Always considering every possible outcome when replacing stock, automated inventory management software ensures that your business is prepared for any replenishment challenge (head here if you need a refresher on replenishment).
Stock replenishment was once a dark art that was based on guesstimates, gut feelings and a mess of spreadsheets.
Whatever you sell, some products are likely in constant demand, while others sell less regularly. Often, it's the most expensive products which experience the most variable demand – making it even more important to order them with care.
Unfortunately, there's not a lot of margin for error, so you can't just hope for the best. Order too few of something important and your business pays the price in lost sales and disappointed customers. Order too many and your business pays the price in terms of crippled cash flow and a clogged warehouse.
With so many factors at play, replenishment is the perfect job for Artificial Intelligence. One of AI’s strengths is that it is capable of drawing on a vast range of data sources, allowing it to consider situations from every angle.
30,000 foot and microscopic view simultaneously
Rather than just looking at each product in isolation, AI can also account for relationships between products – including finding those that may not always be obvious. Handling this manually would quickly become a headache in any larger business, overwhelming for some of our multinational clients - who manage a range consisting of more than 100,000 products, spread across a 1000 locations.
Automated Replenishment doesn't just determine the most likely outcomes. It also considers the outliers and hedges its bets to protect the business against less probable events which could be catastrophic.
While it could be suggested that people have the benefit of experience when making such decisions, but this can also play against even the most experienced planner, who can also be blinded by assumptions and preconceived notions, only to justify decisions after the fact by pinning blame on an unforeseeable variable. AI puts these aside to take an unbiased look at the big picture, focused only on the search of undiscovered insight.
For example, one of Remi AI's clients experienced delays in ordering spare parts for its nationwide fleet of refrigerated vending machines – with breakdowns costing it dearly in sales. Applying AI to the challenge, it discovered a 60% spike in refrigerated vending machine breakdowns when an area went for more than 10 weeks with no days above 32 degrees.
Tracking the weather across every region of Australia in order to anticipate the demand for spare parts is exactly the kind of task on which AI thrives. The introduction of Automated Replenishment ensured spare parts were always on hand when required.
Remember, AI isn't there to completely take people out of the loop. While it can automate some decisions, part of its job is to offer insight into the more challenging cases to help people make more informed decisions.
AI vs Rules Based Systems
When it comes to Automated Replenishment, AI applies 'Reinforcement Learning'. This learns by trial and error, simulating all the possible outcomes to determine the best strategy for meeting your wider objectives.
There's a common misconception that Automated Replenishment is merely an advanced rules-based system, but it's actually the opposite.
With a rules-based system, you're constantly refining a large set of rules in the hopes of achieving the required business outcomes for each product – whether it be maximising sales, minimising stockouts or striking a balance.
With Automated Replenishment, you specify your required business outcomes and parameters. AI then works backwards, making the best decisions in order to achieve your goals. This lets you focus on your high-level objectives and priorities, rather than getting bogged down in the minutia.
With rules-based replenishment systems, businesses can make the mistake of treating them as set and forget. After a while, often the system will start making disastrous decisions because it's working with old data.
In comparison, AI decisions are always based on the latest information – taking into account up-to-date historical and seasonal data. AI can also look to the future, drawing on everything from weather forecasts and exchange rates to search trends and the schedule for upcoming major shopping events.
Automated Replenishment doesn't just blindly follow the rules. It looks at the big picture and weighs up every possible outcome to ensure that your business can always deliver the goods.
Want to know more about inventory management and replenishment? Head here to find out how AI can help you avoid stock-outs, or here to learn all about the things you need to consider before implementing AI. You can also see our platform in action via our case studies - including how we helped a wholesale distributor reduce stock-outs by 24%.Once you’ve had your fill of content from our blog, why not drop us a line here.