Assortment Planning Strategies – Examples and Optimisation

Begin with a structured 12-week pilot that targets the core revenue generators. Identify the top 20% of SKUs by revenue and lock them in as core offers; test two growth sets with complementary styles; prune underperformers weekly. This approach keeps the purchase flow predictable, improves stock turns, and yields measurable financial gains.

Analysing historical data from manufacturers and distributors helps identify signals for demand. Use trained models to forecast demand and assign corresponding stock levels. Combine search signals (views, adds to cart, conversions) with seasonality to refine the assortment.

Create groups by styles and function. For each group, set a target revenue share and a purchase plan, and keep a structured pool to adapt to shifts in demand. The goal is to select a mix that aligns with the desired coverage across categories and channels.

Track performance with a thorough metric set: sell-through, GMROI, stock cover, and revenue per SKU. Maintain a financial perspective and an action calendar, and hold weekly reviews with manufacturers to adjust the assortment while respecting supply constraints.

Example: for an industrial product line, start with a core trio of styles across two price ranges. Create two offers that bundle accessories with purchases. Measure revenue impact and adjust the selection of items in the next cycle to improve margins and meet the desired mix.

Set clear, measurable objectives for assortment decisions

Define three to five measurable objectives for assortment decisions, each with a numeric target, a forecast anchor, and a time frame in months. Tie profitability, stockouts, and the breadth of offering to concrete numbers: for example, reduce stockouts by 12–15% and lift profitability by 6–8% within six months. Involve specialists from merchandising, analytics, and store operations to ensure objectives reflect capabilities and limitations across geographic regions, and note patterns observed from prior cycles to inform broader planning. Document these objectives in a single plan to support integration across teams. This requires much discipline and attention to how each objective interacts with the rest of the portfolio.

Translate objectives into clear rules for the assortment and a routine to monitor progress. Use a dashboard that shows progress for each objective, flags gaps between planned and actual results, and guides decisions on replenishment, pricing, and item removal. Ensure data sources are trusted, dont rely on a single data feed; compare stockouts, units shipped, and margin per unit across channels. Schedule monthly reviews to recalibrate targets and confirm owners and accountability. Furthermore, this cadence supports optimisation across the broader portfolio and helps when market conditions shift.

Table below provides actionable examples and how to track them.

Translate business goals into category-level targets (growth, margin, share)

Define category targets that translate business goals into measurable growth, margin, and share for the upcoming season, and appoint a single owner per category to drive accountability.

For example, jackets: last season revenue 180m, gross margin 32%, share 14%. Target: growth 5–7%, margin 34–36%, share 15–17%. This establishes a certain direction and a baseline for development.

To ensure targets reflect reality, analyze data across departments during a series of meetings. Involve professionals from sales, marketing, product development and finance. sheraz leads the analytics work, ensuring the data between sources stays consistent and comparable.

Process steps:

1. Frame category targets that correspond to the top-level goals: growth, margin, share, with explicit numbers and a given time horizon (e.g., next season).
2. Analyze historical performance and external benchmarks. Use a similar category’s performance as a reference to set realistic levels while pushing for improvement.
3. Set sub-targets by channel, region, and assortment–ensure appropriate granularity. Include jackets, accessories, and other related categories to balance portfolio development.
4. Choose techniques that fit your data and constraints. Combine traditional planning with metaheuristics for optimization, and supplement with scenario analysis to incorporate risk and opportunities.
5. Run collaborative workshops to validate targets and finalize the plan. Use meetings to reach consensus, document assumptions, and commit to implemented actions across departments.
6. Translate targets into a measurement plan. Define KPIs, frequency, data sources, and dashboards to measure performance timely. Ensure the plan is reviewed quarterly and adjusted as needed.
7. Governance and implementation: assign accountability, define owners, and set milestones. Ensure the plan is implemented with alignment across departments and that changes are communicated promptly.

Measurement approaches:

• KPIs: category revenue growth, gross margin, and share; measure progress monthly and compare to targets.
• Performance tracking: analyze deviations between forecast and actual results, identify root causes, and adjust tactics quickly.
• Incorporation of feedback: incorporate learnings from past seasons into new targets to improve accuracy and responsiveness.

Define SKU-level targets (volume, turnover, sell-through)

Set SKU-level targets for volume, turnover, and sell-through for each market and channel for the next quarter. Analyze historical data to understand consumer behavior and what drives purchase decisions. Predict SKU performance by weeks; some SKUs might move faster during march promotions.

Define volume targets by SKU based on similar performance in comparable categories and the data from the last 8–12 weeks. Begin with a conservative baseline and apply a 5–15% uplift for priority markets.

Align profitability targets by SKU with margin expectations, carrying costs and sell-through. Set targets that keep stock within rack limits while maximizing turnover in key markets.

Collaborative planning across channels links selections to march calendars. Involve category managers and frontline teams to decide which selections to push in march across markets and channels.

Implementation steps: assign a right owner for each SKU, set weekly targets, and publish a simple dashboard for teams to follow.

Measurement: follow actual results weekly and compare to forecast; use what-if analyses to predict adjustments and avoid stockouts or excess inventory.

Examples: SKU A: volume target 2,000 units, turnover 1.6x, sell-through 70% within 6 weeks; SKU B: volume target 1,150 units, turnover 1.3x, sell-through 62% within 6 weeks.

Establish service level and availability targets by channel

Set channel-specific service level targets and establish a 60-day review cadence to meet customer expectations and drive improvement across channels. Define clear, measurable targets per touchpoint and anchor them to available capabilities and personnel. For example, aim to respond to live chat within 2 minutes for 90% of inquiries, reply to emails within 4 hours for 80% of messages, have phones answered within 30 seconds for 90% of calls, and respond to social DMs within 1 hour for 85% of cases. Tailor self-service guidance to resolve a majority of common questions within the total volume and lift first-contact experiences.

