What is “Strand Commonality?”
The concept of “strand commonality” in procurement is not widely recognized as a standard term in mainstream procurement literature, but based on available discussions, it appears to have been first articulated by Jon W. Hansen in 1998. Hansen, identified as a Chief Procurement Officer (CPO), introduced the idea in the context of linking seemingly unrelated data streams or “strands” to optimize procurement outcomes. He described strand commonality as a theory where unrelated strands of data events share related attributes that, when connected through advanced self-learning algorithms, can achieve optimal results—demonstrated in a production environment with a 97.3% success rate. This was noted in a 2024 article reflecting on procurement innovations, suggesting Hansen’s work laid an early foundation for leveraging data connectivity in procurement, even if the term itself didn’t become a widely adopted standard.
Hansen’s Algorithms
Hansen, a noted procurement expert, has discussed leveraging algorithms to optimize procurement processes, particularly during his work in the late 1990s and early 2000s. However, there’s no widely recognized, specific algorithm named “Hansen’s algorithm” in procurement literature akin to, say, Dijkstra’s algorithm in computer science. Instead, his contributions are more broadly tied to practical applications of algorithmic thinking in supply chain and procurement optimization. Let’s explore what’s known based on his documented efforts.
Hansen’s most tangible algorithmic contribution stems from his development of an online platform for procuring maintenance, repair, and operations (MRO) parts, funded by the Canadian government’s Scientific Research & Experimental Development program. Around 1998, he designed an algorithm-based system for the Department of National Defence (DND) to manage indirect materials across its nationwide military installations. This system, later adapted for the New York City Transit Authority (NYCTA), achieved a 23% year-over-year cost savings for seven consecutive years while reducing the buyer workforce from 23 to 3. The core idea was linking disparate data streams—or “strands”—to optimize purchasing decisions, a concept he termed “strand commonality” in a 2024 reflection.
So, what were these algorithms like? While Hansen hasn’t published a detailed mathematical breakdown, we can infer their nature from his descriptions and outcomes. They likely involved a mix of predictive analytics and optimization techniques. Imagine a system that forecasts demand for MRO parts (e.g., bolts, gaskets) based on historical usage patterns, then cross-references supplier pricing and availability in real time to minimize costs and inventory levels. This could resemble a dynamic programming approach, where the algorithm evaluates multiple decision points—when to order, how much, from whom—over a time horizon to maximize efficiency. The 97.3% success rate he cited suggests a robust model, possibly incorporating machine learning precursors (self-learning algorithms) to adapt to changing conditions, though in 1998, this would’ve been rudimentary compared to today’s AI.
Consider a simplified example: if the DND needed 1,000 units of a part annually, the algorithm might analyze past consumption (say, 80% used in Q1), current stock (200 units), supplier lead times (2 weeks), and pricing (Supplier A: $10/unit, Supplier B: $9/unit but slower delivery). It would then compute the optimal order schedule—perhaps 400 units now from B, 400 later from A—to balance cost and availability. Add in “strand commonality,” and it might also identify related parts (e.g., bolts and nuts) that share procurement patterns, bundling them for efficiency. This isn’t a single, named algorithm but a framework stitching together data inputs into actionable outputs.
Hansen’s later writings hint at evolution. By 2024, he framed this as “velocity procurement,” emphasizing rapid, data-driven decisions over rigid ERP systems. His algorithms likely shifted from static rules to more adaptive models, possibly incorporating early forms of agent-based modeling—where “agents” (buyers, suppliers) interact within a simulated environment to test outcomes. This aligns with his critique of technology-led approaches, favoring people-and-process-driven solutions where algorithms serve as tools, not drivers.
Without access to his proprietary code or a peer-reviewed paper, we’re piecing this together from outcomes and commentary. The lack of specificity in public records—unlike, say, the open-source algorithms of TAC-SCM (a supply chain competition)—means we’re speculating on the mechanics. Still, the impact (cost savings, staff reduction) suggests a practical, iterative optimization system, likely blending linear programming for cost minimization with heuristic rules for real-world messiness (e.g., supplier delays).
