Comparison of SAP’s Joule Feedback Loops and Hansen’s Learning Loopback Process (Post 1 of 3 Today)

As noted in previous posts, the Procurement Insights blog doesn’t explicitly detail a “learning loopback process for self-learning algorithms.” However, past posts imply an iterative, adaptive mechanism within an Agent-based Metaprise model, where autonomous agents refine procurement decisions (e.g., sourcing cosmetics packaging or coffee beans) based on real-time data and outcomes.

This process—constructed from Agent-based Metaprise principles (e.g., 2007, 2014, and “The GenAI Metaprise,” Oct 11, 2024)—involves observing data, acting, assessing results, and adapting, aligning with self-learning concepts but prioritizing practical outcomes over technical specifics. In contrast, SAP’s Joule, an AI copilot in S/4HANA Cloud, uses explicit feedback loops, such as gradient descent for procurement analytics, to optimize tasks like supplier selection and spend management, leveraging structured AI methodologies.

Below, I compare how SAP’s Joule might employ explicit feedback loops (e.g., gradient descent) with the above-referenced learning loopback process, focusing on their approaches to self-learning algorithms in procurement. I’ll ground this in the Metaprise framework, incorporate the 2025 tariff context (e.g., cosmetics and coffee supply chains), and highlight technicality, adaptability, and application differences.

ProcureTech Solution Providers Worth Watching

In the context of the above, the following ProcureTech solution providers are at various stages of making (or likely making) the transition to an Agent-based Metaprise or hybrid model.

Here is a list of ProcureTech solution providers in 2025 that I believe are (or could be) on the “right path” to being on the right path, perhaps finally breaking the high generational ProcureTech initiative failure rate. I am tracking them, and I suggest you do the same.

ConvergentIS
Focal Point
JAGGAER
Zycus
ORO Labs
ProcureTech, An ORO Labs Company
Pactum AI
SPARETECH
Ivalua
GEP Worldwide (GEP SMART)
akirolabs
Sievo
SAP Ariba

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Comparison of SAP’s Joule Feedback Loops and Hansen’s Learning Loopback Process

1. Technical Approach

• SAP’s Joule (Explicit Feedback Loops):
  • Mechanism: Joule likely uses gradient descent or similar optimization techniques in supervised or reinforcement learning models to minimize errors in procurement analytics (e.g., cost predictions, supplier scoring). Gradient descent iteratively adjusts model parameters (e.g., weights for supplier reliability) based on a loss function (e.g., deviation from optimal spend), converging to better outcomes.
  • Process:
    1. Data Input: Structured data (e.g., supplier bids, historical costs) feeds predictive models.
    2. Prediction: Models output recommendations (e.g., select Vietnam for $0.22/unit cosmetics packaging).
    3. Feedback: Errors (e.g., over-costly supplier choice) update weights via gradient descent, reducing future errors.
    4. Iteration: Continuous training refines accuracy, leveraging SAP’s cloud infrastructure for scale.
  • Example: In cosmetics, Joule might analyze China’s $0.32/unit packaging under 60% tariffs, recommend Mexico ($0.30/unit), and adjust based on cost savings ($0.02/unit), optimizing Estée Lauder’s $1B savings goal (web ID 0).
  • Technicality: Explicit, math-driven (e.g., ∂L/∂w for loss L, weights w), relying on large datasets and SAP’s predefined AI skills (500+ by 2025, Forbes, Jan 21, 2025).
• Hansen’s Learning Loopback Process (Inferred):
  • Mechanism: Implicit, process-driven feedback where Metaprise agents adapt decisions based on stakeholder outcomes, not formal algorithms like gradient descent. Likely resembles reinforcement learning’s explore-exploit logic, where agents test actions (e.g., sourcing coffee from Ethiopia) and refine based on rewards (e.g., cost savings, delivery speed).
  • Process:
    1. Observation: Agents collect unstructured data (e.g., tariff hikes, supplier delays).
    2. Action: Propose commodity-specific solutions (e.g., Brazil’s $0.55/kg talc for Revlon).
    3. Assessment: Evaluate outcomes (e.g., $0.10/unit savings, 95% demand retention).
    4. Adaptation: Update priorities (e.g., favor Brazil over China), iterating without explicit math.
  • Example: For coffee, agents observe 10% tariff costs ($4.40/kg Brazil), shift to Guatemala ($4/kg), assess $0.20/kg savings, and prioritize Guatemala, capping retail hikes at $0.10–$0.20/lb.
  • Technicality: Non-explicit, focusing on agent interactions and practical feedback (e.g., supplier performance metrics) over mathematical optimization, reflecting Hansen’s practitioner-oriented approach (2007 post,).
• Comparison:
  • Joule: Relies on structured, algorithm-heavy loops (gradient descent) for precision, assuming robust data and computational power. Best for predictable, data-rich tasks.
  • Hansen: Uses flexible, outcome-driven loops, less reliant on formal math, suited for dynamic, stakeholder-diverse environments like tariff-impacted supply chains (2024 post,).
  • Cosmetics/Coffee: Joule excels in cosmetics’ spend analytics (e.g., MAC’s $0.05–$0.10/unit savings), while Hansen’s process adapts to coffee’s volatile farmer margins ($3.20/kg).

