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Celonis Product Documentation

Inventory Management Planning Parameter Optimization app - object-centric

Accurate and up-to-date planning parameters are essential for efficient supply chain planning. Planning Parameter Optimization, an Inventory Management app, enables planners to identify materials with overstated or understated lead times, safety stock, or reorder points based on their consumption and replenishment patterns. The app provides recommendations and action flows to enable material planners and supply chain analysts to easily correct parameters in the source system. The result is improved supply chain planning, with reduced stockout and excess inventory scenarios, freed working capital, improved material availability, and increased customer satisfaction.

The Inventory Management Planning Parameter Optimization app leverages data from purchasing, production, and sales to derive recommendations for these planning parameters:

  • Purchase Lead Time (average, median, or optionally with Machine Learning to eliminate biases and outliers)

  • Production Lead Time (average or median)

  • Safety Stock (King’s formula)

  • Reorder Point (Lead Time Demand + Safety Stock)

These Material Insights are contextualized with your current planning parameters and your transactional data during calculations. Material priority is based on the degree to which the planned parameters in the source system deviate from Celonis' recommendation. The app highlights value opportunities when the material’s master data for the lead time, safety stock level, or reorder point shows that it is 20% above or below the recommended value. Planners can also use inefficiency classifications and ABC/XYZ classifications to determine material priority.

To help you better understand the impact of different factors, the app offers an exhaustive simulation feature for business users to compute their own parameters. They can then write either the recommendation made by Celonis or their own simulated value back to the ERP system.

As an additional feature if you join our Early Adopter program, the Inventory Management Planning Parameter Optimization app can leverage a Machine Learning driven model to eliminate biases and outliers and determine accurate lead times based on historical transactions. The model looks for materials with insufficient historical data to compute parameters for similar materials.

The Inventory Management Planning Parameter Optimization app comes with these views:

  • Material Insights - in this view users can access and filter a prioritized list of materials with recommendations by Celonis to update and improve planning parameters, including lead times, safety stock, and reorder points. There’s a bulk update option to multi-select materials and mass update parameters in the source system with recommendations from Celonis.

  • Safety Stock Validation - in this view users can validate the recommended safety stock by checking the KPI against input orders used to compute replenishment lead times and consumption.

  • Lead Time Settings - this view lets you select the calculation mode for lead times (average, median, or Machine Learning).

  • Material Profile View - this view contains a deep dive on a given material-plant combination with the following content:

    • Recommendations - this view lets users review Celonis’ recommendations to correct and improve lead time, safety stock, and reorder point parameters alongside current master data values in the source system and values simulated by the planner. You can accept or dismiss recommendations and update the master data. 

    • Lead Time - this view enables a deep dive into lead times at the transaction level to validate lead time recommendations.

    • Consumption - this view provides a detailed analysis and visualization of historical consumption data, including order quantity at the transaction level, to validate safety stock recommendations.

    • Simulation - this view is a deep dive into the excess stock inefficiency. Users can modify input parameters such as average consumption and service level based on new updates or anticipated events.  The app computes simulated parameters based on the new inputs, which can then be updated in the source system using Action Flows.

This documentation is for the object-centric version of the Inventory Management Planning Parameter Optimization app, which works with objects and events created for object-centric process mining. The Inventory Management Planning Parameter Optimization app needs some additional custom object types to enable its use cases, so you’ll need to create a custom Inventory Management perspective to use with the app in place of the supplied perspective. We’ll provide a step-by-step guide for you to do this.

The Inventory Management Planning Parameter Optimization app was formerly known as the Inventory Management Master Data Improvement app, and the case-centric version of the app still has that name. For the documentation and setup instructions for the case-centric version of the app, see Getting Started Guide.