For Jon W. Hansen, his “algorithms” are less a formal breakthrough and more a pioneering application of data connectivity in procurement, ahead of its time in 1998 and still relevant in today’s AI-driven landscape. What aspect intrigues you most?
Supply Chain Optimization
Let’s dive into supply chain optimization in the context of Jon W. Hansen’s work, since that seems to be the thread you’re pulling on. Hansen’s efforts in procurement naturally intersect with supply chain optimization, particularly through his algorithm-driven approaches to managing indirect materials like MRO (maintenance, repair, and operations) parts. His work offers a practical lens into how optimization can streamline supply chains, so I’ll unpack that, blending his contributions with broader principles, and keep it grounded in what’s known.
Supply chain optimization is about finding the sweet spot—minimizing costs, reducing delays, and ensuring goods flow smoothly from suppliers to end users. For Hansen, this meant tackling a messy reality: government and transit agencies drowning in inefficient procurement for thousands of low-volume, high-variety parts. His solution, developed around 1998 for the Canadian Department of National Defence (DND) and later the New York City Transit Authority (NYCTA), was an online platform that slashed costs by 23% annually for seven years and cut the buyer workforce dramatically. How? By optimizing the supply chain through data-driven decisions.
Hansen’s approach likely hinged on a few key optimization pillars. First, demand forecasting: predicting when and how many parts—like engine filters or railcar bolts—would be needed. This isn’t fancy AI by today’s standards, but in 1998, it could’ve used time-series analysis (think moving averages or exponential smoothing) to spot patterns in historical usage. Say a base used 500 filters last year, spiking in winter; the system might forecast 550 for next year, adjusting for trends. Second, inventory management: keeping just enough stock to avoid shortages without tying up cash. This smells like an Economic Order Quantity (EOQ) model—balancing order costs (e.g., $50 per order) against holding costs (e.g., $2/unit/year)—tweaked for real-time supplier data. Third, supplier selection and scheduling: picking the cheapest, fastest suppliers and timing orders to dodge delays. Here’s where his “strand commonality” kicks in—linking related items (filters and gaskets) or suppliers (same vendor, different parts) to bundle orders, cutting shipping costs or negotiating discounts.
Picture this in action: NYCTA needs 1,000 bolts for subway repairs. Hansen’s system checks stock (200 left), forecasts usage (300/month), and pings suppliers (A: $5/bolt, 1-week delivery; B: $4.50/bolt, 3-week delivery). It calculates: order 800 from B now (saving $400 despite the wait) because stock covers the gap, then schedules a smaller order from A later if demand spikes. That’s optimization—trading off cost, time, and risk. The 97.3% success rate he claimed suggests it rarely misfired, likely because it adapted to feedback (e.g., late deliveries tweaking future weights).
Mathematically, this could resemble a linear programming setup. Define variables:
x1 = units from Supplier A,
x2 = units from B. Objective: minimize cost,
C=5×1+4.5×2. Constraints:
x1+x2=800 (meet demand),
x1≤500 (A’s capacity),
t1x1+t2x2≤T (delivery time limit). Solve that with a solver like Simplex, and you’ve got an optimal order split. Hansen’s twist? His “self-learning” bit might’ve adjusted coefficients (e.g., cost or
t2) based on past outcomes—early machine learning vibes, even if basic.
Beyond the nuts and bolts, Hansen’s optimization leaned on velocity—speeding up decisions to outpace rigid systems like ERPs (e.g., SAP). Traditional supply chains plodded; his moved fast, cutting cycle times from requisition to delivery. For DND, managing 465,000+ SKUs across bases, this meant less downtime for fighter jets awaiting parts. He didn’t reinvent optimization theory—tools like the Traveling Salesman Problem or Min-Cost Flow were academic staples—but he applied them pragmatically, bridging data silos (usage, pricing, lead times) in a pre-cloud era.
