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The first steps on your AI inventory optimisation journey

Tom Nicholls, Chief Commercial Officer at Datasparq

It'll come as no surprise that AI is changing the role of inventory specialists. The good news is that having both an operational background and commercial background positions those working in inventory roles perfectly to champion its adoption.

Based on what I've experienced at Datasparq, I jotted down a few simple things to help you get started. First, I explore the signs that AI could be helpful, then I identify the data you’ll need to get started, before offering a step-by-step breakdown of how to get started. Read on to see whether anything resonates with you.

Signs that AI might work for you

  • You frequently experience stockouts or excess inventory: If you struggle to maintain optimal stock levels, AI demand forecasting can predict peaks and valleys, preventing stockouts and the costs of holding excess inventory.
  • You rely on inaccurate manual processes: Managing your business in Excel is time-consuming and prone to errors. AI can free up your team’s time and improve accuracy.
  • Your market has hard-to-predict demand patterns: If your demand fluctuates due to seasonality, promotions, or external factors, AI can analyse these trends and adjust forecasts accordingly.

The data you’ll need (not exhaustive, not all necessary)

  • Historical sales data: This is the foundation. It includes past sales figures, order patterns and customer behaviour.
  • Product information: Details like size, weight, and seasonality help AI categorise and predict demand for specific products.
  • Warehouse and supplier data: Lead times, storage capacity and supplier reliability all factor into AI's optimisation engine.

A few steps to get started

  1. Define your goals: What specific inventory challenges do you want AI to address? Reduced stockouts? Lower storage costs? Improved order fulfilment times? Clarity helps choose the right AI solution.
  2. Identify data sources: Pinpoint where the data you need resides within your existing systems (ERP, CRM, warehouse management software). Ensure data quality and clean up any inconsistencies.
  3. Explore AI solutions: There are various AI solutions available. Look for a vendor that caters to your specific needs.
  4. Start small: Don't try to do everything at once. Choose a pilot project focusing on a particular product category or warehouse location. This allows you to test the AI's effectiveness and gain buy-in from your team.
  5. Monitor and refine: Track the impact of AI on your chosen metrics (e.g., stockout rates, inventory carrying costs). AI is an ongoing learning process. As you gather more data, the AI's forecasts will continuously improve.

Finally, always remember—AI is a tool to empower your expertise, not replace it. Your business knowledge is crucial in interpreting the AI's recommendations and tailoring them to the specific needs that your business has. If you're interested in finding out more, get in touch with us today and kickstart your AI journey.

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