Forecasting demand accurately is key to running an efficient supply chain, keeping inventories stocked to the ideal levels and fulfilling your customer orders. Analytics-based demand forecasting is more accurate than other approaches, allowing you to minimise money tied up in stock, avoid shortages at times of high demand and identify otherwise hidden inefficiencies.
Often, forecasts are based on past demand and a company’s perceived growth or decline in demand. This sort of approach is incredibly rudimentary and does not account for external factors in the market, such as new technologies, changing competition and evolving political landscapes.
Peak’s data machine combines info about your sales volumes with macro and micro economic data. We look at different models for forecasting demand within your business, such as time series based approaches, state space representations and exponential smoothing, and develop the one that is most accurate and best fits your business’s needs.
The Techie Bit
Our forecasts are built using methods ranging from simple distribution fitting to sARIMA models, exponential smoothing and Bayesian forecasting methods. We use these forecasts, alongside mixed integer programming, to determine optimum stocking levels across all of your locations.
Our data machine not only produces forecasting insights for your business, but suggests actions for you to take. Both are communicated to you in plain English by our analysts, but can also be delivered directly into your IT systems for accurate, automated, real-time decision-making. Ultimately, you’ll see improved efficiencies and increased revenues.