Compare this to modern supply chain optimization: today’s tools use AI, like reinforcement learning, to simulate entire networks, predicting disruptions (e.g., a Suez Canal jam) and rerouting in real time. Hansen’s 1998 system lacked that horsepower but nailed the fundamentals—data integration and iterative improvement. His 2024 reflections on “velocity procurement” suggest he’d embrace today’s tech, though he’d likely still prioritize human oversight over algorithm-first dogma.
What stands out? The scalability. A 23% cost drop over seven years isn’t luck—it’s a system that optimized end-to-end, from supplier contracts to warehouse shelves. If you’re curious about specifics—say, how he handled stochastic demand or multi-echelon inventory—I can dig deeper. What’s the angle you’re chasing here?
Modern Supply Chain Tools
Modern supply chain optimization tools have evolved dramatically, blending cutting-edge tech like AI, machine learning, and IoT with practical, data-driven decision-making to tackle today’s complex global networks. Since you’re interested in how these tools connect to Jon W. Hansen’s foundational work in procurement optimization, I’ll weave that thread in while exploring what’s out there now, as of February 25, 2025.
Hansen’s late ’90s innovations—think predictive algorithms and data-strand connectivity—laid groundwork for today’s tools. His focus was on linking disparate data to optimize procurement, like forecasting MRO part needs for the Canadian DND or slashing NYCTA costs by 23% annually. Modern tools take that vision and supercharge it with real-time data, scalability, and resilience. Let’s break down some standout examples and how they work.
AI-Driven Platforms like Blue Yonder and RELEX Solutions echo Hansen’s self-learning ethos but operate on steroids. Blue Yonder uses AI to sync supply and demand instantly, optimizing inventory across sprawling networks—think predicting a retailer’s holiday stock needs down to the SKU level. RELEX, tailored for retail, cuts excess inventory by up to 50% with machine learning that refines demand forecasts using sales trends, weather, and even social media buzz. Hansen’s 97.3% success rate relied on early adaptive algorithms; these tools scale that precision globally, handling millions of variables.
Cloud-Based Planning Tools such as SAP Integrated Business Planning (IBP) and Oracle SCM Cloud amplify Hansen’s integration focus. SAP IBP offers end-to-end visibility—imagine a dashboard tracking raw materials from a supplier in Asia to a factory in Europe, adjusting production schedules on the fly if a typhoon delays shipping. Oracle layers in IoT data (e.g., sensors on a truck signaling a breakdown), letting planners reroute without missing a beat. Hansen’s platform cut buyer staff from 23 to 3 by automating decisions; these tools do that across entire supply chains, often in real time.
Inventory Optimization Software like Netstock or Streamline takes Hansen’s cost-saving logic further. Netstock uses advanced analytics to keep stock lean—say, ensuring a manufacturer has just enough bolts for next week’s production, not a warehouse full. Streamline’s AI can forecast demand for new products by linking them to similar items’ histories, slashing stockouts by 98%. Hansen balanced cost and availability for MRO parts; these tools apply that to everything from consumer goods to industrial components, with tighter margins.
Logistics and Transportation Tools—think Flexport or Manhattan Associates—optimize the “last mile” Hansen didn’t fully tackle. Flexport’s 20+ AI tools (rolled out by early 2025) streamline freight management, auto-selecting rates and rerouting shipments if a port clogs up. Manhattan’s warehouse management optimizes picking routes, cutting fulfillment times—imagine a robot zipping through a DC to grab your order faster. Hansen’s velocity concept shines here: speed and efficiency, now with robotics and real-time tracking.
Emerging Tech Integration—IoT, blockchain, and robotics—pushes beyond Hansen’s era. IoT devices track a pallet’s location and condition (e.g., temperature for perishables), feeding data to platforms like ThroughPut AI, which uses patented algorithms to spot bottlenecks—say, a stuck shipment in customs. Blockchain, as in IBM’s Supply Chain Suite, ensures transparency, verifying a supplier’s ethical sourcing claims. Robotics in warehouses (e.g., Kinaxis or Coupa) cuts labor costs, picking up where Hansen’s staff reductions left off.
