Development of a hybrid framework for inventory leanness in Technical Services Organizations

Area of Science:

• Operations Research
• Supply Chain Management
• Inventory Control

Background:

• Supply chain uncertainties in demand and supply lead to suboptimal inventory replenishment, causing lost sales or excessive stock.
• Organizations often maintain high inventory levels, consuming a significant portion of their annual budget, to manage erratic demand.
• Effective inventory management requires accurate decisions on order quantity and timing, driven by precise demand information.

Purpose of the Study:

• To develop and implement a hybrid framework for optimum level inventory forecasting in Technical Services Organizations.
• To address the critical inventory management challenges of determining 'how much to order' and 'when to order'.
• To enhance competitive advantage by minimizing excessive inventory accumulation through precise demand forecasting.

Main Methods:

• Statistical analysis of historical data and comprehensive fault trend analysis.
• Development of a comparative analysis matrix based on price and quantity.
• Implementation of a decision criterion (Forecasting Model) using Weighted Moving Average, Exponential Smoothing, and Trend Projection with Minimum Absolute Deviation.

Main Results:

• Identified a forecast error of 142.5 million rupees over five years, leading to over 25,000 excess inventory units.
• Analysis revealed that 65% of the annual budget is tied to only 9% of high-price, small-quantity (HS) items.
• The proposed forecasting techniques significantly reduced forecast errors for HS items.

Conclusions:

• A novel comparative analysis matrix for critical item identification was introduced.
• A Multi-Criteria Forecasting Model was developed to enhance inventory management across various operations.
• The study proposes integrating forecasting criteria into an interactive Decision Support System (DSS) for maintaining optimal inventory levels.