In an ideal world, demand and supply would be steady and predictable, resulting in optimal capacity utilization and no back orders or missed customer orders. However, in the actual supply chain world variations in actual sales vs. projected sales result in lower forecast accuracy, and either overstocking or stock out situations.
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.
While major supply chain consequences aren’t usually life threatening, you can bet they have a huge impact on you, your organization, and your customers. Your goal is to prevent problems early but it doesn’t always happen.
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 our whitepaper on the 8 essentials of an S&OP Software, we outlined creating a consistent data basis for supply chain planning as an essential S&OP software requirement. In this week’s ‘Supply Chain Talk,’ Arkieva CEO Harpal Singh discusses the key aspects of creating a consistent data basis that supports supply chain planning.
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.
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