Grocery · Assortment · SAP · Director Store Ops

Assortment Planning + SAP + Grocery Retail: Built for Director Store Ops

Grocery operators find Assortment problems in post-mortems and quarterly reviews. Ward catches them daily — with root causes and recommended actions. Your Store Operations team has the data. What they don't have is bandwidth to find what's buried in it.

What is Assortment Planning for Grocery & Supermarket?

Assortment Planning is the process of ward analyzes sell-through by store cluster to recommend which skus to add, drop, or reallocate.

For Grocery & Supermarket retailers specifically, this means monitoring 30,000+ SKUs across stores. Fresh availability, shrinkage, and promo effectiveness across hundreds of stores. Ward monitors perishable turn rates and flags waste before it happens.

How Ward delivers Assortment insight cards: Ward clusters stores by demographic, traffic, and sales patterns, then measures SKU performance against cluster benchmarks.

Key capabilities

  • Store cluster segmentation
  • SKU rationalization recommendations
  • Whitespace opportunity detection
  • Planogram optimization inputs
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Live product demo — Ward analyzing retail data in real time.

Why Assortment matters for Grocery retail

Every assortment addition displaces something else, so the real question is incremental contribution after cannibalization and basket effects. Ward clusters stores by demographics, traffic, and competitive landscape, then benchmarks SKU performance at the cluster level to produce assortment recommendations that go beyond national planograms.

How Ward connects to SAP Retail

Ward connects to SAP Retail (S/4HANA, ECC, CAR) via standard BAPIs and IDocs. Transaction data, inventory positions, and master data flow into Ward without custom development.

Setup: Ward reads from SAP via RFC/BAPI or OData APIs. No changes to your SAP configuration. Read-only access. Data syncs on your schedule.

Data Ward reads from SAP

POS transactions
Inventory positions
Purchase orders
Material master
Vendor master
Promotion calendar

Impact metrics with SAP

Replenishment Accuracy
Gaps flagged early
Inventory positions matched to POS velocity before shelf impact.
Shrinkage
Anomalies surfaced continuously
Transaction-level detection catches what periodic audits miss.
Forecast Accuracy
External signals layered
Weather, events, and competitor data sharpen SAP demand signals.
Promo ROI
True lift isolated
Sell-through data exposes cannibalization and halo effects.

Data lake enrichment

Ward enriches SAP data with: POS transactions, Weather & events, Competitor pricing, Loyalty & CRM, Supplier fill rates

Managing 800 stores from a spreadsheet is insane.

Pain points
  • ×Morning check-ins rely on phone calls and email chains
  • ×No single view of which stores need attention today
  • ×Labor scheduling is disconnected from demand signals
  • ×Planogram compliance is checked manually, quarterly
  • ×Exception management is reactive and inconsistent
How Ward helps
  • Morning brief delivered at 06:47 with prioritized action list
  • Estate-wide heat map of store performance, updated hourly
  • Staffing recommendations correlated with predicted traffic
  • Planogram compliance anomalies detected and flagged
  • Consistent exception handling with recommended actions

Poor labor allocation and inconsistent execution cost multi-store retailers 3–5% in lost sales. — RSR Research

Category review, natural/organic section

A category manager reviews the natural/organic section across 300 stores. Ward's analysis reveals three distinct clusters: urban health-conscious stores that should carry more SKUs, suburban stores aligned with the national plan, and rural locations where organic moves at a fraction of the estate average. The one-size-fits-all planogram is leaving revenue on the table in urban stores while tying up slow-moving inventory in rural ones.

What a Ward insight card looks like

Ward · Grocery · Assortment06:47 AM

Cluster B stores (urban, high-traffic) underperforming on premium snacks vs Cluster A by 34%. Assortment gap: 12 SKUs missing.

✓ Action recommendedGrocery context appliedSAP data

Grocery KPI impact

Shrinkage
Cause-level attribution
Loss prevention shifts from guesswork to targeted intervention.
Fill Rate
24–72hr head start
Stockout prediction cards arrive before customers notice gaps.
Fresh Waste
Flagged before spoilage
Perishable turn rates monitored by store.
Promo ROI
Net lift, not gross
True lift net of cannibalization and pull-forward.

Frequently asked questions

Ward analyzes sell-through by store cluster to recommend which SKUs to add, drop, or reallocate. For Grocery retail specifically, Ward monitors 30,000+ SKUs across your stores and delivers automated insight cards with root cause analysis and recommended actions.

Ward tracks Fill rate, Shrinkage %, Fresh waste %, Promo lift, Basket size at the store-category level. Ward clusters stores by demographic, traffic, and sales patterns, then measures SKU performance against cluster benchmarks.

Ward reads from SAP via RFC/BAPI or OData APIs. No changes to your SAP configuration. Read-only access. Data syncs on your schedule. Data points include: POS transactions, Inventory positions, Purchase orders, Material master, Vendor master, Promotion calendar.

Yes. Ward reads SAP data and combines it with contextual signals (weather, events, demographics) to generate Grocery-specific insight cards. No custom development required.

Managing 800 stores from a spreadsheet is insane. Ward solves this with automated insight cards: Morning brief delivered at 06:47 with prioritized action list. Estate-wide heat map of store performance, updated hourly. Staffing recommendations correlated with predicted traffic.

Ward delivers daily insight cards covering Fill rate, Shrinkage %, Fresh waste % — tailored for Store Operations decision-making. Each card includes what changed, why it matters, and what to do next.

Ward tracks SKU productivity (revenue per facing), incremental contribution, substitution patterns, and cluster-level demand elasticity — all weighted against supplier fill rates and promotional obligations.

A category manager reviews the natural/organic section across 300 stores. Ward's analysis reveals three distinct clusters: urban health-conscious stores that should carry more SKUs, suburban stores aligned with the national plan, and rural locations where organic moves at a fraction of the estate average. The one-size-fits-all planogram is leaving revenue on the table in urban stores while tying up slow-moving inventory in rural ones.

First insight cards arrive within 48 hours of data connection. Ward needs approximately 2 weeks to establish robust baselines for your specific operation.

No. Ward sits on top of your existing stack. It is the proactive intelligence layer that watches your data continuously and delivers insight cards — so your team acts on findings instead of hunting for them.

Ward
Insight
Dispatch
Feedback
Evaluate
Learn
01

Insights surface

Ward’s agents detect what changed, why it matters, and what to do about it. Every insight includes a recommended action—not just a chart to interpret.

Real-time detection Root cause + recommendation
02

Insights become actions

Any insight card can be turned into a tracked ticket or task. Dispatched to the right person, on the right channel—mobile push, text, or email. Not every insight needs a ticket. But when one does, it has an owner.

Tickets created automatically Dispatched to the right person
03

Your team responds

Insights get voted up or down with reasoning. Tickets get completed or rejected. Every response is a signal—Ward learns what worked, what missed, and why.

Vote up / down Ticket completed Reasoning attached
04

Outcomes measured

Ward evaluates real results: revenue, margin, fill rate, labor cost. Did the action actually improve the number it targeted? Measured outcomes, not assumptions.

KPI impact tracked Results vs. prediction scored
05

Agents get sharper

Every vote, every completed ticket, every measured outcome feeds back in. Ward learns from your team’s judgment and real-world results. Each cycle sharpens the next. Then it starts again.

Cycle repeats, sharper each time
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See what Grocery assortment problems Ward catches.

Root causes, not just alerts. See it on your data.

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Find out what your data has been hiding.

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