Supply Chain Leaders Invest in Automation — and How That Will Change

Colin McAleenan, vice president of marketing and business development at HAI Robotics, notes that how supply chain leaders invest in automation has fundamentally changed over the past five years. Five years ago, VC (Venture Capital) appetite was robust and capital flowed into the supply chain automation sector. But now, in an environment where interest rates have risen and capital costs have increased, automation decisions are being made far more cautiously, met with skepticism from CFOs. McAleenan observes that careers of automation project leaders are at risk; a failed project can mean not just capital loss but personal reputation damage as well. From a supply chain perspective, HAI Robotics is a pioneer in the ACR (Autonomous Case-handling Robot) category and competes in the goods-to-person market with rivals such as Geek+, AutoStore, Exotec, Symbotic, and Locus Robotics. The HaiPick system can handle cases and totes in racks 5-10 meters high and increases cube utilization 4-5x compared to traditional racking.

McAleenan emphasizes that supply chain leaders must move away from a single distribution center mindset and adopt a broader network, planning, and delivery perspective. An automation decision at one DC has a domino effect on upstream processes (planning, inventory, procurement) and downstream processes (delivery, last-mile, customer experience). McAleenan notes that data science and processes—including the automation supplier—require simultaneous investment. He uses the phrase "mutually assured success"; the supplier's success and the customer's success are interdependent. From a supply chain perspective, network design decisions, greenfield versus brownfield facility choices, geographic placement of DCs, capacity allocation by customer cluster, and WMS, OMS, TMS integration are critical decision parameters. Llamasoft (now Coupa), Optilogic, AnyLogic, and Kinaxis are key players in the network optimization and simulation domains.

According to McAleenan, the pain points of automation projects are a long journey in sustaining the supplier-customer relationship. McAleenan uses the phrase "If you can see what the data is telling you right in front of you on the shop floor, chances are you've got the right inputs and the right processes". Data visibility, real-time operator dashboards on the shop floor, andon board and visual factory management applications are concrete indicators of automation success. From a supply chain perspective, OEE (Overall Equipment Effectiveness) is the product of three dimensions—availability, performance, and quality—and world-class standard is 85% or higher. MTBF (Mean Time Between Failures) and MTTR (Mean Time To Repair) metrics directly impact the ROI of automation investment. Application of Six Sigma and lean manufacturing principles in the automated warehouse is documented within ISO 9001 and ISO 13485 quality management systems frameworks.

McAleenan's observations clearly summarize today's automation investment climate. Five years ago, in an environment of abundant capital, failed projects could easily be absorbed until the next round. Today, every dollar must pass through capex committees and investment review boards. From a supply chain perspective, as WACC (Weighted Average Cost of Capital) has risen to the 8-12% range, calculations of NPV (Net Present Value), IRR (Internal Rate of Return), and payback period must be performed more rigorously. Total Cost of Ownership (TCO) models include not just hardware cost but also integration cost, change management, training, spare parts, energy consumption, and decommissioning line items. The RaaS (Robotics-as-a-Service) model is becoming increasingly prevalent in response to demand to shift from capex to opex. In conclusion, McAleenan's views clearly demonstrate that warehouse automation is an engineering and data investment requiring financial discipline.

Key Takeaways:
\n1. McAleenan notes that VC appetite was robust 5 years ago.
\n2. Interest rates are making automation decisions more cautious.
\n3. Network, planning, and delivery mindset is essential, not a single DC perspective.
\n4. Data science and processes require investment alongside the automation supplier.
\n5. Data visibility on the shop floor is a concrete indicator of correct processes.

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