Selecting AI Retail Merchandise Planning Software: An Executive Guide

Retail merchandise planning is a complex process that involves forecasting consumer demand, allocating the right products to stores, optimizing inventory levels, and ensuring products are available when and where customers want them. Historically, retailers have relied on teams of planners and analysts to make merchandise planning decisions using a combination of historical data, intuition and manual techniques.

However, the retail landscape is changing rapidly. Customer expectations are higher than ever. E-commerce has exploded. Promotional cadences have accelerated. The proliferation of data has overwhelmed planning teams. As a result, many retailers struggle to accurately forecast demand, leading to stockouts, write-downs, and unsatisfied customers.

In recent years, AI and machine learning technology has emerged as a powerful tool to enhance retail merchandise planning. AI retail planning solutions apply algorithms to large volumes of historical and real-time data to create demand forecasts, optimize inventory allocation, and enable more automated, predictive planning. As AI planning tools become more pervasive, leading retailers are rethinking their core planning processes and strategy.

This guide will discuss the key considerations for retailers exploring AI merchandise planning software. It covers the strategic rationale, business benefits, predictive and generative AI capabilities, and provides a step-by-step guide for evaluating, selecting and implementing the technology.

Strategic Challenges Driving Adoption of AI Planning

Several pressing challenges are motivating retailers to adopt AI planning solutions:

• Forecasting complexity. Traditional statistical forecasting struggles with exponential product assortments, fast-changing trends, short lifecycles, and shifting consumer preferences. AI demand forecasting solutions like OmniThink.AI can consider millions of product and sales variables simultaneously to improve demand sensing.
  
• Inventory performance. Despite large planning teams, many retailers struggle with inventory distortion, stockouts, write-downs and overstocks. AI-based inventory optimization identifies optimal stock levels across distribution networks.
  
• Planning workload. Bloated product assortments and constant new product introductions have overwhelmed planning teams. AI automation provides relief through autonomous forecasting and workflow prioritization.
  
• Merchandising agility. Lengthy planning cycles restrict retailers’ ability to respond quickly to sales trends or competitor actions. AI enables rapid simulations and ‘course correcting’ of plans.
  
• Data silos. Disparate data trapped in siloed technology systems handicaps visibility and hampers the organization. AI digests data from across channels and sources.

The promise of AI planning is elevated forecast accuracy, optimized inventory productivity, and more flexible plans through automation. But harvesting this opportunity requires a strategic implementation approach.

Business Benefits of AI Merchandise Planning

When executed effectively, retailers can achieve robust benefits from AI planning capabilities:

• Reduced inventory. Machine learning algorithms dynamically adjust stock levels based on predictive data, minimizing excess inventory exposure. Less surplus lowers carrying costs.
  
• Fewer stockouts. By considering countless demand drivers, AI forecasting synthesizes demand with greater fidelity. Improved accuracy means having the right products in the right locations.
  
• Optimized promotions. Platforms can run endless simulations to optimize promotional frequency, depth, product selection and placement to lift sales and margins.
  
• Labor efficiency. Automating repetitive forecasting and inventory tasks enables planners to focus on value-added decision making and strategy.
  
• Merchandising agility. Rapid scenario modeling and ‘what-if’ simulations empower retailers to respond quicker to events impacting demand.
  
• Enhanced visibility. Consolidated views of multi-channel product movement, inventory and trends enable smarter executive decisions.

While these benefits are alluring, extracting maximum value requires upfront work examining processes and data readiness. Retailers able to leverage AI planning capabilities as part of a holistic merchandise management strategy are poised to gain considerable competitive advantage.

Overview of AI Techniques in Merchandise Planning

Broadly speaking, AI merchandise planning platforms incorporate two key techniques – predictive AI and generative AI:

Predictive AI

Predictive AI analyzes data to make reasoned assumptions about future outcomes. For merchandise planning, common predictive applications include:

• Demand forecasting. Examines sales histories alongside variables like promotions, pricing, events, cannibalization etc. to predict future demand for optimized inventory.
  
• Size optimization. Forecasts optimal inventory quantity or ‘size curve’ for a product based on projected sales distribution across sizes.
  
• Lifecycle planning. Estimates stage of product lifecycle based on maturity of sales patterns over time to inform forward planning and replenishment timing.
  
• Markdown optimization. Analyzes pricing elasticity and sell-through rates to calculate optimal future markdowns for maximizing sales and margin.
  
• New product forecasting. Evaluates analogous products and sales attributes to predict demand for new products without historical data.
  
• Site selection. Determines optimal new store sites or expansion locations based on predictive sales models for geographical variables.

Generative AI

Generative AI goes beyond predictions to actively create new merchandise plans, products, promotions and experiences. AI models like Large Language Models (LLMs), optimization algorithms and simulations can be applied for use cases like:

• Product design. Leverages LLMs and deep learning on customer data to generate completely new product designs that align with customer preferences. Enables creating thousands of differentiated designs for rapid testing.
  
• Visual merchandising. Uses computer vision and stylistic embeddings to generate optimal merchandise assortments, store layouts and visual displays tailored to location that balance aesthetics, shoppability and compliance.
  
