OmniOrder® Core

Unifies trading, inventory and traceability to help producers and wholesalers of fresh and perishables reduce waste and improve margins through demand prediction and supply optimisation.

Our solution ensures that all fresh and perishable supply chain partners can benefit from using OmniOrder®️ based on their business needs.

POS for Market floor

OmniOrder® provides a simple tablet-based Point of Sale (POS) solution which was built for wholesale market trading. Unlike retail focused POS systems, OmniOrder® POS handles all of the nuances of fresh produce such as various packaging formats, multiple units of measure, grade and size, and has an easy-to-use interface built for speed, allowing orders to be taken in fast paced market trading floors.

Stock Control & Traceability

OmniOrder® allows full control of inventory at batch level. Batches can be created within the platform, or can be integrated with grading & sorting equipment to feed batches through. Live inventory eliminates the need for on-the-fly stock checks and allows orders from multiple channels to be linked to the same stock, optimising fulfilment operations.

Digital Order Taking

OmniOrder® allows digital orders to be received through our buyer app. Wholesale buyers can connect to their preferred suppliers and place orders based on the products offered.
OmniOrder® provides full control of the information displayed in the buyer app. Sellers can choose whether to display or hide specific products and whether to display prices or to request negotiation via in-built chat.

Invoicing & Pick Slip Automation

OmniOrder® automatically generates invoices, receipts and pick slips, as well as email notifications based on order status, whether taken face to face via Point of Sale or digitally through the buyer app.
OmniOrder® integrates with common accounting systems such as Xero and MYOB, allowing invoices to be immediately reconciled with the correct customer and reducing the need for manual data entry.

Supply & Demand Forecasting

Current forecasting methods are generally manual and based on historical data only. Typical forecasting methods involve manually identifying the same week in a previous year or year(s), which are considered to be similar to the current year, and manually adjusting based on experience and gut feel to account for new information like competitive activity, price movements, promotions and weather. Simple statistical forecasting often doesn’t provide an improvement over manual methods based on experience. However, machine learning based methods allow more variables such as broader price data, weather data, public holidays, geographical and socioeconomic data to be incorporated leading to greater accuracy than manual methods.

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