If your endgame goal is to achieve better margins and faster calls on the floor, the best place to start is by getting everyone aligned on the same truth.
When orders, inventory, inbound POs, and customer value all come from common, authoritative data products, the debates stop and the work speeds up.
That’s the point of using the new SAP BDC Connect for Snowflake: keeping SAP definitions as the backbone, enriching those definitions with what the business already uses, then letting an agent do the heavy lifting while a person stays in the loop.
Most retailers still run a custom ABAP allocator. It pulls the right tables, asks an analyst for rules, and then invites hours of hand edits. It works, but it’s slow and blind to real customer signals (loyalty, LTV, service promises).
With SAP BDC Connect for Snowflake, we keep the same intent—get scarce inventory to the right orders—but we do it on governed products with explainable choices and just one click to post back to SAP.
What Changes in This Model
- Inputs you can trust. Open orders, inventory by location, expected receipts, item/site attributes, calendars, ATP rules—delivered as SAP data products with shared semantics.
- Context you actually use. In Snowflake, we join Salesforce signals like loyalty tier, predicted LTV, service promises, and key-account flags. Now the allocator “knows” who the order is for, not just what and when.
- Optimization that explains itself. We rebuild the warehouse-level agentic allocator in Snowflake. It respects site capacity, carrier cutoffs, channel priorities, and your goals (margin, service level, inventory days). Cortex proposes; the analyst reviews and nudges; an agent posts reservations and confirmations back to SAP under policy.
- Governance and cost control in the flow. Products live in Horizon with owners, lineage, and policy. Dynamic Tables set per-product freshness: fast where operations need it; relaxed where planning is fine. Spend follows those choices.
A Realistic Day in the Life (Three Doors, Many Streams)
This doesn’t run in a straight line. Different teams act at once and stay aligned because all three doors use the same products and policies.
- Fiori (operators): DC teams post goods receipts, see live ATP, manage waves, pick/pack, and transfer orders. Exception tiles flag VIP holds or launch protections.
- SAC (leaders): E-commerce and merchandising watch fill rate, margin lift, and OTIF as they move during the day. Finance tracks cost curves tied to freshness targets and warehouse sizing. Field ops spot stores and sizes at risk of aging.
- Snowflake (analysts): The allocator workspace shows Cortex proposals by warehouse with callouts for at-risk orders, predicted margin impact, split-ship risk, and aging. Salesforce value is in-line. Analysts test small nudges (protect VIPs, cap splits in one region) and recompute. New ASNs or ETA shifts trigger a delta plan that changes only what needs to change. On approval, the agent posts to SAP. Fiori and SAC reflect the new state without side spreadsheets or late-night reconciliation.
- Close of play: Leaders review margin, OTIF, and split-ship % against baseline in SAC. Analysts check the “why” log, tag edge cases for policy tweaks, and adjust tomorrow’s freshness windows. DC views in Fiori show any holds for upcoming events.
Why This Matters
- Better allocations → higher margins. Scarce units land where they do the most good. Aging stock is visible and treated like a constraint, not an afterthought.
- Faster turns. The time from receipt to confirmed shipment compresses—especially on constrained SKUs.
- Less manual work. Analysts supervise trade-offs and exceptions instead of hand-editing lists.
- Lower cost to run. Freshness and compute are tuned where consumption happens; in other words, you pay for what you actually need.
- Audit you can live with. Policy and lineage sit in the catalog. Recommendations include plain-English reasons. You can simulate, approve, post, or roll back with a clear trail.
Rollout that Fits a Real Program
Phase 1: Stand up SAP products for orders, inventory, and POs. Join Salesforce signals. Rebuild the agentic allocator for one DC and one high-velocity category. Keep a human in the loop. Post back to SAP with the agent.
Phase 2: Expand to top DCs and categories. Add store allocation if needed. Introduce target policies like VIP holds or launch-day protections.
Phase 3: Cover the long tail, add seasonal rules, and tune the feedback loop based on margin and service outcomes.
Why the “Power of Two” Makes a Difference for Your Agentic Allocation Use Case
If there’s a consistent through-line between Hakkoda and IBM, it’s that we don’t treat analytics as a tech stunt.
We treat it as a functional enabler, and that’s why the partnership works.
Hakkoda’s SAP analytics practice works in lockstep with IBM’s S/4 functional team. One side translates business rules into data products, policies, and agent workflows. The other keeps those choices true to clean core, Fiori, SAC, and day-to-day operations.
Together, we don’t just wire a plug-and-play connector. We bring allocation, replenishment, and a long list of adjacent S/4 use cases to life on a backbone of shared, authoritative data that improves margins and accelerates decisions.
Ready to get a taste of what that acceleration might look like for your enterprise? Let’s talk.
