A sales and inventory forecast program where the CRM pipeline and the stock on hand finally talk.
For distributors, light manufacturers, and multi-SKU wholesalers, the sales forecast and the inventory forecast have to join. Most teams run one in the ERP and one in a spreadsheet. Strkr runs both as a single operational view on the pipeline and the SKU catalog.
A business selling physical goods through B2B channels has two forecasts that need to agree. The sales forecast says "we are closing 1,400 units of SKU 2217 in the next 60 days." The inventory forecast says "we have 820 units on hand with a 42-day lead time to replenish." If these numbers do not line up, the business either ships late and loses the customer or overorders and absorbs carry cost. The common state is that these forecasts live in two systems: the CRM holds the pipeline, the ERP holds the inventory, and the spreadsheet holds the join that nobody fully trusts. Strkr's approach is to run the join as a live operational surface, with pipeline demand per SKU, stock on hand, lead time, and reorder recommendations on one view. The pain points below show up on every call with a distributor or light manufacturer evaluating a replacement for their current spreadsheet-plus-ERP motion.
The CRM and ERP do not join
Pipeline forecast and inventory position live in different systems.
A distributor runs Salesforce or HubSpot for the CRM and NetSuite, Sage, or SAP for the ERP. Each system has its own view of the business. Joining the two requires exports to Excel, a VLOOKUP marathon, and a shared ops person who maintains the stitched view. The sales manager and the ops manager meet weekly to reconcile. Strkr runs the join as a native view with the SKU-level pipeline demand mapped to the live inventory position.
Pipeline deals do not carry SKUs
The opportunity shows a dollar amount, not the units.
In most CRMs the opportunity is a dollar amount and the SKU breakdown lives in the proposal or the order. The sales forecast rolls up revenue, not units. The ops team cannot derive demand per SKU from the pipeline, so they forecast demand from historical shipment patterns which miss the pipeline signal. Strkr runs SKU-level pipeline with units per deal so the demand forecast reads the pipeline directly.
Lead time variability
A 42-day lead time averages 42 days and swings 25.
Vendor lead times vary. The posted 42-day lead time is a mean; actual deliveries can range from 28 to 65 days per supplier. Static inventory forecasts assume the mean and lose when the swing hits. Strkr tracks lead-time distribution per supplier per SKU and applies a configurable safety-stock buffer that respects the actual variability, which materially reduces stockout risk versus a mean-based reorder trigger.
Backlog and backorder visibility
A rep sells 300 units; 220 are already allocated.
A rep working a deal does not see current backlog or backorder allocation against the SKU. They sell 300 units, the ops team receives the order, and discovers 220 of the available 400 units are already allocated to other deals. The ship date gets pushed, the customer is unhappy, the sales manager hears about it on Friday. Strkr surfaces allocatable inventory on the opportunity so reps commit against reality.
Seasonality blind spots
The Q4 ramp is predictable; the forecast never catches it.
Many distributors see seasonal patterns (Q4 heavy for consumer-adjacent SKUs, Q2 heavy for construction-adjacent SKUs). The historical ERP forecast captures the pattern coarsely; the pipeline-driven forecast captures the deal-specific signal. Combining the two requires modeling work most teams defer. Strkr applies seasonality adjustments on top of the pipeline demand so the forecast catches both the deal-driven and the pattern-driven demand.
Reorder decisions are gut
The ops manager places the order from memory.
When current inventory crosses the reorder threshold, someone decides how much to order. In most teams this decision is heuristic (the ops manager knows the business) and the heuristic degrades when the pipeline shifts under them. Strkr's reorder recommendations join pipeline demand plus trailing shipment velocity plus lead-time variability plus economic order quantity math into a recommendation the ops manager can accept, adjust, or reject with reasoning captured.
What the Strkr sales-plus-inventory program ships
The joined operational view that makes the forecast work.
A sales and inventory forecast program needs specific capabilities to earn its line on the invoice. The cards below are the jobs the Strkr forecast surface runs for distributors, light manufacturers, and multi-SKU operators.
