Topic Transport ORTEC

Demand Forecasting and Order Generation

What is Demand Forecasting and Order Generation?

Demand Forecasting and Order Generation is the process of predicting demand and orders in order to ensure customers will not run out of stock. In a Vendor Managed Inventory (VMI) environment, the vendor is responsible for the availability of the product in the storages of the delivery locations and has the flexibility to determine when to replenish them. The vendor therefore needs accurate demand forecasting and order generation algorithms to ensure customers won’t run out of stock.

Why optimize Demand Forecasting and Order Generation?

Demand forecasting and order generation is a key part of the VMI process. The planner can decide when and how much to deliver, adding complexity to the transportation planning process. And while inventory can be controlled using measured stock levels, this will only provide information on current status. For a controlled VMI process, it is crucial also to monitor future stock levels, and then respond with good decisions.

The key challenges for a planner in the VMI business are:

  • How can I prevent stockout now and in the future?
  • When is the best moment for an on-time delivery?
  • To which storages should I deliver?
  • What is the optimal delivery volume?
  • How can I increase my delivery size?
  • How can I prevent peak demands?
  • How can I easily monitor the accuracy of the data?
Finding the right balance between achieving on-time delivery and maximizing drop size is the biggest single challenge when it comes to VMI. Deliveries that are too late can mean high penalties, while too early deliveries will mean small drop sizes and thus higher long-term transportation costs.

These needs are especially related to sectors and businesses where the vendor is in control of the inventory of a limited number of liquid or bulk product storages at the customer site. Good examples are the delivery of goods to retail stores and the delivery of fuels to gas stations in the oil and gas industry.

How to optimize Demand Forecasting and Order Generation?

ORTEC’s Demand Forecasting and Order Generation helps planners through the decision-making process while creating orders. This process begins with determining the ideal moment and size of product delivery to storage. This requires good information on both the expected usage of the product over time and expected amount of product in the storages.

Stock levels are identified from various sources, such as telemetry readings, delivery readings and sales/usage information. While an alerting system flags up exceptional data in the system, which the user can then verify and/or correct. Using historical information, future stock levels are calculated based on forecasted demands. Various advanced algorithms exist for demand forecasting including moving average, exponential smoothing and seasonal indices. The optimal forecasting parameters are then determined by the Forecasting Analysis function, with historical usage, usage time windows, exceptional usage and non-usage periods (e.g. public holidays), daily variation, weekly differences and seasonal influences in demands all taken into account. In order generation, future calculated storage levels are used to determine the optimal moment and size of delivery, taking into account the combination of storages on a site together with many other factors such as delivery windows, vehicle/compartment sizes, storage sizes, etc.

What is Demand Forecasting and Order Generation?

The results of optimizing Demand Forecasting and Order Generation with ORTEC:

  • Prevent stock out
  • Determine the best moment for an on-time delivery
  • Determine the storages to deliver
  • Determine the optimal delivery volume
  • Maximizing drop size
  • Prevent peak demands
  • Prevent penalties
  • Savings on transportation costs
  • Decreased CO2 emissions
Why contact ORTEC to optimize Demand Forecasting and Order Generation?

The ORTEC offering results in a number of vital benefits:

  • More on-time deliveries with fewer stockouts
  • Optimal drop sizes
  • Spreading of risk: avoiding too many critical orders that don’t fit the fleet capacity
  • Less rework on the schedule during execution, as orders will now fit in the storages and delivery vehicles, and match the delivery windows.