November 18, 2019      < 1 min read

Regardless of the industry, it is necessary for manufacturers and retailers to maintain the right quantities of inventory stock to ensure the smooth running of production operations and sales activities.

Holding certain levels of inventory stock helps companies to avoid lost sales, will reduce ordering costs, help to maintain efficient production runs and reduce customer service dissatisfaction. However, in addition to these benefits, there are two broad costs associated with holding inventory stock: order processing costs and carrying costs.

To mitigate some of the costs associated with ordering and carrying inventory stock inventory models have been developed to help companies determine the optimal inventory stock levels to maintain relative to their organisation.

These inventory control models are classified into two major types the Deterministic Models, built on the assumption there is no uncertainty in the demand and replenishment of inventory stock and Probabilistic Models which acknowledge a degree of uncertainty in the demand pattern and lead time of inventories.

Probabilistic model of inventory control

The Probabilistic inventory model is closely aligned to the manufacturing and retail reality that from time to time, demand will vary. Demand variations cause shortages, particularly during lead time if a retailer only has a limited amount of inventory stock to cover the demand during the lead time when replenishment stock has not arrived.

The probabilistic inventory model incorporates demand variation and lead time uncertainty based on three possibilities.

The first is when lead time demand is constant but the lead time itself varies and the second is when lead time is constant but demand fluctuates during lead time. The third possibility is when both lead time and demand during lead time vary.

Employing known economic, geological and production data the probabilistic inventory model creates a collection of approximate inventory stock quantities and their related probabilities. The advantage of a probabilistic approach is that by using values within a bandwidth, modelled by a defined distribution density, you achieved greater reliability than when using deterministic figures.

Probabilistic inventory methods

Probabilistic inventory models consisting of probabilistic supply and demand are more suitable in most circumstances. Two methods are used based on the frequency of order placement for procuring inventory stock, these are single period and multi-period inventory systems.

  • The term single period term refers to the situation where the inventory stock is perishable, and orders are typically only made once. Generally, for one time ordering of seasonal products or where demand exists only for the period in which it is ordered. For example, a newspaper sold today will not be sold at the same price tomorrow nor will summer clothing items be likely to sell during the winter season.
  • An incremental analysis is used to determine the optimal order quantity for a single period inventory with probabilistic demand. Assessing how much to order by comparing the cost or loss of ordering one additional unit with the cost or loss of not ordering that one additional unit.

With the multi-period method orders are placed multiple times over an entire production cycle and are further classified as continuous review or periodic review inventory.

  • Continuous review inventory is reviewed constantly and when inventory stock drops to a certain predetermined par or reorder level, a fixed quantity is ordered. Continuous review is commonly used for high volume, valuable or important stock items.
  • Periodic review inventory is examined at periodic intervals in predetermined timeframes, irrespective of the levels to which inventory levels drop. At this time an order is then placed to bring inventory up to the maximum level, the method is largely used for moderate volume items.

In plain terms, the probabilistic model of inventory control is based on or adapted to a theory of probability which involves or is subject to chance variation. Multiple possible outcomes exist, each having varying degrees of certainty or uncertainty of its occurrence.

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