The process of demand forecasting involves predicting future sales of a given product or service. The best way to do this is through trend projection, which looks at past sales to predict future sales. When using trend projection, make sure to remove any anomalies, such as spikes caused by viral stories or temporary drop-offs after eCommerce site hacks. However, demand forecasting can be very challenging for ecommerce companies, and there are a number of challenges you may face.
Disadvantages of Demand Forecasting
Demand forecasting by stock trim has several advantages, and it is one of them. It reduces the cost of unused components and materials by preventing companies from over ordering. It also helps companies fulfill customer orders, reduces inventories, and eliminates the need to expedite last-minute orders. However, this practice is not without its disadvantages. Let’s discuss these below to better understand how to optimize demand forecasting.
Spreadsheets are a great tool for start-ups and are relatively low-cost, but their limitations become problematic when SKUs increase. Spreadsheets cannot handle large amounts of data, are slow to analyze and lack security. They are also not easy to integrate with ERP and sales source systems. Moreover, spreadsheets do not allow collaboration or security, two factors essential for effective supply chain management.
Challenges of Demand Forecasting
The challenge of determining demand from historical sales data is even greater today, when consumers’ attitudes toward risk and anxiety are changing. With multiple sales channels, new products are launched everyday, and customers have more choices than ever before, it is imperative to accurately predict what will be sold when. In addition, customers are more discerning than ever, so poor demand forecasting can mean losing customers. In order to make sense of this complex problem, demand forecasting is a vital function of any company’s planning process.
Demand forecasting by stock trim can be highly effective in helping retailers prevent overstocking, broken assortments, and replenishment costs. It can also help to determine pricing by taking into account the price elasticity of demand. By utilizing a forecast, a retailer can determine what type of price to charge to drive sales and avoid having empty shelves. It’s also vital to consider seasonality. Certain products may become popular only during certain seasons, and therefore, be hard to predict.
Methods of Demand Forecasting
Several methods are available for demand forecasting by stock trim. Using the trend projection method is the easiest method. Trend projection analyzes past sales data to predict future sales. However, to be accurate, you must remove anomalies, including recent viral stories and eCommerce site hacks. Listed below are some of the most common types of trend projection methods. To get the best results, use data from the last two years.
One of the most important applications of demand forecasting is in production planning. It can also be used to identify new market potential and future capacity requirements. It is best to use a combination of competitive pricing, marketing tactics, and business development strategies. Cutting prices or placing items on promotion can temporarily increase demand. However, if demand is weak, a company may need to rush restock and incur rush charges from suppliers. Aside from this, a promotion can boost sales. Companies can also use the scarcity principle in promoting products, such as offering exclusive deals.
Demand Forecasting for an Ecommerce Business
For ecommerce businesses, determining the right balance between inventory and demand is critical. Using quantitative data analytics or qualitative forecasting, ecommerce businesses can improve inventory management by analyzing historical data and trends. Quantitative forecasting relies on hard data and develops trend projections based on prior information. Data can be collected through website analytics and sales data, and statistical methods allow companies to extrapolate trends from individual SKU sales.
Demand data is unreliable. Using last year’s data as a base for forecasts can be skewed by demand fluctuations. Consumer behavior has changed significantly since last year, and news stories and current events continue to shape buying behaviour. As a result, it’s difficult to predict demand accurately. In addition, data is also affected by ‘coronavirus effects’, which can skew forecasts.
Demand Forecasting improves supply chain efficiency and visibility by integrating customer service into the S&OP process flow. While customer service is generally seen as a planning function, it can also provide a vital feedback loop.