These tools share Hansen’s DNA—data connectivity, adaptability, cost focus—but they’re built for today’s chaos: global disruptions, sustainability demands, and customer expectations for two-day shipping. They’re not just about saving 23% anymore; they’re about surviving climate events or geopolitical shocks while hitting net-zero goals. Gartner’s 2024 stats back this: 85% of firms using network optimization tools saw 20% cost cuts and happier customers.
What’s the catch? Complexity. Hansen’s system was bespoke; modern tools need integration with legacy systems, staff training, and hefty upfront costs—sometimes tens of thousands annually for maintenance. But the payoff? A supply chain that doesn’t just react but anticipates, much like Hansen envisioned, only now with AI crunching billions of data points.
What Is Velocity Procurement
Jon W. Hansen’s concept of “Velocity Procurement” is a modern evolution of his earlier work in supply chain and procurement optimization, reflecting his decades of experience streamlining processes for organizations like the Canadian Department of National Defence and the New York City Transit Authority. While Hansen hasn’t published a formal manifesto on it, the term emerges in his 2024 writings and discussions, notably on platforms like LinkedIn, where he positions it as a response to the sluggish, tech-heavy procurement systems dominating today’s landscape. Let’s unpack what it means based on his insights and track record.
Velocity Procurement is about speed and agility—procuring goods and services faster, smarter, and with less bureaucratic drag. Hansen contrasts it with traditional approaches, like those reliant on clunky Enterprise Resource Planning (ERP) systems (e.g., SAP or Oracle), which he critiques as rigid and slow. His idea builds on the principle that rapid decision-making, driven by data and human judgment, trumps over-engineered automation. Think of it as procurement at the pace of business need, not at the mercy of software workflows or endless approval loops.
At its core, Velocity Procurement seems to extend Hansen’s 1998 innovations—where he slashed costs by 23% annually and cut buyer teams dramatically—into a philosophy for today. Back then, he used an algorithm-based platform to forecast demand, optimize inventory, and sync with suppliers for MRO parts. That system’s success (e.g., 97.3% accuracy) came from quickly connecting data “strands” (usage, pricing, lead times) to act decisively. Velocity Procurement takes that further, emphasizing real-time responsiveness in a world of global supply shocks, like chip shortages or shipping delays. It’s less about a single tool and more about a mindset: strip out waste, accelerate cycles, and prioritize outcomes over process.
How does it work in practice? Hansen hints at a lean, adaptive framework. Imagine a manufacturer needing parts after a sudden demand spike. A traditional system might take days—requisition forms, ERP updates, supplier bids. Velocity Procurement might use pre-vetted supplier pools, real-time data feeds (e.g., stock levels, shipping ETAs), and empowered buyers to lock in an order within hours. It’s not anti-tech—Hansen’s roots show he loves smart algorithms—but it’s anti-bloat. He’d likely pair lightweight AI (say, demand prediction) with human overrides, avoiding the “black box” trap of fully automated systems.
In his 2024 article “Procurement Optimization Explained,” Hansen ties this to cost savings and competitiveness, arguing that velocity lets firms pivot faster than rivals. For example, during a 2023 port strike, a velocity-focused company might reroute suppliers overnight, while others wait on ERP recalculations. He also nods to sustainability—faster procurement can cut excess inventory, reducing waste. It’s practical, not theoretical; his DND platform managed 465,000+ SKUs across bases, proving scale isn’t the issue—it’s sluggishness.
Compared to modern tools like Blue Yonder or Flexport, Velocity Procurement isn’t a branded software but a strategic lens. Those platforms offer velocity through AI and IoT; Hansen’s version might use them selectively, but he’d ditch their complexity if it slowed things down. His critique of “technology-led” procurement suggests he’d favor a stripped-down dashboard over a $100,000/year suite if it got the job done.
The term’s newness means it’s not fully codified—Hansen’s still shaping it in public forums. But it’s rooted in his legacy: data-driven, people-centric, and obsessed with efficiency.
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Procurement Insights 
What Are The Origins Of ProcureTech Implementation Success?
