The Tier-N Configuration section within the Exposure Analysis Setup is used to define which Tier-N suppliers are considered for the Exposure Analysis. You can include or exclude different data sources and set filters for these sources.
Configuring Tier-N Settings
This section allows you to define the scope and depth of your Tier-N configuration. By adjusting these settings, you can control how many tiers of your supply chain are included, which data sources are considered, and how specific filters, such as commodities or shipment thresholds, are applied. Proper configuration helps ensure that your analysis is both accurate and aligned with your goals for the Exposure Analysis.
Max tiers
This defines how many tiers of the supply chain should be considered for the analysis. Increasing the number of tiers will increase the number of suppliers that are included.
Suppliers to consider
Define which suppliers are included in the Tier-N configuration scope based on data visibility:
Private: Any supplier based on the user’s organization’s private Tier-N data to be included in the analysis.
Shared: Any supplier based on data that your suppliers have shared with you in Prewave to be included in the analysis.
Public: Any supplier based on Prewave’s public supply chain graph to be included in the analysis. This data can be further filtered in the “Sources” section.
Commodity Filter
The Commodity filter is an advanced filter that allows the user to filter your Tier-N data based on a value chain structure. After filtering, only those suppliers that are relevant for the selected value chain are included for the analysis.
When accessing the Commodity filter, you have the option to select different commodities by checking their boxes as well as two toggles to turn on or off, as you see fit.
You can learn more about the functionality of “Suspected Supplier” and “Logistics Provider” in the overall Commodity filter article.
Sources
The Tier-N data consists of different sources, and depending on the data source there are different filtering options:
Customer: Any supplier based on the user’s organization’s private Tier-N data to be included in the analysis.
Customs: Some countries make customs data on the company-level available to the public, and Prewave uses this data to construct the public supply chain graph. You can further define the considered customs date with below filters:
Minimum shipments: Filter out buyer-supplier relationships where less than the defined amount of shipments have been shipped.
Shipments timeframe: Filter out buyer-supplier relationships where the latest shipment hasn’t taken place in the defined timeframe.
Media: Prewave scans media data for buyer-supplier relationships in a similar way as it does to find alerts. You can choose to include the buyer-supplier relationships identified in the media data into the analysis.
Prewave Prediction: Prewave has a proprietary model predicting buyer-supplier relationships based on companies' broader sourcing patterns, relevant commodities and geographical distance.You can further customize this setting by choosing the minimum probability:
Minimum probability: The outcome of the prediction model is a probability of the relationship, and the user can choose how high of a probability is desired. Choosing a higher probability will lead to less predicted suppliers to be added into the analysis.
Best practice: Optimal filter settings
Optimal filter settings depend on the user's organization’s risk tolerance and the capacity to act based on the results of the analysis. Looser filters (e.g. lower Minimum shipments value) lead to more suppliers analysed, and stricter filters (e.g. higher Minimum shipments value) lead to less suppliers analysed.
Analysing less suppliers is more efficient because potentially less exposures need to be acted upon, but it could mean that some risks go unnoticed. Therefore, defining very loose filters can be considered as risk-averse behavior, and very strict filters as risk-seeking behavior. However, very loose filters might not be feasible because they might lead to too many exposures to be actionable. Each organization must find a balance between the level of risk they are willing to tolerate and the capacity for follow up action based on the identified exposures.
The commodity filter is recommended because it is tailored to a specific value chain and will help achieve a balance between no essential information missed while keeping the volume of exposures manageable. Getting the best results with the commodity filter requires setting up the base collection according to a commodity.
This means that the collections are set up so that the scope of each collection corresponds to the desired commodity filter. For example, a user should ideally use a “Copper” commodity filter with a collection that contains copper suppliers.