Automated replenishment workflow in inventory optimization reduces stockouts by 40%

SCM Infor SCM
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1

Sharing our success story implementing automated replenishment workflows in Infor SCM’s inventory optimization module. We’ve reduced stockouts by 40% over six months while actually decreasing inventory carrying costs by 12%.

The key was configuring smart replenishment triggers based on demand velocity rather than just static reorder points. Our inventory threshold configuration now considers seasonal patterns, lead time variability, and supplier reliability. The procurement integration automatically generates purchase requisitions when triggers fire, eliminating the manual review bottleneck that used to delay replenishment by 2-3 days.

Happy to share details about our configuration approach and the business impact we’ve achieved.

2

The procurement integration piece is what we’re missing. Right now our inventory system flags low stock, but buyers still have to manually create POs. How did you handle the automatic requisition generation? Do you have approval workflows built in, or does it go straight to PO creation for certain item categories?

3

Lead time variability is critical and we do account for it. We calculate safety stock using the standard deviation of both demand and lead time. For your 5-15 day scenario, the system would use the 90th percentile lead time (probably around 13 days) for safety stock calculation rather than the average. We also track supplier on-time delivery performance and automatically increase safety stock for unreliable suppliers. This is all configured in the replenishment trigger rules.

4

Absolutely, let me provide comprehensive details on our implementation addressing all three critical components that drove our success.

Replenishment Triggers - The Foundation:

We moved from static reorder points to dynamic trigger rules based on multiple factors:

Demand Velocity Triggers:

  • Fast movers (A items, >100 units/month): Trigger when inventory drops below 15 days of forecasted demand
  • Medium movers (B items, 20-100 units/month): Trigger at 25 days of forecasted demand
  • Slow movers (C items, <20 units/month): Trigger at 45 days of forecasted demand
  • New products: Conservative 60-day trigger until 6 months of demand history established

Seasonal Adjustment Factors:

  • System compares current period to same period last year
  • Applies multiplier to base triggers: 1.5x for peak season, 0.7x for slow season
  • Gradual ramp-up starting 45 days before historical peak periods
  • Automatic de-stocking triggers 30 days after peak season ends

Lead Time Variability Protection:

  • Calculate safety stock as: (Z-score × √(avg lead time × demand variance + avg demand² × lead time variance))
  • We use Z-score of 1.65 for 95% service level on A items, 1.28 for B items (90% service level)
  • System recalculates safety stock weekly based on rolling 90-day performance

Supplier Reliability Integration:

  • Track on-time delivery % by supplier over last 6 months
  • Suppliers <85% on-time get 20% safety stock increase
  • Suppliers >95% on-time get 10% safety stock decrease
  • New suppliers default to highest safety stock tier until proven reliable

Inventory Threshold Configuration - The Intelligence Layer:

Our threshold configuration is where the real optimization happens:

Multi-Tier Threshold Structure:

  • Green Zone (>30 days supply): Normal operations, no action
  • Yellow Zone (15-30 days supply): Monitor closely, pre-alert procurement
  • Orange Zone (7-14 days supply): Trigger standard replenishment workflow
  • Red Zone (<7 days supply): Trigger expedited replenishment, escalate to manager
  • Critical Zone (<3 days supply): Emergency procurement, consider air freight

Dynamic Threshold Adjustment:

  • System analyzes stockout history by item
  • Items with >2 stockouts in last 6 months get 25% threshold increase
  • Items with zero stockouts and excess inventory get 15% threshold decrease
  • Quarterly review cycle with buyer approval for threshold changes

Product Lifecycle Considerations:

  • New product launch: Conservative thresholds (60-day supply) for first 90 days
  • Growth phase: Aggressive thresholds (20-day supply) with frequent replenishment
  • Mature phase: Optimized thresholds based on demand stability
  • Phase-out: Gradual threshold reduction to minimize obsolescence

Warehouse Capacity Constraints:

