Historical trend data has long been the backbone of demand forecasting. However, the unpredictability of external factors such as supply, logistics, and global trade partners necessitates a broader data scope. Without incorporating this data into near-term planning, even the most advanced AI tools can falter. The solution? A digital twin.
Creating a Digital Twin: Opening the Data Floodgates
A digital twin is a comprehensive, accurate, and feasible supply chain plan that requires a vast amount of data from various sources to feed the AI-enabled insights engine. This data includes information from all internal systems and data sources, such as ERP, CRM, MES, WMS, TMS, PLM, finance and accounting, and other planning systems.
Incorporating External Partner Data
In addition to internal data, external partner data across all tiers is crucial. This includes POS, store inventory, distribution center inventory, materials inventory, supplier capacity, supply commits/decommits, advanced shipping notices, third-party data (weather, social sentiment, risk events, import and export duties and tariffs, restricted party lists, forced labor regulations, market data, etc.), and IoT sensor data.
Streamlining Multi-Enterprise Connections
A supply chain business network, akin to LinkedIn, can streamline and scale the process of connecting to ecosystem partners. However, not all networks are created equal. The scope, scale, and capability of these networks can vary significantly. The ideal network should offer complete visibility across all four ecosystems – supply, channel, logistics, and global trade, and connect across all tiers of upstream and downstream partners.
Transforming Dirty, Disparate Data into Valuable Assets
Data is inherently dirty, containing missing, wrong, and null values. A multi-enterprise MDM (ME-MDM) can transform this disparate data into a valuable, decision-grade asset. It acts as a universal translator that understands and interprets information from every system and partner into a single, clear language. The system must cleanse errant values and harmonize data from disparate multi-enterprise sources at scale.
The key to effective supply chain planning lies in harnessing the power of data, both internal and external, and utilizing it to create a comprehensive digital twin. This approach not only improves planning processes but also paves the way for a more efficient and streamlined supply chain.