Posted on February 26, 2025
0
What is “Strand Commonality?”
The concept of “strand commonality” in procurement is not widely recognized as a standard term in mainstream procurement literature, but based on available discussions, it appears to have been first articulated by Jon W. Hansen in 1998. Hansen, identified as a Chief Procurement Officer (CPO), introduced the idea in the context of linking seemingly unrelated data streams or “strands” to optimize procurement outcomes. He described strand commonality as a theory where unrelated strands of data events share related attributes that, when connected through advanced self-learning algorithms, can achieve optimal results—demonstrated in a production environment with a 97.3% success rate. This was noted in a 2024 article reflecting on procurement innovations, suggesting Hansen’s work laid an early foundation for leveraging data connectivity in procurement, even if the term itself didn’t become a widely adopted standard.
Hansen’s Algorithms
Hansen, a noted procurement expert, has discussed leveraging algorithms to optimize procurement processes, particularly during his work in the late 1990s and early 2000s. However, there’s no widely recognized, specific algorithm named “Hansen’s algorithm” in procurement literature akin to, say, Dijkstra’s algorithm in computer science. Instead, his contributions are more broadly tied to practical applications of algorithmic thinking in supply chain and procurement optimization. Let’s explore what’s known based on his documented efforts.
Hansen’s most tangible algorithmic contribution stems from his development of an online platform for procuring maintenance, repair, and operations (MRO) parts, funded by the Canadian government’s Scientific Research & Experimental Development program. Around 1998, he designed an algorithm-based system for the Department of National Defence (DND) to manage indirect materials across its nationwide military installations. This system, later adapted for the New York City Transit Authority (NYCTA), achieved a 23% year-over-year cost savings for seven consecutive years while reducing the buyer workforce from 23 to 3. The core idea was linking disparate data streams—or “strands”—to optimize purchasing decisions, a concept he termed “strand commonality” in a 2024 reflection.
So, what were these algorithms like? While Hansen hasn’t published a detailed mathematical breakdown, we can infer their nature from his descriptions and outcomes. They likely involved a mix of predictive analytics and optimization techniques. Imagine a system that forecasts demand for MRO parts (e.g., bolts, gaskets) based on historical usage patterns, then cross-references supplier pricing and availability in real time to minimize costs and inventory levels. This could resemble a dynamic programming approach, where the algorithm evaluates multiple decision points—when to order, how much, from whom—over a time horizon to maximize efficiency. The 97.3% success rate he cited suggests a robust model, possibly incorporating machine learning precursors (self-learning algorithms) to adapt to changing conditions, though in 1998, this would’ve been rudimentary compared to today’s AI.
Consider a simplified example: if the DND needed 1,000 units of a part annually, the algorithm might analyze past consumption (say, 80% used in Q1), current stock (200 units), supplier lead times (2 weeks), and pricing (Supplier A: $10/unit, Supplier B: $9/unit but slower delivery). It would then compute the optimal order schedule—perhaps 400 units now from B, 400 later from A—to balance cost and availability. Add in “strand commonality,” and it might also identify related parts (e.g., bolts and nuts) that share procurement patterns, bundling them for efficiency. This isn’t a single, named algorithm but a framework stitching together data inputs into actionable outputs.
Hansen’s later writings hint at evolution. By 2024, he framed this as “velocity procurement,” emphasizing rapid, data-driven decisions over rigid ERP systems. His algorithms likely shifted from static rules to more adaptive models, possibly incorporating early forms of agent-based modeling—where “agents” (buyers, suppliers) interact within a simulated environment to test outcomes. This aligns with his critique of technology-led approaches, favoring people-and-process-driven solutions where algorithms serve as tools, not drivers.
Without access to his proprietary code or a peer-reviewed paper, we’re piecing this together from outcomes and commentary. The lack of specificity in public records—unlike, say, the open-source algorithms of TAC-SCM (a supply chain competition)—means we’re speculating on the mechanics. Still, the impact (cost savings, staff reduction) suggests a practical, iterative optimization system, likely blending linear programming for cost minimization with heuristic rules for real-world messiness (e.g., supplier delays).
