Accuracy Speed Adaptability Speed Adaptability Winning Incomplete Automation that improves only one of the threewithout affecting the others is incomplete.
Three dimensions of a winning automation strategy.

Inventory Optimization

Getting the right product to the right place starts with demand forecasting, not warehouse layout. AI-enabled forecasting tools can factor in historical sales, seasonality, and market trends, but the output is only as good as the data feeding it. Forecasts need regular review, not a set-it-and-forget-it deployment.

Once inventory is positioned correctly, warehouse slotting determines how efficiently it moves:

  • Organize storage based on actual demand patterns, not static assumptions
  • Reduce picker travel time through better slotting, which compounds with every automation layer added later

Inventory accuracy has to be maintained continuously, because every automation layer inherits the accuracy of the data beneath it.

Fulfillment and Distribution Automation

This is where most of the visible technology sits, and where sequencing matters most.

A modern warehouse management system (WMS) is the foundation. It gives real-time visibility into inventory, streamlines order processing, and optimizes picking routes. Everything layered on top, including robotics, AI-driven demand sensing, and automated storage, depends on the WMS feeding it clean, current data. A recent example is a custom WMS built across 60 3PL facilities, where exception-handling logic and seasonal rhythm calibration made the difference between scalable automation and costly rework.

The layers of a supply chain automation stack Layered diagram of a supply chain automation stack, with accurate inventory data and the WMS at the base, and conveyors, sorters, autonomous mobile robots, and wearable tools above. Inventory accuracy is maintained continuously, the WMS feeds clean, current data to the physical automation above it, and every layer inherits the accuracy of the data beneath it. LAYERED ON THE FOUNDATION WHAT EACH LAYER INHERITS PHYSICAL AUTOMATION adds capacity once the foundation is in place Conveyors and sorters Mobile robots (AMRs) Wearable tools clean, current data Modern WMS, the foundation real-time inventory visibility, order processing, picking routes Inventory accuracy maintained continuously Every layer inheritsthe accuracy of thedata beneath it. The layers of a supply chain automation stack Layered diagram of a supply chain automation stack, with accurate inventory data and the WMS at the base, and conveyors, sorters, autonomous mobile robots, and wearable tools above. Inventory accuracy is maintained continuously, the WMS feeds clean, current data to the physical automation above it, and every layer inherits the accuracy of the data beneath it. LAYERED ON THE FOUNDATION PHYSICAL AUTOMATION adds capacity once the foundation is in place Conveyors and sorters Mobile robots (AMRs) Wearable tools clean, current data Modern WMS, the foundation real-time inventory visibility,order processing, picking routes Inventory accuracy maintained continuously Every layer inherits the accuracyof the data beneath it.
The layers of a supply chain automation stack.

Physical automation adds real capacity once that foundation is in place:

  • Conveyors and sorters handle repetitive movement tasks
  • Autonomous mobile robots (AMRs) support picking, packing, and sorting with speed and consistency
  • Wearable tools, like smart glasses or exosuits, support workers rather than replace them, reducing fatigue and error rates on manual tasks

Momentum here is real but uneven. Gartner projects that by 2030, half of new warehouses in developed markets will be designed as human-optional facilities, which Gartner describes as robot-centric. Separately, Interact Analysis projects that only 13% of warehouses will have deployed even one fulfillment AMR by that same year. The two figures measure different things: the first covers new facilities in developed markets, the second covers warehouses overall. Together they show a market where design ambition is running ahead of deployment.

Data Visibility and Integration

Automation without integration just creates faster silos. Connecting the WMS, ERP, sales systems, and carrier data gives every stakeholder the same accurate picture, instead of each team working from a different version of the truth.

IoT tools extend that visibility further. RFID sensors, GPS trackers, and edge devices provide real-time data on product movement, performance, and bottlenecks. But visibility only pays off when it's acted on. Automated decisions still require accurate data, defined approval limits, audit trails, and human override procedures. Automation reduces manual work. It does not reduce the need for governance.

Where Automation Projects Actually Go Wrong

Most failed automation initiatives don't fail on the technology. They fail on sequencing and ownership. Four patterns show up repeatedly:

Buying technology before defining the objective

Tools get selected based on features, not on the specific problem they're solving. The result is capable software sitting on top of a process it was never designed to fix.

Avoiding this starts with outcome definition, not platform selection. An omnichannel upgrade across three brands shows what sequencing looks like when business outcome leads the conversation.

Skipping the phased rollout

Full-scale deployment without proving the approach at a smaller scale first amplifies mistakes instead of catching them early. Peak season is the wrong time to discover that a system configuration doesn't match how the operation actually runs.

No single owner for the rollout

Automation initiatives that span IT, operations, and finance need one accountable leader, not a committee. When ownership is distributed, decisions get deferred and the implementation drifts. That is why naming the owner is step two of the sequence below.

Treating change management as optional

Employees who don't understand why a process is changing resist it, regardless of how good the technology is. The system can be configured perfectly. If the people running it weren't prepared for the change, the outcome was decided before go-live.

