Supply chain planning is highly complex yet vital for a smooth operating supply chain. Supply chain coordination, integration, and execution are always burdened by internal and external uncertainties. To be successful, firms must accurately anticipate and satisfy demand by planning for and controlling inventory. This dissertation is a compilation of three journal papers. The first paper examined the difference between the two most commonly used univariate time-series for demand planning: customer orders and sales orders (shipments). Here the forecast from two univariate time-series were tested, and results were validated using data from a North American fastener and tools manufacturer. It was concluded that the difference between the customer orders time-series and the shipments time-series for steady, normally distributed, demanded products and their respective forecast is systemic rather than random. The second paper proposed and assessed a multi-criteria inventory control model for the effective management of inventory. The rapidly deployable model (RDM), which is an equal-weight model, was compared with models in the extant literature. The proposed model performed better than most of the models studied. In the third paper, ABC inventory classes for turnover performance were studied. Results showed that A-class inventory has higher turnover and the smallest inventory days on hand than both B-class and C-class categories, irrespective of the model used. No performance difference was observed between the B class and C class.