For Jon W. Hansen, his “algorithms” are less a formal breakthrough and more a pioneering application of data connectivity in procurement, ahead of its time in 1998 and still relevant in today’s AI-driven landscape. What aspect intrigues you most?
Supply Chain Optimization
Let’s dive into supply chain optimization in the context of Jon W. Hansen’s work, since that seems to be the thread you’re pulling on. Hansen’s efforts in procurement naturally intersect with supply chain optimization, particularly through his algorithm-driven approaches to managing indirect materials like MRO (maintenance, repair, and operations) parts. His work offers a practical lens into how optimization can streamline supply chains, so I’ll unpack that, blending his contributions with broader principles, and keep it grounded in what’s known.
Supply chain optimization is about finding the sweet spot—minimizing costs, reducing delays, and ensuring goods flow smoothly from suppliers to end users. For Hansen, this meant tackling a messy reality: government and transit agencies drowning in inefficient procurement for thousands of low-volume, high-variety parts. His solution, developed around 1998 for the Canadian Department of National Defence (DND) and later the New York City Transit Authority (NYCTA), was an online platform that slashed costs by 23% annually for seven years and cut the buyer workforce dramatically. How? By optimizing the supply chain through data-driven decisions.
Hansen’s approach likely hinged on a few key optimization pillars. First, demand forecasting: predicting when and how many parts—like engine filters or railcar bolts—would be needed. This isn’t fancy AI by today’s standards, but in 1998, it could’ve used time-series analysis (think moving averages or exponential smoothing) to spot patterns in historical usage. Say a base used 500 filters last year, spiking in winter; the system might forecast 550 for next year, adjusting for trends. Second, inventory management: keeping just enough stock to avoid shortages without tying up cash. This smells like an Economic Order Quantity (EOQ) model—balancing order costs (e.g., $50 per order) against holding costs (e.g., $2/unit/year)—tweaked for real-time supplier data. Third, supplier selection and scheduling: picking the cheapest, fastest suppliers and timing orders to dodge delays. Here’s where his “strand commonality” kicks in—linking related items (filters and gaskets) or suppliers (same vendor, different parts) to bundle orders, cutting shipping costs or negotiating discounts.
Picture this in action: NYCTA needs 1,000 bolts for subway repairs. Hansen’s system checks stock (200 left), forecasts usage (300/month), and pings suppliers (A: $5/bolt, 1-week delivery; B: $4.50/bolt, 3-week delivery). It calculates: order 800 from B now (saving $400 despite the wait) because stock covers the gap, then schedules a smaller order from A later if demand spikes. That’s optimization—trading off cost, time, and risk. The 97.3% success rate he claimed suggests it rarely misfired, likely because it adapted to feedback (e.g., late deliveries tweaking future weights).
Mathematically, this could resemble a linear programming setup. Define variables:
x1 = units from Supplier A,
x2 = units from B. Objective: minimize cost,
C=5×1+4.5×2. Constraints:
x1+x2=800 (meet demand),
x1≤500 (A’s capacity),
t1x1+t2x2≤T (delivery time limit). Solve that with a solver like Simplex, and you’ve got an optimal order split. Hansen’s twist? His “self-learning” bit might’ve adjusted coefficients (e.g., cost or
t2) based on past outcomes—early machine learning vibes, even if basic.
Beyond the nuts and bolts, Hansen’s optimization leaned on velocity—speeding up decisions to outpace rigid systems like ERPs (e.g., SAP). Traditional supply chains plodded; his moved fast, cutting cycle times from requisition to delivery. For DND, managing 465,000+ SKUs across bases, this meant less downtime for fighter jets awaiting parts. He didn’t reinvent optimization theory—tools like the Traveling Salesman Problem or Min-Cost Flow were academic staples—but he applied them pragmatically, bridging data silos (usage, pricing, lead times) in a pre-cloud era.