  • Maximum inventory levels by storage location
  • Prevents over-ordering of bulky items that consume warehouse space
  • Balances replenishment frequency against receiving capacity
  • Coordinates replenishment timing across product families

Procurement Integration - The Execution Engine:

Seamless procurement integration was essential to eliminate the 2-3 day manual delay:

Automated Requisition Generation:

  • Workflow triggers create requisitions automatically when orange zone reached
  • System calculates order quantity using Economic Order Quantity (EOQ) model adjusted for:
    • Current inventory position
    • In-transit inventory
    • Existing open purchase orders
    • Forecasted demand through lead time + safety stock period
  • Requisitions include all necessary details: supplier, unit cost, delivery date, GL account

Tiered Approval Workflows:

  • Tier 1 (<$5K, approved supplier, standard items): Auto-approve to PO, no human intervention
  • Tier 2 ($5K-$25K or new supplier): Route to buyer for review, 4-hour SLA
  • Tier 3 (>$25K or critical items): Buyer + Manager approval, 8-hour SLA
  • Emergency requisitions (red/critical zone): Expedited approval path, manager notified immediately

Supplier Communication Automation:

  • POs transmitted automatically via EDI for integrated suppliers (60% of our volume)
  • Email POs with acknowledgment request for remaining suppliers
  • Automatic follow-up if no acknowledgment received within 24 hours
  • Delivery date confirmation triggers automatic update to expected receipt date

Exception Handling:

  • Supplier out-of-stock response triggers alternative supplier workflow
  • Price variance >10% from standard cost flags for buyer review
  • Lead time extension triggers safety stock recalculation and potential second source
  • Quality issues with supplier trigger hold on auto-approval for that supplier

Implementation Results and Key Success Factors:

Our 40% stockout reduction broke down as:

  • 25% from dynamic replenishment triggers responding faster to demand changes
  • 10% from improved lead time and supplier reliability modeling
  • 5% from elimination of manual procurement delays

The 12% inventory carrying cost reduction came from:

  • 8% from optimized safety stock levels (not over-protecting)
  • 4% from better alignment of order quantities with actual demand patterns

Key success factors that made this work:

  1. Data quality foundation - cleaned up item master, supplier master, and 2 years of transaction history before go-live
  2. Phased rollout - started with 200 A items, validated for 60 days, then expanded to B items, finally C items
  3. Change management - trained buyers on monitoring dashboards and exception management rather than order creation
  4. Continuous improvement - monthly review of stockout incidents and threshold adjustment recommendations
  5. Executive sponsorship - VP of Operations championed the project and held teams accountable to using the system

The configuration took 3 months to implement and tune, but the ROI was evident within the first 90 days. We’re now expanding the same approach to our distribution center replenishment workflows with similar expected results.

Happy to answer specific technical questions about the configuration parameters or share our lessons learned from the implementation process.

5

How did you handle the inventory threshold configuration for items with high lead time variability? We have suppliers where delivery can range from 5 to 15 days, and it’s hard to set appropriate safety stock levels. Do you factor supplier reliability into your automated triggers?

6

Could you share more specifics about your configuration? I’m helping a client implement similar automation and your 40% stockout reduction is exactly the kind of ROI they’re looking for. What were the key configuration parameters that made the biggest impact?

7

Great questions. For demand velocity, we’re using Infor’s adaptive forecasting with custom weighting factors we tuned based on our product categories. Fast-moving items use a 30-day rolling average, while seasonal items use year-over-year comparison with the same period. The system adjusts reorder points dynamically every week.

For procurement integration, we implemented tiered approval workflows. Items under $5K and from approved suppliers go straight to PO. Items $5K-$25K need buyer approval. Anything over $25K requires manager sign-off. This automation handles about 70% of our replenishment volume without human intervention.

8

Those are impressive results! I’m particularly interested in how you configured the demand velocity triggers. Are you using Infor’s built-in forecasting algorithms or did you implement custom logic? We’re struggling with seasonal products where static reorder points cause either stockouts during peak season or excess inventory during slow periods.