For businesses in the fast changing food and beverage industry, finding the perfect inventory levels to avoid surplus and stockouts is a usual yet elusive goal. Food retailers continue to feel evolving pressure from e-commerce and omnichannel:
 
•             70 percent of consumers are going to do their grocery shopping online by 2024
•             This brings about a $100 billion market and additional channels to think about when forecasting demand
•             54 percent of grocery customers say product accessibility is more crucial than price
•             Customers visit two to three stores on average to do their food shopping, and expect to find customized offerings on the shelf
•             75 percent of wholesale suppliers say sustaining with new competitors, customers and channels for example , e-commerce is their top challenge in forecasting demand
 
Food-based businesses troubled to combat complex, unknown demand patterns often turn to stockpiling as a strategy. Many are seeing that more is not always better.
 
Too Much of a Good Thing?
 
Stockpiling inventory to meet any foreseeable situation can create additional holding costs and affect profitability. In a recent survey, over 60 percent of wholesalers report having more than one month of inventory on hand, an increase from 2018.
 
But even with these record inventory levels, customer service levels are actually decreasing. The same research found that 77 percent of businesses had lost sales, and 27 percent of wholesalers missed sales of more than 4 percent, an 8 percent increase from 2018.
 
No matter how much inventory you have on hand, some missed sales are unavoidable – the key is to develop a data-driven strategy for optimizing inventory levels. That is an area many food-and-beverage businesses haven't explored yet. They depend on elementary forecasting models or just trust their gut, instead of using data to make informed purchasing decisions. Food-based organizations can easily enhance customer service levels without the added cost of stockpiling inventory. It just takes some simple math and awareness of what NOT to do.
 
Here are the five common inventory management mistakes made by food-and-beverage demand planners, and how a much more streamlined, data-driven approach can help:
 
1.            Not optimizing by product. Almost four in 10 businesses report problems forecasting the lifecycle of individual products. As a result, businesses take a blanket method and adjust levels across their total portfolio, rather than considering the factors that impact demand for each SKU. To create an effective service-level strategy, food wholesalers need a comprehensive baseline forecast that builds in the nuances of individual items.
 
2.            Not responding to customer preferences quickly enough. From gluten-free to keto-friendly, what’s prominent today can quickly become tomorrow’s castoff. Cultural trends and purchasing preferences can result rapid shifts in the products and channels customers want to use. Without a way to track these factors in real-time, businesses struggle to expect deviations from expectations and can ramp up inappropriately assigning inventory levels.
 
3.            Inability to make seasonal adjustments. Altering product levels based on seasons is an obvious, but often overlooked, forecasting tool. Close to half (45 percent) of businesses say that guessing seasonal demand is a challenge. To manage these swings successfully, businesses have to have an inventory optimization system that can create these alterations and results in a better accurate forecast.
 
4.            Not considering external factors. What happens to product demand if a sudden hurricane appears off the East Coast or a restaurant chain’s workers go on strike? Without a platform that considers macro changes in demand, businesses are headed to fall on inventory optimization.
 
5.            Not optimizing order frequency. To offset rising transportation costs, 63 percent of businesses are placing less, larger orders. Without taking cost dynamics into factor to consider, however, businesses can wind up overpaying for products and winding up with more items than they need. Rather than holding more inventory, businesses have to consider all relevant costs to make more thoughtful decisions about what – and when – to order.
 
Managing Demand with Data
 
As demand patterns become more challenging, companies across the food industry are understanding that outdated forecasting models are no longer sufficient. Yet businesses have been slow to embrace technology-based approaches, with 50 percent of suppliers reporting they have not used machine learning in their forecasting yet. Implementing a measurement platform can yield significant outcome for businesses, differentiating them in an industry which continue to relies largely on manual methods.
 
The correct demand planning technology allows businesses to perform more advanced modeling techniques, such as economically optimized replenishment cycles, cost of service analysis and safety stock cycle, so they can forecast with confidence for every product in their portfolios. Using the power of predictive analytics, these technologies empower businesses to produce correct forecasts based on factors like:
 
•             Customer demographics
•             Sale price of items by transaction
•             Item promotions
•             Competitor information
•             Weather
 
Together with the right support, these data-driven demand planning instruments can support businesses handle inventory levels more correctly, improve customer service and drive revenue growth. In an industry where customer demand is more and more elusive, companies that leverage technologies to improve decision-making have a powerful advantage.
 
This article is originally posted on tronserve.com