SKU-level pipeline
Opportunities carry units per SKU, not just a dollar amount.
Each opportunity carries SKU-level line items with units, unit price, bundle math, and expected ship date. The pipeline rolls up to revenue (for the sales forecast) and to units per SKU (for the demand forecast). The sales manager and the ops manager read the same data for their respective views.
Live inventory position
Stock on hand, allocated, in transit, backlog per SKU.
The inventory position syncs from the ERP (NetSuite, Sage, SAP, QuickBooks Enterprise) in real time or near-real time. Strkr shows stock on hand, allocated to open orders, in transit, and backlog per SKU with the aging on each bucket. The sales manager sees the picture a rep should see before committing a ship date on a new deal.
Allocatable-inventory check at commit
Reps see what they can actually promise.
When a rep enters SKU and units on an opportunity, Strkr checks allocatable inventory in real time (stock on hand minus already-allocated minus safety-stock buffer plus confirmed inbound). The rep sees the number they can commit for a given ship date without over-promising. The ops team stops absorbing the surprise.
Lead-time distribution per supplier
Not just a mean; the swing matters.
Each SKU-supplier pair carries a lead-time distribution (mean, standard deviation, trailing 12-month actuals). The reorder recommendation applies a configurable service-level safety-stock buffer (95 percent service level, 98 percent service level, specific per-SKU targets) which respects the actual variability instead of the posted mean lead time.
Reorder recommendation
Specific suggested order quantities with the math shown.
When current inventory crosses the reorder threshold, Strkr generates a recommended order with the math visible: pipeline demand over the lead time, trailing shipment velocity, safety-stock buffer, economic order quantity considering the carrying cost and the ordering cost. The ops manager accepts, adjusts, or rejects with reasoning captured in the audit log.
Seasonality overlay
Pattern-driven demand adjustment on the pipeline forecast.
The forecast applies a configurable seasonality overlay to the pipeline-driven demand. For SKUs with strong seasonal patterns (Q4-heavy consumer SKUs, Q2-heavy construction SKUs), the overlay catches the pattern the deal-level signal misses. The admin configures the pattern per SKU or per SKU group, and the overlay respects historical actuals.
Backorder management
When demand exceeds supply, backorder with ship-date commit.
For situations where committed demand exceeds the allocatable inventory even after the pending reorder, Strkr manages backorders with ship-date commits based on the current inbound schedule. Reps can commit to a future ship date with buyer acceptance captured on the opportunity. Backorder aging surfaces on the sales manager dashboard so no backorder stays stale.
Strkr AI demand narrative
The weekly inventory story writes itself.
Each Friday Strkr AI drafts the inventory narrative paragraph: SKUs at risk of stockout, SKUs with surplus, specific pipeline deals driving the demand swing, specific reorder recommendations ready for approval. The ops manager edits in 10 minutes instead of writing from scratch on Sunday. The ops leader team reads the same shape every week.
Audit trail on commits
Every ship-date commit logged with reasoning.
For regulated or audit-conscious businesses (medical device, aerospace, defense), every ship-date commit, every reorder decision, every allocation change logs with the actor, the timestamp, the reasoning. The audit trail satisfies the "why did we promise this ship date" question that comes up in quarterly reviews. SOC 2 Type II in progress through 2026-Q4.
Where this program fits in the stack
The operational role Strkr plays.
A sales and inventory forecast program is one surface in a broader distribution stack. Strkr's role is specific: run the join between CRM pipeline and inventory signal as a live operational view. The cards below describe the stack integration and what each system still owns.
ERP stays the ERP
NetSuite, Sage, SAP keep inventory, AP, AR.
The ERP continues to own the authoritative inventory count, the AP and AR ledgers, the general ledger, the manufacturing BOM (if applicable), and the warehouse management specifics. Strkr reads the inventory state and writes the order and shipment back. The ERP is not replaced; it is paired with a sales-and-inventory surface the ERP's native UI typically cannot deliver.
CRM pipeline on Strkr
Opportunity, forecast, activity on Strkr.
