Thanks to platforms such as Uber and its utilization of data and analytics, the office party is no longer followed by hours of waiting for a cab to get home. Over at Amazon, data analytics have been helping you find the books to gift to difficult relatives – or to keep for yourself. Even your local supermarket is in on point: data analytics there are deployed to improve customer service and create personalized offers.
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But can analytics affect the price of your Christmas cookies? Is big data changing the cost of a cinnamon cupcake? Emphatically, yes. The festive season now comes with a large side helping of data science. Here’s how:
Sourcing the Ingredients
Whether your preferred indulgence is a German stollen, an English Christmas cake, or American pecan pie, the chances are it includes a pretty international mix of ingredients. Sugar, flour, fruit, nuts and dairy products can be procured from all over the world. Even the local-sourcing artisan bakery gets its ingredients from a wide variety of suppliers – and is more dependent on data than the rustic interiors might at first suggest.
At one end of the supply chain, smart growers are using data analytics to keep their own costs down without compromising quality or damaging their profits.
Gathering and understanding the right information can help improve the output from each acre of land, for example, and keep the use of expensive fertilizers and water to a minimum. Elsewhere, dairy farmers can use data analytics to monitor the diet of their cows, either to get a better yield, to produce milk that is more suitable for making butter or cheese, or simply to keep their herds healthy and veterinary bills down.
At the other end of the chain, bulk buyers are using advanced analytics to buy their ingredients from the right supplier at the right time – and at the right price. For example, predictive analytics help them understand the impact of changing weather conditions on production and prices. We all know that cranberry consumption peaks during the holiday season – but data analytics are also helping buyers spot and respond to less obvious demand trends. With analytics and data helping them to identify consumer trends early on, they can make better planning and purchasing decisions.
Farmers and producers are also starting to connect machines and equipment with each other using Internet of Things technology. This produces another tranche of data about equipment performance that can be analyzed to create more efficient maintenance schedules that lengthen the lifecycle of expensive pieces of kit, and keep machines running at peak performance for longer for a lower overall production cost.
Transporting Raw Materials
Wherever they are grown, raw materials need to be transported. So commercial bakers can use data and analytics to work out whether it is more cost-effective to buy sugar from Brazil (which might be cheaper to buy but more expensive to deliver) or to spend more on sugar from the EU but less on transport costs. Importantly, they also help manufacturers work out the best time to buy: entering an agreement in June to buy in December could work out to be far more cost effective than bulk buying once the fairy lights are already up.
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Data and analytics can also help buyers work out the best trading routes and transport methods, taking into account costs such as transport fuel (a huge input cost), freight costs, the likely congestion in shipping routes, the need for refrigeration and other forms of compliant storage, international trade deals, demands for provenance and hundreds of other factors. Even choosing the size of ships can have an impact, by making it possible to dock and unload at ports with lower fees or shorter onward routes.
When transport, warehousing and other logistics are all lined up, buyers can shorten the time to market for seasonal goods, cut expensive waste and rents for unnecessary storage, and make their contribution to controlling the cost of the holidays.
Baking and Manufacturing
Transforming wheat into flour and flour into cakes on a commercial scale also offers plenty of opportunities for data analytics to make an impact. Efficient manufacturers use data and analytics to create streamlined operations where the right amount of every ingredient is in exactly the right place at exactly the right time. They can optimize scheduling and the use of equipment, so that neither raw materials nor finished goods are hanging around.
For example manufacturers can schedule a production sequence for all items that contain cinnamon to keep storage costs down. Or they can bake all nut-free goods before working on the peanut brittle – to maximize equipment uptime, without threatening the health of their customers!
Most of all data analysis can help producers understand what consumers want and adjust their output accordingly. Should Instagram users convince the world that kale brownies are the perfect Christmas gift, manufacturers with the right data can drop the cinnamon buns and get cracking on the kale to meet demand.
Selling the Goods
Retail is a highly competitive environment, and overheads contribute hugely to the goods we buy. Merchants can’t do much about sky-high rents, but they can use data to understand the locations and times their target customers are most likely to visit, helping them to maximize footfall and profit per outlet. Data analytics are also behind ‘smart’ buildings and shops, in which all other inputs, such as electricity, air conditioning, heating, and ventilation can be controlled to keep energy bills down.
Retail is also seeing something of a revolution in customer service. The ‘clicks and mortar’ model brings together the best of online information and personalization with targeted in-store service to create a much better shopping experience for customers. Behind it all? Interactive technology and in-depth analysis of customer data, shopping trends, and the effectiveness of in-store promotions.
The result is much more satisfying loyalty schemes (no more money-off vouchers for the Christmas Ham sent to vegetarians, for example). All of which helps maximize output without increasing costs. Better profit margins for shops means better prices for customers.
From turkey farm to festive table, and from spice gardens to spiced punch, the secret stories behind some of our seasonal favorites are far more exotic and complicated than first appears. But those stories are also rich in data – which thanks to advanced analytics can now be used to illuminate the journey, and make better decisions along the way.
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