Should you factor returns in your forecast error calculation? In this article, we’ll use a sample data set, to demonstrate if you should consider returns when calculating your forecast errors.

An open order is defined as an order placed by the customer which is under process and is yet to be fulfilled by the supplier. For effective analysis, open order data needs to be recorded daily in an ERP system. A minimum of twelve to eighteen months of open order history is required for your sales forecasting analysis and fine-tuning process. A shorter period could render unreliable and skew the results of your data analysis.

Demand sensing helps us identify the actual customer order trends and helps us improve the near-term forecast. Strategic actions like demand shaping for knowingly increasing or decreasing the demand for the product can be undertaken by sensing demand signals.

Over the past few months, we’ve been running simulation tests on different demand forecasting methods: Winter’s additive & multiplicative, seasonal and robust seasonal. Then, we used MAPE to determine the forecast accuracy for each method. Here’s what we found.

Seasonal method is a regression method that fits a linear trend along with sine and cosine curves. These sine and cosine portions of the regression can fit any seasonal deviations from the linear trend. Robust seasonal method also fits a trend along with sine and cosine curves, however this method uses linear programming to fit a seasonal series in a way that compared to the regular seasonal method is less likely to be thrown off by noisy values that depart from the trend or seasonality.

For most businesses that rely on demand forecasts for supply and capacity planning, improving demand forecast accuracy is critical. There are many methods to measure forecast bias and the accuracy of supply chain forecasts including using statistical methods like the Mean Absolute Percent Error or MAPE that we’ve discussed in our previous blogs.

In the previous What-if Wednesday posts, we experimented with the three parameters of the Holt-Winters Multiplicative method, namely; alpha, beta, and gamma. This time we are going to run similar tests by adjusting damping factor for the Winters Multiplicative method.

The Supply chain is driven by demand, supply, and inventory planning. Under demand planning, the importance of sales forecasting is undeniable. It provides a basis for the production process regulating quantities, inventory and maximizes the efficiency of the resources available.

In this week’s What-if Wednesday post, Arkieva Supply Chain Optimization Consultant – Abhishek Shah – shares the results of a what-if demand forecast simulation using the gamma multiplicative forecast parameter of the Winters forecasting method.

Statistical forecasts are often used as the baseline forecast for demand planning. Due to this reason, statistical forecast accuracy is critical to improving the entire demand planning process. Use this easy step by step statistical forecast technique guide to help you get started with improving your forecasts.

“It is said that the present is pregnant with the future” – Voltaire Forecasting, therefore, is an attempt to deduce the future from the present. It is both, art and science. We will explore the practice of forecasting demand in the short to medium term. Within the constraints of economic and technology trends, demand forecasting drives the planning process in most businesses.

In this week’s What-if Wednesday post, Arkieva Supply Chain Optimization Consultant – Abhishek Shah – shares the results of a what-if demand forecast simulation using the Winters multiplicative beta parameter.