Businesses are starting to implement demand-sensing solutions, with the hope that they may improve supply chains in terms of replenishment planning effectiveness. Demand sensing tools analyze huge amounts of data to anticipate demand, generally from the Point of Sale as a primary source. Additionally, some supply chains are including Internet of Things (IoT) data to lower transportation costs and reduce inventory. IoT sensor data runs value chains and assists in ensuring that supply chains have accurate inventory levels in their locations.
How IoT works with supply chains
Historically, supply chains have run into problems with inaccurate forecasting and uncoordinated planning. To run value chains more efficiently and fully leverage data, about 70 percent of supply chain businesses have started transforming their supply chains.
Businesses are using IoT and demand-sensing technologies, which take large volumes of data and analyze them to help predict consumer demand, and more importantly, potential shifts in demand. Data includes variables like weather conditions, customer demand, point of sale, seasonal purchases, and other factors.
- Asset tracking through barcodes and tracking numbers
- Vendor relationships to optimize schedules, reduce costs, and increase production
- Inventory and forecasting, as sensor automation reduces human error
- Connected fleets to optimize deliveries
- Scheduling maintenance to manage predictive maintenance
If you have questions about how to use demand sensing software to synthesize sales and other data, contact Vanguard.
As supply chains grow, so should their demand planning
Supply chain practitioners interested in demand signal repositories should consider the demand management stages to better understand the data needed for demand sensing:
- Traditional forecasting: Helps with supply chain aggregates as companies starting out aren’t using granular data in forecasting models. With growth, they may use splitting formulas for historical sales.
- Statistical bottom-up forecasting: Helps with order lines and frequency at distribution centers (DCs). Here, businesses use forecasting with historical SKU data that’s used from shipments to help with replenishments.
- Ship-to forecasting: Helps with multiple channels; instead of working from the number of SKUs leaving distribution centers, forecasting assesses how many SKUs ship to every location (similar to multi-echelon inventory optimization).
- Demand sensing: Helps with point-of-sale and demand signal repository, where big data and IoT collaborate with demand-sensing software. Historical data provides SKUs from all sales for a particular location or time frame. Forecasts then use these profiles to describe the behavior patterns of customers for future demand.
How IoT and demand sensing work together
It’s been estimated that demand sensing lowers forecasting errors up to 30 percent when implemented correctly as part of a comprehensive approach to forecasting. Demand sensing also helps with inventory levels and replenishment planning.
IoT captures data from various sources:
- Internal data: Transportation, PoS, inventory, production, customer service, and e-commerce sales
- Business data: Staff, apps, websites, and marketing campaigns
- External data: Weather, third-party partners, dates, paydays, and macroeconomic indicators
- Other data: Wearables, social media, connected devices, geo-locations, and digital personal assistants
Ultimately, leveraging IoT data can provide a better ROI. Real-time delivery status enables a more accurate order schedule. Supply chains that have the right inventory levels at the right locations are better equipped to meet customer demand. Additionally, effective promotional planning inventory levels are optimized in order to meet predicted demand.
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