2. Adaptability to Market Volatility

• SAP’s Joule:
  • Strength: Rapidly processes tariff changes (e.g., 60% on China) via real-time analytics, recommending shifts (e.g., Vietnam packaging for Revlon) with 30% cycle time cuts (web ID 19). Pretrained models handle structured disruptions.
  • Weakness: Struggles with unstructured volatility (e.g., coffee farmer strikes, cosmetics’ ESG shifts) if data isn’t pre-modeled. Gradient descent requires retraining for novel scenarios, risking delays (5–10% in high-transaction settings).
  • Cosmetics Example: Joule optimizes Estée Lauder’s $0.40/unit bioplastics sourcing but may lag if new tariffs (e.g., 20% on India) emerge unexpectedly, needing human tweaks.
  • Coffee Example: Predicts $0.20/lb coffee hikes but may miss smallholder dynamics (80% of growers), requiring manual intervention.
• Hansen’s Learning Loopback:
  • Strength: Highly adaptive, as agents learn from local outcomes (e.g., Mexico’s 2-week packaging delivery) without rigid models, ideal for 2025’s tariff flux (e.g., paused Canada/Mexico duties). Hansen’s model saved 23% by aligning with commodity needs ().
  • Weakness: Slower to scale in data-heavy environments (e.g., cosmetics’ 10M+ units), as iterative learning lacks Joule’s computational speed. Relies on human oversight for ethics, per Hansen’s caution (2009 post,).
  • Cosmetics Example: Shifts MAC to Egypt’s $70/kg scents seamlessly, adapting to China’s 84% retaliation, ensuring four–six launches/year.
  • Coffee Example: Prioritizes Colombia’s $3.20/kg beans, learning from farmer feedback, but may need time to optimize globally.
• Comparison:
  • Joule: Faster for structured volatility (e.g., tariff cost modeling), but brittle for unpredictable shifts, needing retraining.
  • Hansen: Slower but more flexible for unstructured chaos (e.g., coffee supply disruptions), thriving in stakeholder-driven contexts.
  • Edge: Hansen’s process suits 2025’s trade war flux (cosmetics’ $1B export risks, coffee’s $500M losses), aligning with his agility focus (2024 post).

3. Stakeholder and Commodity Alignment

• SAP’s Joule:
  • Approach: Centralizes learning via enterprise-wide models, optimizing for broad metrics (e.g., total spend, delivery times). Gradient descent prioritizes system efficiency, potentially overlooking local nuances (e.g., cosmetics’ luxury vs. affordability).
  • Strength: Scales for large firms (e.g., Estée Lauder’s 150 countries, web ID 0), delivering $200–$300M tariff exemptions via analytics.
  • Weakness: May misalign with diverse stakeholders (e.g., coffee farmers’ $500–$1,000/year losses), as models favor corporate goals over granular needs, risking generic sourcing Hansen critiques (2007 post,).
  • Cosmetics Example: Suggests India’s $2/kg mica for Revlon, but humans must ensure it meets mass-market standards.
  • Coffee Example: Recommends Brazil’s $4.40/kg coffee, but smallholder ethics (80% of growers) need manual checks.
• Hansen’s Learning Loopback:
  • Approach: Decentralizes learning, with agents adapting to stakeholder priorities (e.g., MAC’s premium quality, coffee farmers’ margins). Feedback loops reflect local outcomes (e.g., $0.07/unit savings), per Hansen’s commodity focus (2014 post,).
  • Strength: Tailors to diverse needs (e.g., Estée Lauder’s $0.40/unit bioplastics for ESG, Revlon’s $0.22/unit packaging for cost), avoiding one-size-fits-all pitfalls.
  • Weakness: Slower to unify enterprise-wide goals, requiring coordination to scale for giants like L’Oréal (web ID 7).
  • Cosmetics Example: Ensures France’s $110/kg fragrances for Estée Lauder, learning from consumer demand (10–15% sales lift, web ID 1).
  • Coffee Example: Adapts to Guatemala’s $4/kg beans, preserving farmer incomes ($3.20/kg), critical for 25M growers.
• Comparison:
  • Joule: Enterprise-focused, efficient for broad alignment but risks stakeholder disconnect, needing human governance.
  • Hansen: Stakeholder-centric, ideal for commodity nuances but less streamlined for massive firms.
  • Edge: Hansen’s process excels in cosmetics’ brand diversity (luxury, premium, affordable) and coffee’s farmer equity, per his empowerment ethos (2007 post).