Strkr runs the full CRM functionality (opportunity management, forecast, activity, dashboards, account health, lead-to-opportunity motion). The SKU-level opportunity data flows into the inventory forecast and the demand picture. Teams moving off HubSpot or Salesforce for the CRM side typically consolidate to Strkr for both the CRM and the inventory-forecast surface in one subscription.
WMS stays the WMS
Warehouse management specifics stay specialized.
For teams running a dedicated WMS (3PL Central, SnapFulfil, Softeon, Manhattan), the WMS continues to own pick, pack, ship, bin location, and warehouse labor management. Strkr integrates with the main WMS tools via API; the integration passes the inventory position and the shipment commits back and forth. The specialty WMS layer is not touched.
EDI and vendor portals
Vendor-specific EDI integrations stay with the specialist.
For teams with heavy EDI traffic (specific trading partner formats, X12, EDIFACT, retailer-specific portals), the specialty EDI translator (SPS Commerce, TrueCommerce, DiCentral) stays in place. Strkr consumes the EDI-translated order feed and feeds the shipment-side data back. We do not try to be an EDI translator.
Finance planning in finance tools
Mosaic, Pigment, Anaplan for the strategic forecast.
The strategic financial plan (cash flow, carrying cost analysis, inventory investment versus service level trade-offs at the annual-planning scale) stays in the finance planning tool. Strkr feeds the operational demand and inventory data; the strategic analysis layer sits on top. The operational forecast surface and the strategic plan serve different purposes and the specialist tool for each stays appropriate.
Vertical integrations by API
REST and webhook for specialty tools.
For the long tail of distribution-specific tools (specific inventory optimization software, forecast analytics specialists like NetStock, replenishment engines like E2open), Strkr exposes REST + GraphQL API, outbound webhooks with HMAC signing, and OpenAPI spec. A buyer with a strong admin or an integration partner wires the tool in. We do not pretend to ship a native connector for every niche distribution tool.
What this program is not
the limits.
Strkr's sales-and-inventory forecast surface is scoped intentionally. The limits below show where the specialist tools still win and where we recommend running Strkr alongside the specialist.
Not an ERP
We do not run AP, AR, GL, or the ledger.
Accounts payable, accounts receivable, general ledger, financial close, tax reporting, cost accounting, manufacturing BOM, specific warehouse labor management, pick and pack workflows. All of these stay in the ERP. Strkr is the sales-and-forecast layer that reads the ERP data and feeds back order and shipment commits. We are explicit about this scope.
Not a MRP
Manufacturing resource planning is a different problem.
For discrete manufacturers running a true MRP (material requirements planning with multi-level BOM explosion, work order scheduling, capacity planning across production lines), the specialty MRP tools (Katana, Fishbowl, Fulfil.io, enterprise MRP in the main ERPs) still own the manufacturing-planning layer. Strkr handles finished-goods demand forecasting and inventory position; it does not try to replace MRP.
Not forecast analytics at NetStock depth
Specialty inventory optimization tools still win on depth.
For deep inventory optimization (multi-echelon planning, specific demand-sensing algorithms, machine-learned reorder optimization across thousands of SKUs), the specialty tools (NetStock, StockIQ, SmartSoft) carry depth Strkr does not match. For teams running those tools, Strkr integrates and provides the pipeline-side signal that improves the specialty tool's accuracy. For teams without a specialty tool, Strkr's native capabilities handle the mid-market case cleanly.
Not an e-commerce storefront
Shopify, BigCommerce, Magento stay the storefront.
For distributors or wholesalers with a B2B e-commerce storefront (Shopify B2B, BigCommerce B2B Edition, custom Magento) the storefront continues to own the buyer-facing ordering surface. Strkr integrates with the main storefronts via API, consuming the orders and feeding the inventory position and ship-date commits back. The storefront is not replaced.
No specific EDI translator
EDI formats stay with the EDI specialist.
As noted, EDI translation stays with the specialist (SPS Commerce, TrueCommerce). For retailer-specific portals (Walmart Retail Link, Target Partners Online, specific Home Depot or Lowes workflows), the specialist handles the portal integration and feeds Strkr. We consume the translated feed; we do not translate.