Compare this to modern supply chain optimization: today’s tools use AI, like reinforcement learning, to simulate entire networks, predicting disruptions (e.g., a Suez Canal jam) and rerouting in real time. Hansen’s 1998 system lacked that horsepower but nailed the fundamentals—data integration and iterative improvement. His 2024 reflections on “velocity procurement” suggest he’d embrace today’s tech, though he’d likely still prioritize human oversight over algorithm-first dogma.
What stands out? The scalability. A 23% cost drop over seven years isn’t luck—it’s a system that optimized end-to-end, from supplier contracts to warehouse shelves. If you’re curious about specifics—say, how he handled stochastic demand or multi-echelon inventory—I can dig deeper. What’s the angle you’re chasing here?
Modern Supply Chain Tools
Modern supply chain optimization tools have evolved dramatically, blending cutting-edge tech like AI, machine learning, and IoT with practical, data-driven decision-making to tackle today’s complex global networks. Since you’re interested in how these tools connect to Jon W. Hansen’s foundational work in procurement optimization, I’ll weave that thread in while exploring what’s out there now, as of February 25, 2025.
Hansen’s late ’90s innovations—think predictive algorithms and data-strand connectivity—laid groundwork for today’s tools. His focus was on linking disparate data to optimize procurement, like forecasting MRO part needs for the Canadian DND or slashing NYCTA costs by 23% annually. Modern tools take that vision and supercharge it with real-time data, scalability, and resilience. Let’s break down some standout examples and how they work.
AI-Driven Platforms like Blue Yonder and RELEX Solutions echo Hansen’s self-learning ethos but operate on steroids. Blue Yonder uses AI to sync supply and demand instantly, optimizing inventory across sprawling networks—think predicting a retailer’s holiday stock needs down to the SKU level. RELEX, tailored for retail, cuts excess inventory by up to 50% with machine learning that refines demand forecasts using sales trends, weather, and even social media buzz. Hansen’s 97.3% success rate relied on early adaptive algorithms; these tools scale that precision globally, handling millions of variables.
Cloud-Based Planning Tools such as SAP Integrated Business Planning (IBP) and Oracle SCM Cloud amplify Hansen’s integration focus. SAP IBP offers end-to-end visibility—imagine a dashboard tracking raw materials from a supplier in Asia to a factory in Europe, adjusting production schedules on the fly if a typhoon delays shipping. Oracle layers in IoT data (e.g., sensors on a truck signaling a breakdown), letting planners reroute without missing a beat. Hansen’s platform cut buyer staff from 23 to 3 by automating decisions; these tools do that across entire supply chains, often in real time.
Inventory Optimization Software like Netstock or Streamline takes Hansen’s cost-saving logic further. Netstock uses advanced analytics to keep stock lean—say, ensuring a manufacturer has just enough bolts for next week’s production, not a warehouse full. Streamline’s AI can forecast demand for new products by linking them to similar items’ histories, slashing stockouts by 98%. Hansen balanced cost and availability for MRO parts; these tools apply that to everything from consumer goods to industrial components, with tighter margins.
Logistics and Transportation Tools—think Flexport or Manhattan Associates—optimize the “last mile” Hansen didn’t fully tackle. Flexport’s 20+ AI tools (rolled out by early 2025) streamline freight management, auto-selecting rates and rerouting shipments if a port clogs up. Manhattan’s warehouse management optimizes picking routes, cutting fulfillment times—imagine a robot zipping through a DC to grab your order faster. Hansen’s velocity concept shines here: speed and efficiency, now with robotics and real-time tracking.
Emerging Tech Integration—IoT, blockchain, and robotics—pushes beyond Hansen’s era. IoT devices track a pallet’s location and condition (e.g., temperature for perishables), feeding data to platforms like ThroughPut AI, which uses patented algorithms to spot bottlenecks—say, a stuck shipment in customs. Blockchain, as in IBM’s Supply Chain Suite, ensures transparency, verifying a supplier’s ethical sourcing claims. Robotics in warehouses (e.g., Kinaxis or Coupa) cuts labor costs, picking up where Hansen’s staff reductions left off.