Determine targets by channel using psychographics and usage data, so you meet expectations while respecting channel capabilities. In an omnichannel approach, align benchmarks with the total experience and with community feedback. This supports continuous improvement and enables departments to coordinate on capacity planning and experience design. Specific targets should be reviewed quarterly and adjusted after each learning cycle.

Anchor availability to capacity by channel and deploy automated routing to maximise personnel utilisation and capabilities. Aim for channel uptime above 99.5% and minimise backlog by aligning staffing with forecasted volumes. Use real-time dashboards to monitor SLA attainment and reviews at weekly cadence to adapt targets as learnings accumulate. Still, guardrails are needed to prevent overcommitment during peak periods.

Coordinate across departments–operations, IT, marketing, and customer success–within a single governance model. Map channel targets to training plans, knowledge base improvements, and cross-channel handoffs. Use psychographics-driven insights to tailor experiences and ensure consistency across the total experience. Document reviews and improvements to capture learnings and drive growth within the community and across the organisation.

Benefits include faster responses, higher first-contact resolution, and experiences that feel seamless across channels. Clear targets reduce redundant work and help minimise costs while boosting retention and growth metrics. Requires ongoing reviews, data quality checks, and alignment with staffing plans and technology investments to maintain service levels and availability. Plan next steps: run a 90-day pilot per channel, finalize baselines, and scale omnichannel capabilities to expand the total service footprint.

Identify constraints and trade-offs (space, budget, lead times)

Recommendation: lock in a precise cap on shelf-space, set a funding envelope, and target predictable lead times. Use a practice that tests SKUs against these conditions with a life-cycle lens, focusing on evergreen items as the core to reduce risk and incurring fewer disruptions in life-cycle planning.

• Space constraints: allocate shelf-space by category with a clear split–evergreen 40–60%, seasonal 20–30%, testing 10–20%. Limit total shelf-face count per display to 20–24 to keep visibility high and perception consistent. These allocations create alignment across store formats and help maintain a visual, easy-to-navigate experience for customers.
• Budget constraints: assign funding so evergreen SKUs receive the majority of budget (60–70%), reserve 15–25% for new tests, and keep 5–15% for promotional activity. This helps reduce over-commitment to low-turn items while maintaining room for agile experiments.
• Lead-time constraints: establish targets by supplier type–fast-moving items: 7–14 days, other SKUs: 21–28 days. Maintain a vetted supplier set with capped maximum lead times to protect service levels, especially during demand spikes.

These settings require disciplined data collection. Capture current shelf-space usage, spend by category, and supplier lead times in a single setting document to enable quick comparisons and traceable decisions. Visual dashboards with color-coded progress make alignment obvious for merchandising, procurement, and finance teams.

Trade-offs and decisions benefit from a structured view. The team should consider less breadth in favor of deeper coverage on high-margin evergreen, while keeping a small, controlled window for testing. These choices seem simple but carry impact on service level, carrying costs, and perception of value among customers and staff.

1. Balance breadth against depth: prioritize top-performing evergreen SKUs for stable shelf presence; reserve limited slots for seasonal or test items to minimize disruption to core assortments.
2. Trade speed for cost: negotiate standard replenishment where possible; where speed is critical, accept a higher unit cost if it reduces stockouts and lost sales.
3. Harmonize with supplier conditions: align lead-time targets with supplier capabilities; adjust forecasts and order quantities to avoid frequent, last-minute expedites.

Implementation requires a practical approach. Use a gams model or a simple Excel-based tool to compare scenarios that vary shelf-space allocation, funding portions, and lead-time buffers. This helps translate these constraints into actionable assortment decisions and provides a clear insight into the impact of each change.

Roles and setting clarify responsibility. Merchandising defines target shelf-space and category priorities; sourcing manages lead times and supplier reliability; finance reviews funding allocation and impact on profitability; analytics monitors forecast accuracy and service levels. By aligning these roles, the team stays focused and agile, with a shared perception of how each constraint shapes the plan.

Experience with similar settings shows that a tight approach to space and lead times, coupled with a disciplined funding plan, reduces stockouts and frees up room for evergreen items that consistently makes life easier for customers and staff alike. Regular reviews provide life-cycle insight, enabling iterative improvements and less ad-hoc changes, while still allowing room to test new concepts in a controlled way.

Create objective cascades across teams and processes

Define one top-level objective for assortment value and cascade it to every team with explicit owners and KPIs. Tie the cascade to cross-category performance and store-level targets so merchandising, planning, and store teams act in sync rather than in silos.

Map each cascade to measurable elements: assortment breadth, lead times, out-of-stock risk, and customer satisfaction. For example, set a target to increase overall sell-through by 5% while holding gross margin steady; assign owners per category and per process.

Create cross-category alignment by tying product-level goals to categories like fashion and basics; ensure equal attention to foundational items and trend-driven SKUs.

Establish governance with disciplines: merchandising, planning, supply chain, store operations, and analytics; hold frequent reviews and quarterly strategy sessions; maintain an up-to-date, reorganized data model.

Implementation blueprint: assign cross-functional owners, define SLAs for data delivery and decision cycles, and use a store-level pilot to validate cascades.

Know your limitations: data gaps, seasonal volatility, and supplier constraints; plan contingencies and maintain steady buffers; use sensitivity analysis.

Measurement framework: track gain in revenue by cross-category, improvement in satisfaction, and reduction in stockouts; analyze significantly which cascades drive the most value.

Development path: run experiments in small, controlled pilots; learn from others and iteratively extend cascades to new products.

Close with a feedback loop: update objectives quarterly; if a cascade underperforms, reorganize responsibilities and reallocate resources to the most impactful products.