Not a specialty quality system
ISO quality workflows stay with the QMS.
For regulated manufacturers running ISO 9001 or AS9100 quality management systems with specific CAPA (corrective and preventive action) workflows, document control, and training management, the specialty QMS tools (MasterControl, ETQ, Greenlight Guru) continue to own the quality layer. Strkr handles the sales and inventory operations; the quality system layer is adjacent, not inside.
Head-to-head
Strkr sales-plus-inventory forecast vs ERP-plus-spreadsheet.
Here is the practical head-to-head for a 25-rep distributor running NetSuite ERP plus HubSpot CRM plus a shared Google Sheet versus moving the sales-and-inventory forecast motion to Strkr.
Feature
Strkr
NetSuite + HubSpot + Google Sheet join
SKU-level pipeline
Native line items with units per deal
Dollar amount only; SKU detail in quotes
Live inventory on opportunity
Real-time check at commit time
Manual lookup to NetSuite
Allocatable-inventory enforcement
Reps see what they can promise
Ship-date surprises on operational handoff
Lead-time distribution
Distribution per SKU per supplier
Posted mean lead time only
Reorder recommendation
EOQ math with pipeline demand and safety stock
Heuristic decision by ops manager
Seasonality overlay
Pattern plus pipeline joined
Separate pattern analysis in Excel
Backorder management
Ship-date commits with buyer acceptance
Email thread workflow
AI demand narrative
Strkr AI weekly inventory paragraph
Manual write-up by ops lead
Audit trail on commits
Every commit and reorder logged
Reconstruction from emails
All-in annual cost for 25-rep distributor
Flat per-seat with forecast surface included
NetSuite + HubSpot + spreadsheet labor cost
How teams use Strkr
How distributors run the Strkr forecast program today.
The teams moving to Strkr for the sales-and-inventory forecast motion share a trigger: a quarter with ship-date promises missed or a quarter with overordering that absorbed carry cost. The playbooks below are real customer motions.
Industrial distributor, 85 reps, 2800 SKUs
Stockout rate dropped from 11 percent to under 3.
An industrial distributor with 85 reps and 2800 SKUs ran NetSuite plus Salesforce plus a shared Google Sheet reconciliation. Stockout rate on their 300 highest-velocity SKUs was running 11 percent monthly, costing roughly 420K in avoidable backorder margin and expedite freight. Strkr's joined forecast with lead-time distribution and pipeline demand lowered the stockout rate to under 3 percent in the first 6 months. The avoidable-cost savings paid back the Strkr investment in roughly 4 months.
A specialty food distributor with 18 reps and heavy Q4 seasonality (holiday-related SKUs) had historically ordered the Q4 ramp from last-year patterns, which worked but often undercalled or overcalled by roughly 15 percent. Strkr's seasonality overlay plus live pipeline demand surfaced the Q4 pattern 6 weeks earlier with higher accuracy. The ops team placed reorders earlier, avoided the usual Q4 expedite freight line, and the inventory investment efficiency improved by roughly 22 percent year over year on the same revenue base.
A 12-rep light manufacturer had chronic ship-date disputes between sales and operations, usually caused by reps committing against inventory the ops team had already allocated. The Strkr allocatable-inventory check at commit time stopped the over-commits. Ship-date dispute incidents dropped from roughly 14 a month to under 2. The sales-ops Friday meeting that had been running 90 minutes to reconcile compressed to 15 minutes focused on actual exceptions, and the sales-manager relationship with the ops manager materially improved.
Medical device distributor, 22 reps, regulated SKUs
Audit trail satisfied the FDA inspection.
A medical device distributor with 22 reps and FDA-regulated SKUs needed audit trails on every ship-date commit, every reorder decision, every allocation change. The old motion maintained the audit evidence manually. Strkr's audit log captured every commit, reorder, and allocation event with actor, timestamp, and reasoning. The subsequent FDA inspection pulled the Strkr audit log directly and finished the inventory-and-shipment portion in 2 hours instead of the usual 2 days of email forensics. HIPAA compliant with BAA available for the regulated handling.