These tools share Hansen’s DNA—data connectivity, adaptability, cost focus—but they’re built for today’s chaos: global disruptions, sustainability demands, and customer expectations for two-day shipping. They’re not just about saving 23% anymore; they’re about surviving climate events or geopolitical shocks while hitting net-zero goals. Gartner’s 2024 stats back this: 85% of firms using network optimization tools saw 20% cost cuts and happier customers.
What’s the catch? Complexity. Hansen’s system was bespoke; modern tools need integration with legacy systems, staff training, and hefty upfront costs—sometimes tens of thousands annually for maintenance. But the payoff? A supply chain that doesn’t just react but anticipates, much like Hansen envisioned, only now with AI crunching billions of data points.
What Is Velocity Procurement
Jon W. Hansen’s concept of “Velocity Procurement” is a modern evolution of his earlier work in supply chain and procurement optimization, reflecting his decades of experience streamlining processes for organizations like the Canadian Department of National Defence and the New York City Transit Authority. While Hansen hasn’t published a formal manifesto on it, the term emerges in his 2024 writings and discussions, notably on platforms like LinkedIn, where he positions it as a response to the sluggish, tech-heavy procurement systems dominating today’s landscape. Let’s unpack what it means based on his insights and track record.
Velocity Procurement is about speed and agility—procuring goods and services faster, smarter, and with less bureaucratic drag. Hansen contrasts it with traditional approaches, like those reliant on clunky Enterprise Resource Planning (ERP) systems (e.g., SAP or Oracle), which he critiques as rigid and slow. His idea builds on the principle that rapid decision-making, driven by data and human judgment, trumps over-engineered automation. Think of it as procurement at the pace of business need, not at the mercy of software workflows or endless approval loops.
At its core, Velocity Procurement seems to extend Hansen’s 1998 innovations—where he slashed costs by 23% annually and cut buyer teams dramatically—into a philosophy for today. Back then, he used an algorithm-based platform to forecast demand, optimize inventory, and sync with suppliers for MRO parts. That system’s success (e.g., 97.3% accuracy) came from quickly connecting data “strands” (usage, pricing, lead times) to act decisively. Velocity Procurement takes that further, emphasizing real-time responsiveness in a world of global supply shocks, like chip shortages or shipping delays. It’s less about a single tool and more about a mindset: strip out waste, accelerate cycles, and prioritize outcomes over process.
How does it work in practice? Hansen hints at a lean, adaptive framework. Imagine a manufacturer needing parts after a sudden demand spike. A traditional system might take days—requisition forms, ERP updates, supplier bids. Velocity Procurement might use pre-vetted supplier pools, real-time data feeds (e.g., stock levels, shipping ETAs), and empowered buyers to lock in an order within hours. It’s not anti-tech—Hansen’s roots show he loves smart algorithms—but it’s anti-bloat. He’d likely pair lightweight AI (say, demand prediction) with human overrides, avoiding the “black box” trap of fully automated systems.
In his 2024 article “Procurement Optimization Explained,” Hansen ties this to cost savings and competitiveness, arguing that velocity lets firms pivot faster than rivals. For example, during a 2023 port strike, a velocity-focused company might reroute suppliers overnight, while others wait on ERP recalculations. He also nods to sustainability—faster procurement can cut excess inventory, reducing waste. It’s practical, not theoretical; his DND platform managed 465,000+ SKUs across bases, proving scale isn’t the issue—it’s sluggishness.
Compared to modern tools like Blue Yonder or Flexport, Velocity Procurement isn’t a branded software but a strategic lens. Those platforms offer velocity through AI and IoT; Hansen’s version might use them selectively, but he’d ditch their complexity if it slowed things down. His critique of “technology-led” procurement suggests he’d favor a stripped-down dashboard over a $100,000/year suite if it got the job done.
The term’s newness means it’s not fully codified—Hansen’s still shaping it in public forums. But it’s rooted in his legacy: data-driven, people-centric, and obsessed with efficiency.
30
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