In retail, the market for supply chain decision software is tough to navigate. As the founder of a SaaS solution, I learned this the hard way.
But it's even harder for retailers and suppliers to retail. New technologies, especially AI derivatives, are quickly gaining ground. They typically use large language models and predictive analytics to learn what's hot, what's not, and what may go viral to trigger the right response for your supply chain.
Data driven decision-making is not new.
While having lots of data is great, data-driven decision-making is nothing new. As a merchandiser in the early nineties, I found the reasons for good performance from data. By looking at Polaroids of merchandise called 'Buyer's Cards.' Assistants wrote the weekly sales on the back of these, along with data to answer questions like: 'can we order more?' and 'how quickly can it get to stores?' For in-house production, we asked if we still had fabric and capacity. Using data, we found the winners and discussed why they were successful, based on style, colour, price, fabric, and fit. Our only goal: buy more of what sells, less of what doesn't, and quickly liquidate the 'losers,' starting with the stores that generated the most cash.
Simple. But not easy.
The decision-making units (DMUs) for different product categories were often referred to as 'discussion making units' due to endless 'what if' conversations. What if we fly the products in? What if we deliver daily? What if we eliminate minimum order quantities? What if we cross-dock or ship in packs of multiple units? Whenever we thought we had a good idea, we implemented it on a small scale and adopted it for the next season when it proved helpful.
Today, we have much more data and many ways to get, organise, and use it.
Data-driven decision-making has become a lot faster and cheaper since the nineties. But has it gotten any better?
If we judge 'better' by selling more, buying less, and spending less to do that, then I would argue that it hasn't. Most retail supply chains are still slow and expensive, and their service levels haven't improved at the rate of the tech innovations they may have adopted, because many fashion retailers are still facing massive inventory surpluses as the result of prolific overproduction, leading to significant markdowns, waste and cash tied up in stock.
To truly improve supply chains, we need to shorten the cash-to-cash cycle: order later and ship later to convert more with less (inventory). This will enhance the supply chain's ability to make and execute better decisions faster and cheaper, increasing throughput.
Data driven decisions should use rules, not opinions or averages, even if those averages come from big data, because every SKU is an opportunity to make or lose money and very few, if any, behave in an 'average' way.
Rather than trying to predict the future with big data and making decisions on the 'best fit,' (the most average) we should embrace uncertainty and just accept that we need buffers in the right place to cater for uncertainty.
The really great thing about all this data, is that we can now learn the rules we need to apply to decisions in different situations to make the most money. With digital twin technology for the 'what if's'.
Simulations help us find the right rules and learn if they are being applied correctly, providing immediate feedback and enabling changes without waiting for the next season.
Let's move beyond being data-driven and become rule-driven.
Using a digital twin of your supply flows to retail, you can navigate the complexities of the retail supply chain with confidence and precision, ensuring that the right rules drive the right decisions that will in turn drive sales and profits.
If you want to know if (and how much) your supply chain can do better, and if improvements will come from new rules or from better execution of existing rules, let's simulate your supply chain and uncover the answers you need to succeed.
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Curious about the true potential of your supply chain? Simply send an email to hello@retailtwin.com and one of us will reach out, or schedule a call with me via the calendar link below.