A wholesale apparel distributor with 14 reps and seasonal catalogs (two main seasons plus holiday) needed to forecast demand for pre-season buying 6 to 9 months out. The sales team ran pre-season meetings with buyers, captured intent in Strkr opportunities with SKU-level units, and the demand forecast rolled up the committed-intent numbers plus historical sell-through adjustment. Pre-season buying accuracy improved by roughly 18 percent year over year, which cut the end-of-season markdown rate by roughly 7 percentage points and recovered meaningful gross margin.
A forecast program where sales and inventory stop drifting.
Start a 14-day trial with your CRM pipeline loaded and your ERP inventory position synced. Watch the sales-and-inventory forecast surface deliver the join your spreadsheet has been maintaining by hand.
How does the integration with NetSuite or Sage actually work?
The ERP integration uses the ERP's native REST API where available (NetSuite's REST API, Sage Intacct's API). For older ERPs with SOAP or XML-based APIs, we use a middleware adapter that normalizes to the Strkr sync format. Inventory position syncs on a configurable cadence (near-real-time for high-velocity SKUs, every 15 minutes for standard, hourly for low-velocity). Order writes flow from Strkr to the ERP in real time at commit. For teams on legacy ERPs without API access, a nightly file-based sync is available as a fallback with the obvious freshness tradeoff.
What about teams with very long product lifecycles (durable goods, specialty equipment)?
For businesses with long product lifecycles (specialty equipment with 2 to 5 year sales cycles, durable goods with multi-year replacement cadences), the pipeline-driven demand forecast is more valuable than the historical shipment pattern (which carries fewer data points per cycle). Strkr's pipeline-plus-seasonality approach is particularly well-suited to this motion because the deal-level signal carries most of the forecast weight. The configuration during onboarding adjusts the overlay weighting to reflect the business's specific lifecycle characteristics.
Can we run multi-location inventory with location-specific allocation?
Yes. Strkr supports multi-location inventory with per-location stock on hand, per-location allocation, and location-aware ship-date commits. The reorder recommendations respect per-location demand patterns and vendor lead times to specific receiving locations. For teams with 10 or more distinct locations, the configuration takes a bit longer during onboarding (typically 2 to 3 additional weeks for location-specific rules) but the ongoing operational benefit is material because the allocation decisions get made closer to the actual demand point.
How does the demand narrative compare to specialty tools like NetStock or StockIQ?
NetStock and StockIQ carry deeper inventory optimization math (multi-echelon planning, specific demand-sensing algorithms, machine-learned reorder optimization across thousands of SKUs). Strkr's narrative generation is operational: the weekly inventory paragraph summarizes what moved, flags the risks, and recommends specific reorders ready for approval. For teams running NetStock or similar for the deep optimization, Strkr integrates and provides the pipeline-side signal that improves the specialty tool's accuracy. For teams without the specialty tool, Strkr's native capabilities handle the mid-market case cleanly without the second subscription.
What about businesses with mix of make-to-stock and make-to-order?
The forecast surface handles mixed-mode businesses by treating make-to-stock SKUs as inventory-tracked (with the full stock-on-hand plus allocation plus reorder machinery) and make-to-order SKUs as pipeline-driven (with the production lead time applied to the ship-date commit). The admin configures the mode per SKU during catalog setup. For light manufacturers running both modes across their catalog, the mixed configuration is the common case and the workflow handles both cleanly.
How does Strkr handle vendor-managed inventory or consignment stock?
For vendor-managed inventory (vendor owns the stock until pulled) and consignment stock (customer holds stock as inventory until consumed), the inventory position tracks these as separate buckets from owned on-hand inventory. The reorder trigger for VMI SKUs runs against the vendor's commitments; the consumption-tracking for consignment SKUs flows through the customer-usage data and triggers vendor billing per the consignment agreement. These configurations require specific setup during onboarding and are common patterns for teams in industrial distribution and medical device distribution.
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