• Promotion optimization. Simulates promotional variable combinations of products, discounts, timing, copy and more to create completely new promotions optimized for ROI.
  
• Customer avatar creation. Analyzes customer segments to develop detailed fictional representations of target customer demographics, psychographics, attitudes, and shopping habits for more refined merchandising.
  
• Virtual shopping experiences. Uses VR and AR to model immersive shopping environments tailored to customer avatars to refine store formats, merchandise assortments, and layouts.
  
• Product recommendations. Generates personalized product recommendations customized to individual customer preferences and shopping history to increase basket size and engagement.
  
• New market expansion. Models competitor, demographic and retail trends data to determine optimal new geographies and formats for expansion, along with tailored merchandise assortments.

These examples demonstrate how predictive and generative AI can work together to automate critical components of retail merchandise planning. When evaluating software, dig deeper into the specific mix of AI techniques leveraged for maximum impact.

Also see our FAQs on Generative AI for retail.

Key Steps for Selecting AI Merchandise Planning Software

Adopting AI planning is not simply about acquiring software – it requires aligning technology capabilities with retailer-specific planning objectives and processes. Follow these key steps for a successful project:

1. Document planning pain points. Conduct stakeholder interviews and document major challenges across the entire retail planning value chain – strategy, forecasting, inventory management, allocation, replenishment, and more. AI opportunities exist across the spectrum.
   
2. Formulate AI planning vision. Given identified challenges, define the ideal future role of AI in your planning process. Set goals for metrics like forecast accuracy, inventory productivity, planner efficiency, and inventory cuts. Envision both incremental AI-augmentation of current processes and transformational disruptions.
   
3. Audit data ecosystem. Inventory historical data sources, quality, accessibility, and governance. AI software is only as good as the data it’s fed. Assess gaps requiring cleansing or new collection. Build required data pipelines and processes.
   
4. Review AI solution capabilities. With goals framed, deeply evaluate vendor solutions against your needs.
   
5. Engineer and test. Once a platform is selected, extensively test AI models on your data to validate performance before scaling implementations. Measure against KPIs and fine-tune algorithms for your unique environment.
   
6. Align processes. AI effectiveness is maximized when paired with redesigned processes tailored to AI strengths. Streamline or eliminate redundant manual steps and build institutional alignment to act on system recommendations.
   
7. Drive adoption. Manage organizational change through training and education to gain user acceptance. Continually demonstrate value delivered and celebrate small wins to reinforce adoption.

Following these steps will help retailers maximize value from AI software investments. But success requires looking at AI planning as an enabler of business strategy, not just a new technology. Retail leaders must set bold visions for using AI-driven insights to enhance their competitive positioning in the market.

Implementation Plan for AI Merchandise Planning

Transitioning to AI is a complex, multi-phase initiative requiring careful change management. Follow this sample plan:

Phase 1 – Merchandise Planning Assessment (8 weeks)

• Document detailed assessment of current planning processes, organization, data and technologies.
• Identify pain points, improvement opportunities, and potential AI applications.
• Quantify expected ROI from AI adoption mapped to business objectives.
• Socialize findings and establish executive alignment on AI vision and goals.

Phase 2 – Proof of Value Pilots (10 weeks)

• Launch pilot AI implementations for 2-3 high-impact planning use cases.
• Ingest required historical data into AI platform.
• Configure, train, test models versus test data sets. Measure against KPIs.
• Operationalize models and compare outputs to existing processes. Track performance lifts.
• Refine algorithms based on learnings. Expand scope.

Phase 3 – Scaled Implementations (6 months)

• Progressively roll out AI capabilities for prioritized planning processes.
• Continuously train algorithms on latest data.
• Transition planners from manual to AI-driven model-based workflows.
• Expand use cases and scope across merchandise hierarchy.
• Drive adoption through training, support, and performance management.

Phase 4 – Process Transformation (6+ months)

• Re-engineer planning processes and organizations around AI to amplify value.
• Change policies, eliminate redundant manual work, refocus roles to leverage AI capabilities.
• Develop additional data pipelines required to feed models.
• Expand use cases to address “buy, move, make” value chain.
• Continual improvement through model refinement, updated data, and feedback loops.

While adopting AI planning brings significant opportunity, it also requires careful change management. Following a structured, phased implementation approach is critical to demonstrate value, build confidence, and transform planning. With the right vision and commitment, AI can transition retailers from gut-feel planning to predictive, insight-driven merchandise management. Those that master AI technology will gain an enduring competitive edge.

Conclusion

AI-powered merchandise planning brings immense potential to address pressing retail challenges. As the technology continues advancing, leading retailers are implementing AI to enhance forecasting, inventory management, and core merchandising decision making. Proactive retailers who formulate an AI strategy, invest in integrated platforms, and transform processes stand to gain lasting competitive advantage through elevated productivity and customer satisfaction. While adopting AI entails risk, the greater risk lies in retaining outdated planning approaches. By following the recommendations in this guide, retail executives can thoughtfully navigate the journey to transformative AI planning.