4. Human Oversight and Governance

• SAP’s Joule:
  • Role of Humans: Humans validate outputs (e.g., supplier choices), but Joule automates 60–80% of tasks (e.g., invoice analysis), reducing oversight for routine decisions. Gradient descent ensures consistency but risks errors if data is noisy (e.g., tariff mispredictions).
  • Governance: SAP’s cloud security mitigates risks, but external GenAI hosting raises concerns, as Hansen notes (2024 comment,). Humans set strategic goals (e.g., $650M loss recovery, web ID 0).
  • Cosmetics Example: Joule flags Mexico’s $0.25/unit glass, but humans confirm Clinique’s luxury fit.
  • Coffee Example: Suggests $0.10/lb retail hikes, but humans assess consumer impact (5–10% demand dip).
• Hansen’s Learning Loopback:
  • Role of Humans: Central to governance, guiding agents’ priorities (e.g., ethics for coffee farmers, quality for MAC). Feedback loops rely on human-defined success metrics (e.g., cost savings, ESG), per Hansen’s caution against over-automation (2009 post,).
  • Governance: Decentralized agents reduce systemic risks, aligning with Hansen’s cloud-based, open systems (2014 post,). Humans ensure fairness (e.g., Revlon’s $0.02–$0.05/unit caps).
  • Cosmetics Example: Agents learn India’s $2/kg mica saves $1.20/kg, but humans verify ethical sourcing.
  • Coffee Example: Adapts to Ethiopia’s $4/kg, but humans protect smallholder margins ($500–$1,000/year).
• Comparison:
  • Joule: Automates heavily, minimizing human effort but risking oversight gaps, especially in ethics.
  • Hansen: Human-guided, ensuring accountability but slower for routine tasks.
  • Edge: Hansen’s process prioritizes human judgment, critical for tariff-driven ethics (cosmetics’ ESG, coffee’s fairness), per his stakeholder focus (2024 post).

5. Application to 2025 Tariffs

• SAP’s Joule:
  • Performance: Quickly models tariff impacts (e.g., cosmetics’ $0.12/unit China packaging to $0.05–$0.10/unit savings), delivering $200M exemptions for Estée Lauder. In coffee, predicts $700M import hikes, suggesting $0.20/kg savings via Colombia.
  • Limit: May lag if tariffs shift unexpectedly (e.g., 20% on India), needing retraining, risking 5% delays.
  • Example: Revlon’s $0.10–$0.15/unit savings rely on Joule’s analytics, but humans adjust for market fit.
• Hansen’s Learning Loopback:
  • Performance: Flexibly adapts to tariff chaos (e.g., cosmetics’ $1B export redirection to India, coffee’s $300M ASEAN pivot), learning from local outcomes (e.g., $0.07/unit cosmetics savings, $0.10/lb coffee caps). Achieves 5–12% cost control.
  • Limit: Slower to scale for global firms, needing coordination for Estée Lauder’s 150 countries.
  • Example: MAC’s Egypt scents ($70/kg) or Guatemala coffee ($4/kg) reflect iterative learning, human-validated for quality.
• Comparison:
  • Joule: Faster, data-driven for tariff modeling, ideal for large-scale analytics.
  • Hansen: Slower, adaptive for tariff volatility, excelling in stakeholder alignment.
  • Edge: Hansen’s process better handles 2025’s unpredictable trade war (cosmetics’ $650M loss, coffee’s $500M export risks), per his resilience focus (2024 post).

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Conclusion

SAP’s Joule uses explicit feedback loops like gradient descent, optimizing procurement analytics with structured, math-driven precision, ideal for cosmetics’ $1B savings (Estée Lauder) or coffee’s $700M cost mitigation. It automates 60–80% of tasks but risks misalignment in volatile, stakeholder-diverse settings.

Hansen’s inferred learning loopback process, embedded in the Metaprise model, relies on implicit, outcome-driven feedback, adapting flexibly to tariffs (e.g., cosmetics’ $0.05–$0.10/unit savings, coffee’s $0.10–$0.20/lb caps) with human-guided agents tailored to commodities.

While slower to scale, it excels in cosmetics’ brand nuances (luxury, affordability) and coffee’s farmer equity, aligning with Hansen’s decentralized, practical ethos (2007, 2024 posts). Joule suits data-heavy enterprises; Hansen’s process thrives in dynamic trade wars.

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