Spare Parts

Highlights

Spare Parts is an application designed to forecast the demand for spare parts. Manages all business and product hierarchies, and automates the process of spare parts management, implementing a statistical approach both easy to use and supported by the most advanced forecasting techniques, that guarantee the best results in terms of accuracy.

Business Context

The correct process of spare parts demand planning is a key success factor for any business that needs to manage a large number of items. In the manufacturing market, this phase is most important because it directly affects customer satisfaction and brand perception. Collect and analyze sales data, verify production plans and identify collateral conditions and assumptions without an automated process is likely to drain resources from value added activities, if you want to ensure the robustness of the process. These issues greatly affect accuracy, because forecasting errors can determine incorrect production plans, that are the basis of over stock inventory or shortage of inventory that means sales loss. Accurate demand forecasts are therefore key to determine the optimal stock and to reduce delivery times.

ì4C Application

Spare Parts provides accurate demand forecasts for thousands of spare parts. The application takes into account the amount of stocked items, the lead time for each individual component, the life cycle of the products and other factors, such as replacements and phase-in / phase-out. It then manages all the elements needed to get an accurate and reliable forecast. The application allows to efficiently manage and control the whole process of forecasting and production of components, through integration with the Safety Stock component and the ability to update intra-period forecast and to notify stock level or lead time anomalies for single components.

Business Pain – Key Features

  • Produce accurate forecasts for thousands of SKU’s
  • State-of-the-art statistical forecasting methods
  • Arima, ARMAX, AREG, Exponential Smoothing
  • Forecasts at any level of detail
  • Flexible forecasting horizon
  • Statistical KPI’s
  • Take into account Spare Parts peculiarities
  • Phase-In/Phase-Out events
  • Replacement and updating of product components
  • Lead time management for each component
  • Life cycle evaluation of products to which the single component refers
  • Seasonality, promotion, price drivers
  • Minimize the statistical know-how required
  • Automated modeling for less confident users
  • Stepwise selection, self-identification
  • Modeling logic based on SKU clustering
  • KPI driven intervention strategy
  • Not a black box: advanced users have free access to models
  • Reduce management effort
  • All operations can be scheduled
  • KPI for accuracy assessment
  • Alerts
  • Intervention strategy for exceptions driven by KPI
  • Intra-period forecast update
  • Notification of stock level or lead time anomalies for single components.
  • KPI monitoring the stability of the forecasting and reporting unexpected demand trends
  • Safety Stock