Use of different inventory models for efficient logistics management

By Avishek Majumder & Abhinash on July 15, 2019

Inventory plays a key part in logistics management to ensure that excess stock is not kept at the warehouse and at the same time consumer’s demands are met (Farahani & Rezapour, 2011). Through effective inventory models, businesses ensure that the products remain safe. Surveillance systems during the logistics operations ensure that the materials are handled well and breakages are minimized.

The accuracy of the inventory data ensures that there is no delay in supply and the products reach to the consumers without any damage. Log of inventory and the maintaining of the inventory database helps logistics operations effectively.

Types of inventory

Types of inventory
Types of inventory

Raw material inventory

It is the use of raw materials from the suppliers to develop final products. The raw material inventory management is helpful in different sectors such as automobiles, construction, and pharmaceutical sector (Ben-Daya, et al., 2013).

Work – in – process inventory

Work – in – process inventory products are those materials which remain to wait in line and transformed into something new (Kang, et al., 2017). This might include products that are not yet packaged or assembly products that are yet to be assembled.

For example, in packaged juice factory, oranges are first kept in the raw materials inventory in the storage area. But as soon as they are moved into the assembly line for the extraction of juice, they move to the work – in – progress inventory.

Finished goods inventory

Finished goods inventory comprise of products that are ready for shipments (Tang, 2011). The development of the raw – material inventory products or work – in – progress products also depend on the finished good inventory.

For examples, manufacturing industry, customised good industry, capital goods industry and FMCG industry follow this type of inventory. Companies such as Ford Motors has trimmed down its finished good inventory by decreasing its production.

Inventory models to integrate with logistics

Inventory models assist businesses to evaluate the maximum level of inventory required for maintaining the frequency of orders, production process, and determining the number of raw materials required. Generally, inventory models are classified as fixed reorder period system and fixed reorder quantity system. The multi-tier inventory model followed by Amazon and Quidsi follows the EOQ model. Economic Ordering Quantity (EOQ) model helps to offer solutions to address issues such as frequency of buying, amount of reserve stock and timing of buying products.

Economic Ordering Quantity (EOQ) model
Economic Ordering Quantity (EOQ) model

This model is derived from Baumol’s cash management model, formulated by F.W. Harris in 1913 and is further refined by R.H. Wilson (Sarkar, 2013). The components of carrying costs involve warehousing, administrative, handling, insurance, and deterioration, whereas the components of ordering costs involve order placing, transportation, requisitioning, administrative and storing. Quidsi which is the parent company of,, and has been successfully using this inventory model for its effective inventory management.

The multiple tier inventory model

The multiple tier inventory model helps to maximize inventory at a specific level in real-time, real-time visibility and automatically reciprocates demand and supply conditions across all inventory tiers (Zhang, 2013). Amazon has been using a multiple tier inventory model where the physical flow occurs from Tier 3 to Tier 1 and the information flow occurs between Tier 3 and Tier 1. Tier 1 is the Amazon Distribution center and Tier 3 is the manufacturers and vendors.

The multiple – tier inventory model
The multiple – tier inventory model

With the execution of multiple tier inventory model, Amazon has been able to manage:

  • inventory of million products,
  • shipment to consumers within one week,
  • gaining insights on the needs of the consumers,
  • facilitating delivery within two days,
  • better coordination with manufacturers and wholesale suppliers and,
  • providing buyers with information to track shipments.


  • Ben-Daya, M., As’ad, R. & Seliaman, M., 2013. An integrated production inventory model with raw material replenishment considerations in a three layer supply chain. International Journal of Production Economics, 143(1), pp. 53-61.
  • Farahani, R. & Rezapour, S., 2011. Logistics operations and management: concepts and models. 1st ed. NY: Elsevier.
  • Ha, B., Park, Y. & Cho, S., 2011. Suppliers’ affective trust and trust in competency in buyers: Its effect on collaboration and logistics efficiency. International Journal of Operations & Production Management, 31(1), pp. 56-77.
  • Kang, C. et al., 2017. Impact of random defective rate on lot size focusing work-in-process inventory in manufacturing system. International Journal of Production Research, 55(6), pp. 1748-1766.
  • Sarkar, B., 2013. A production-inventory model with probabilistic deterioration in two-echelon supply chain management. Applied Mathematical Modelling, 37(5), pp. 3138-3151.
  • Tang, D., 2011. Managing finished-goods inventory under capacitated delayed differentiation. Omega, 39(5), pp. 481-492. Zhang, D., 2013. An integrated production and inventory model for a whole manufacturing supply chain involving reverse logistics with finite horizon period. Omega, 41(3), pp. 598-620.

I am currently working as a Research Associate. My work is centered on Macroeconomics with modern econometric approach. Broadly, the methodological research focuses on Panel data and Times series data analysis for causal inference and prediction. I also served as a reviewer to Journals of Taylor & Francis Group, Emerald, Sage.