Where to Start: A Practical Sequence

A sequenced approach reduces risk and makes each layer of automation easier to justify. The sequence starts with the objective and an accountable owner, moves to the data and WMS foundation, and ends with physical automation.

The six-step automation sequence Six-step sequence for supply chain automation: define the objective, assign an accountable owner, assess current processes, build the data and WMS foundation, pilot before scaling, and measure against defined KPIs. Steps one and two set the objective and the owner, steps three and four build the foundation, and steps five and six pilot physical automation and measure it. The owner is named before the assessment begins, so every later decision has a clear owner. A PRACTICAL SEQUENCE OBJECTIVE FOUNDATION PHYSICAL AUTOMATION 1 Define theobjective 2 Assign anaccountable owner 3 Assess currentprocesses 4 Data and WMSfoundation 5 Pilot beforescaling 6 Measure againstdefined KPIs Owner named before the assessment begins, so every later decision has a clear owner. A sequenced approach reduces risk and makes each layer of automation easier to justify. The six-step automation sequence Six-step sequence for supply chain automation: define the objective, assign an accountable owner, assess current processes, build the data and WMS foundation, pilot before scaling, and measure against defined KPIs. The owner is named before the assessment begins, so every later decision has a clear owner. A PRACTICAL SEQUENCE OBJECTIVE FOUNDATION PHYSICAL AUTOMATION 1 Define the objective 2 Assign an accountable owner 3 Assess current processes 4 Build the data and WMS foundation 5 Pilot before scaling 6 Measure against defined KPIs Owner named before the assessment begins,so every later decision has a clear owner. A sequenced approach reduces risk and makeseach layer of automation easier to justify.
The six-step automation sequence.

Define the objective before selecting any tool

Identify the specific process, cost, or bottleneck the investment needs to solve. This determines which technology actually fits.

Assign an accountable owner

One leader should be responsible for the rollout end to end, not a rotating set of stakeholders. Name this person before the assessment begins, so every later decision has a clear owner.

Assess current processes for automation potential

Look for high-cost, high-friction, or repetitive tasks first. These deliver the fastest, most visible ROI.

Build the data and WMS foundation

Connect the WMS, ERP, sales systems, and carrier data so that every automation layer works from the same accurate, current picture.

Pilot before scaling

Prove the approach in one process or one facility before committing to a full rollout.

Measure against defined KPIs

Inventory accuracy, out-of-stock rate, order fulfillment accuracy, and carrying cost of inventory show whether automation is improving accuracy and cost. Order cycle time from customer order to delivery shows whether it is improving speed. The time it takes to adjust capacity and fulfillment rules when demand shifts shows whether the network is more adaptable than before.

Three dimensions of winning, and what measures each Accuracy of inventory and order data, together with cost, is measured by inventory accuracy, out-of-stock rate, order fulfillment accuracy and carrying cost of inventory. Speed of fulfillment is measured by order cycle time, from customer order to delivery. Adaptability of the network is measured by the time it takes to adjust capacity and fulfillment rules when demand shifts. Together, these KPIs show whether accuracy and cost, speed, and adaptability are improving. WHAT WINNING MEANS WHAT MEASURES IT Accuracy and cost Speed Adaptability of inventory and order data of fulfillment of the network Inventory accuracy Out-of-stock rate Order fulfillment accuracy Carrying cost of inventory Order cycle time, from customer order to delivery Time to adjust capacity and fulfillment rules when demand shifts Together, these KPIs show whether accuracy and cost, speed, and adaptability are improving. Three dimensions of winning, and what measures each Accuracy and cost: inventory accuracy, out-of-stock rate, order fulfillment accuracy and carrying cost of inventory. Speed of fulfillment: order cycle time, from customer order to delivery. Adaptability of the network: the time it takes to adjust capacity and fulfillment rules when demand shifts. Together, these KPIs show whether accuracy and cost, speed, and adaptability are improving. WHAT WINNING MEANS Accuracy and cost Speed Adaptability of inventory and order data of fulfillment of the network Inventory accuracy Out-of-stock rate Order fulfillment accuracy Carrying cost of inventory Order cycle time, fromcustomer order to delivery Time to adjust capacity andfulfillment rules when demand shifts Together, these KPIs show whether accuracy andcost, speed, and adaptability are improving.
Each dimension of winning has its own measure. The KPIs in step six cover accuracy and cost, speed, and adaptability together, the same three dimensions that define winning.

Turning This Framework Into Results with Everest Technologies

A winning automation strategy still runs on the fundamentals covered above, but getting there requires a partner who has done the sequencing before, not just sold the technology.

Everest Technologies has delivered 300+ warehouse management system go-lives and is a Manhattan Associates Platinum Partner and two-time Manhattan Partner of the Year (Commerce Services, 2022; Collaboration Excellence, 2023), with delivery across retail, apparel, grocery, distribution, and 3PL operations. That delivery experience is what turns a phased rollout from theory into something that actually holds up at peak volume.

Three services connect directly to the strategy above:

Supply Chain

Builds the WMS foundation and phased implementation sequence that fulfillment automation depends on, so robotics and AI layers have accurate, real-time data to act on from day one.

