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Running out of stock is one of the last things that any retail business owner wants when a product is in hot demand. It is not only disappointing, but it leaves money on the table by letting your customers search for alternatives from your competitors. To avoid such a scenario, inventory forecasting needs to be utilized and implemented across all retail businesses, from mid-sized operations to large-scale enterprise companies.
Inventory forecasting isn’t simply a matter of analyzing past historical trends and predicting future demands. Accurate inventory forecasting requires the right data set from multiple data sources.
Before diving into the data and stats surrounding demand forecasting, it’s worth noting that, within the supply chain context in the eCommerce industry, there are three main types of forecasting, which are:
Demand forecasting:  This is the investigation of the companies demand for an item or SKU, to include current and projected demand by industry and product end use.
Supply forecasting: Is a collection of data about the current producers and suppliers, as well as technological and political trends that might affect supply.
Price forecasting: This is based on information gathered and analyzed about demand and supply. Provides a prediction of short- and long-term prices and the underlying reasons for those trends.

REACH YOUR SALES AND INVENTORY GOALS WITH CURVE

90% of Retailers Fail in Forecasting Since they Ignore Lost Sales

A recent study carried for 2018-2019 period by Neogrid points out an important aspect that many retailers ignore when making their forecasts. The report says that 90% of small businesses do not use their past lost sales to make future predictions. Most of them only focus on demands which sometimes changes hence resulting in huge losses.

With a report of past losses otherwise called historical lost sales, the prediction will most likely be reliable. If you, therefore, run a retail business and would like to make accurate predictions, then make sure you have figures of your historical losses. Use them together with stats on demand, and your inventory forecasting won’t fail.

Retail Businesses Face Serious Problems Even After Spending a Lot on Inventory Management

Reliable information from Bossa Nova, a leading provider of data service says that one of its surveys found that even despite the huge spending that retail businesses make, 73% of them still make inaccurate forecasts. It further reports that most of the problems encountered are as a result of price inaccuracy among others. It, therefore, means as a retail businesses owner, you need to take the time to get accurate prices if you want to make accurate inventory forecasts.

Automating Your Retail Operations Boosts Productivity and Accuracy

Bossa Nova survey report indicates automation could be all you need to improve your productivity. In fact, 73% of the retail businesses interviewed reported that their employee productivity improved when they introduced robots. Furthermore, the same study says that 74% of the retail business owners interviewed expressed their confidence in automation. They argue that their accuracy in inventory forecasts increased when they automated their operations. You should, thus, consider automating operations as well as predictions if you want to improve accuracy, and most importantly, the productivity of your employees.

67% of Retail Businesses Think that Inventory Analyses and Forecasting is a Waste of Time

While inventory analyses and forecasting is being promoted as one of the strategies of making reliable predictions about the future, some retail businesses see it as a waste of time. In fact, 67% of businesses interviewed in Bossa Nova survey released on 28th Feb 2019 feel that spending time analyzing inventory isn’t a good way to use employee’s time.
Instead of spending time on inventory forecasting, most retail businesses often focus on serving the customers present at a given time forgetting that the future is also important. While such an approach can help maximize profits, it is important to note that demand changes with time. A business can only rest assured of existence in the future if it plans ahead through inventory forecasting.

Most Retail Business Lag Behind Technologically

Over 80% of retail businesses lag behind when it comes to the use of technology to find solutions to problems. What is happening is that technology is rapidly changing, and there are so many new technologies that retail businesses can utilize these days. Are you among those lagging behind? Your retail business can make great strides with the right technologies.
In conclusion, it is crucial for retail businesses to plan for future sales, and how to meet the demands of their customers without running out of stock. Alternatively, having excess supply will also mean losses and failed planning.

ABOUT CURVE

Curve uses machine-learning based prediction technology, allowing companies to accurately forecast sales, products, and support requests, to increase revenue and optimize profitability. Our unique technology goes beyond traditional business intelligence, by recommending the right solutions based on use cases and customer segments.

Last week, Shai Cohen, Curve’s VP sales gave a talk at a Supply Chain Management conference in Tel Aviv about the many benefits that machine learning provides in supply chain management. More specifically, Shai talked about how Machine Learning technologies such as Curve’s are helping merchants create clearer inventory forecasts that ultimately improve sales.

A recent report by Wakefield Research which surveyed companies with $500m+ in annual revenue suggests an ongoing catastrophe in how inventory is tracked and forecasted in the $1.3 trillion retail sector.
Over 70 percent of those surveyed, stated that inaccurate inventory forecasting is a major issue, resulting in costly supply-demand mismatches. Additionally, two-thirds of those surveyed reported difficulty tracking inventory through the supply chain.
A few of the most common challenges that eMerchants, as well as brick-and-mortar retailers, are faced with is predicting online demand for their products, especially during holidays (Black Friday, Cyber Monday etc.). Another major challenge is managing inventory for both online and offline activities.

REACH YOUR SALES AND INVENTORY GOALS WITH CURVE

Due to rapidly emerging tech, a major shift is being witnessed across every industry vertical, and the supply change industry is no exception. Professionals need to be prepared for a sudden influx of new orders on account of quick growing retail businesses, or alternatively an unexpected business slowdown which may lead to unsold inventory. That’s where machine learning comes into play.
As Shai shared with the conference attendees, “Curve uses machine-learning based prediction technology, allowing companies to accurately forecast sales, products, and support requests, to increase revenue and optimize profitability.” He also said that “Curve’s unique technology goes beyond traditional business intelligence, by recommending the right solutions based on use cases and customer segments.”

Machine learning when used in conjunction with IoT further provide real-time monitoring throughout the supply chain. With the right sensors and reporting, organizations can track every item through its supply chain with ease. This allows for the identification of core inefficiencies that need to be resolved, as well as the ability to streamline the supply chain process.
In short, Machine learning now makes it possible to discover patterns in supply chain data by relying on a mix of historical data and algorithms that quickly pinpoint the most influential factors to business growth.