Learn how an integrated business platform can overcome the planning challenges faced by discrete manufacturing…
Seasonal holiday promotions used to be the highlight of the retail calendar. Across Easter, Christmas, Black Friday, and Valentine’s Day (to name a few), retailers and restaurant chains could count on spikes in sales thanks to intensive marketing and merchandising activity. But times are changing.
In recent months, many high-profile retailers and dining chains have suffered at times when they would traditionally have been flourishing. Stagnant sales, falling footfall, and poor performance are leaving chains with excess stock and uncertainty over their future direction.
While several factors are contributing to the cause, inefficient and outdated planning and analysis methods used by many retailers and restaurateurs are playing a key part. These solutions are incapable of providing real-time insight for decision-making and helping retailers to ride the waves in an increasingly stormy sea.
Now, more than ever, businesses need to be scrutinizing their activity to the lowest level of detail. It is only through doing this that they can hope to maximize their competitiveness by increasing profit margins, decreasing costs, and removing waste. One of the key ways for retailers to achieve this operational efficiency is to conduct comprehensive demand forecasting and capacity planning, ensuring the right products and promotions reach consumers at the right time and at the right cost.
While a traditional demand planning process requires an intense manual effort to continuously track, collate, compare, and forecast on factors such as sales and footfall, modern solutions offer a much more sophisticated approach. By automatically monitoring sales performance and incorporating macroenvironmental data, predictive demand planning solutions can accurately forecast supply and demand without human interaction. Having this insight to-hand can prevent overstock, overstaffing, and overspend, helping retailers to stay in the black.
This approach is already being realized by leading fashion retailer H&M, which utilizes demand planning capabilities from BOARD to plan resources across the business. The solution takes into account a number of variables including internal capacity, weather forecasts, and seasonal holidays to accurately predict store stock and staffing requirements.
Although the pace of change in retail collections is becoming faster, new powerful predictive capabilities are increasingly being used in fashion retail to forecast sales quantities for classic high-value products – typically the ones which always feature in the catalogs and for which a reliable track of historical data is available.
And it’s not just fashion retailers that can benefit from such technology. Thanks to the increasing cost of sourcing food, rising wages, and the rise in popularity of home delivery, restaurant chains are also under pressure to keep a close eye on profitability. The ability of demand planning software to gather sales figures and compare them with factors such as the utilization of ingredients, geodata, and diner numbers enables restaurateurs to remove underperforming menu items, optimize staff levels, and reduce the wastage of unused ingredients.
To quote another example, fast food chain KFC has substantially reduced planning cycles and removed a plethora of spreadsheets through its integrated business planning approach which incorporates demand planning capabilities.
As increasing numbers of retailers make the shift from spreadsheets to solutions, those who stick with legacy planning and forecasting systems will fall behind, risking both competitiveness and profitability. Making the change is a commitment, but one which retailers cannot afford to ignore if they are to maximize their performance and remain relevant. In the words of a well-known cliché, to fail to plan is to plan to fail.
To find out more about BOARD’s demand planning and forecasting capabilities, request a demonstration using the form at the top right of the article.
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