Integration, Data & Analytics

Connects WMS, ERP, and other operational systems using MuleSoft, Boomi, and Kafka, closing the visibility gaps between systems so automation produces better decisions.

Quality Engineering

Builds validation directly into every release through Everest's Adaptive QA Orchestration, so changes to automated workflows are tested before they go live and stay governed as they scale, not just fast.

Whether an automation strategy is still being scoped or already underway and not delivering the expected results, Everest is glad to walk through what a fit-for-purpose sequence looks like for a specific operation.

Let's talk about your automation strategy
FAQs

Frequently asked questions

Supply chain automation refers to using technology, including software, robotics, and AI, to handle tasks across inventory management, warehousing, and fulfillment that were previously done manually. The goal is to reduce manual effort while improving speed, accuracy, and visibility.

Start with the objective, then the foundation, then the hardware. Define the specific process, cost, or bottleneck the investment needs to solve, and name one accountable owner. Assess current processes, then build the data and WMS foundation that connects inventory, orders, and carrier data. Pilot physical automation such as conveyors, sorters, or AMRs in one process or facility before scaling, and measure results against accuracy, speed, and adaptability.

In most cases, yes. A modern warehouse management system (WMS) gives real-time visibility into inventory, streamlines order processing, and optimizes picking routes. Robotics, AI-driven demand sensing, and automated storage depend on the WMS for clean, current data. Automating a step before that foundation is in place makes one step faster and leaves the upstream bottleneck unchanged. Everest implements Manhattan Active® Warehouse Management and Infios WMS, and synchronizes WMS logic with material handling equipment so physical automation runs on the same system intelligence.

Workforce shortages are a leading factor. 76% of supply chain and logistics leaders surveyed report notable workforce shortages in their operations, with transportation (61%) and warehouse operations (56%) the most affected functions. (Descartes, 2024) Automation is increasingly filling gaps that hiring alone can't close.

No. A phased approach, proving automation in one process or facility before scaling network-wide, is the lower-risk path. It allows for testing, refinement, and clearer ROI validation before a larger investment.

It depends heavily on use case and execution. McKinsey's research on AI-enabled distribution operations points to potential reductions of 20 to 30% in inventory, 5 to 20% in logistics costs, and 5 to 15% in procurement spend. These are ranges, not guarantees, and depend on data quality and implementation.

No. Automated decisions still depend on accurate data, defined approval limits, audit trails, and human override procedures for exceptions. Automation reduces manual labor. It doesn't remove the need for governance.

Define a clear objective and name one accountable owner. Then assess current processes to identify high-cost, high-friction tasks, build the data and WMS foundation that every automation layer will depend on, and pilot physical automation in one area before scaling. Most failed automation projects fail on sequencing, not on the technology itself.

Sources

  1. McKinsey & Company, "Unlocking the industrial potential of robotics and automation," January 2023 (2022 McKinsey Global Industrial Robotics Survey). https://www.mckinsey.com/capabilities/operations/our-insights/unlocking-the-industrial-potential-of-robotics-and-automation
  2. Descartes Systems Group, "How Bad Is the Supply Chain and Logistics Workforce Challenge?", January 2024 (survey of 1,000 supply chain and logistics decision-makers, late 2023). https://www.descartes.com/resources/news/descartes-study-reveals-76-supply-chain-and-logistics-operations-are-experiencing
  3. McKinsey & Company, "Harnessing the power of AI in distribution operations," 2024. https://www.mckinsey.com/industries/industrials/our-insights/distribution-blog/harnessing-the-power-of-ai-in-distribution-operations
  4. Gartner, "Gartner Predicts Half of New Warehouses Built in Developed Markets Will Be Human-Optional Facilities by 2030," press release, April 13, 2026. https://www.gartner.com/en/newsroom/2026-04-13-gartner-predicts-half-of-new-warehouses-built-in-developed-markets-will-be-human-optional-facilities-by-2030
  5. Interact Analysis, "Macro-economic factors cause slowdown in global mobile robot market," press release, January 15, 2025. https://interactanalysis.com/macro-economic-factors-cause-slowdown-in-global-mobile-robot-market/
  6. Everest Technologies, "Your Manhattan Active® Implementation Partner" (Platinum Partner status, Partner of the Year awards, 300+ WMS go-lives). https://everesttech.com/manhattan
  7. Everest Technologies, Supply Chain services (WMS and material handling equipment synchronization). https://everesttech.com/services/supply-chain
  8. Everest Technologies case studies: custom-built WMS across 60 3PL facilities (https://everesttech.com/case-studies/seasonal-retail-warehouse-execution) and Manhattan Active® upgrade across three brands (https://everesttech.com/case-studies/specialty-apparel-manhattan-active-upgrade)

About the Author

Simon Kish

Simon Kish

AVP, Consulting Services, Everest Technologies

As AVP of Consulting Services at Everest Technologies, Inc., Simon Kish helps retail and supply chain organizations turn complex technology challenges into practical, business-aligned strategies. Over 25 years, he has led digital transformations and IT turnarounds, with deep experience in IT strategic planning, systems integration, and vendor management across the full technology life cycle. He brings that hands-on perspective to Everest's retail and supply chain insights.