🏭Production and Operations Management Unit 7 – Inventory Management

What's This Unit All About?

• Focuses on managing inventory levels to optimize costs and meet customer demand
• Covers various inventory systems (periodic, perpetual) and their characteristics
• Explores the trade-offs between holding too much or too little inventory
  • Overstocking leads to increased carrying costs and potential obsolescence
  • Understocking results in lost sales and customer dissatisfaction
• Introduces techniques for inventory control (ABC analysis, EOQ model)
• Emphasizes the importance of accurate demand forecasting for effective inventory management
• Discusses the role of technology in streamlining inventory processes (RFID, barcoding)
• Provides real-world examples and case studies to illustrate inventory management concepts in practice

Key Concepts and Definitions

• Inventory: raw materials, work-in-progress, and finished goods held by a company
• Safety stock: extra inventory held to protect against stockouts and demand variability
• Reorder point: inventory level at which a new order should be placed to replenish stock
• Lead time: time between placing an order and receiving the inventory
• Carrying costs: costs associated with holding inventory (storage, insurance, obsolescence)
• Ordering costs: costs incurred when placing an order (administrative, transportation)
• Stockout costs: costs resulting from running out of inventory (lost sales, customer goodwill)
• Service level: probability of meeting customer demand from available inventory

Types of Inventory Systems

• Periodic inventory system: inventory levels are reviewed at fixed intervals (weekly, monthly)
  • Requires physical counting of inventory at the end of each period
  • Suitable for businesses with low inventory turnover and stable demand
• Perpetual inventory system: inventory levels are continuously monitored and updated in real-time
  • Relies on technology (barcode scanners, RFID) to track inventory movements
  • Provides up-to-date information for better decision-making and inventory control
• Two-bin system: inventory is divided into two bins, with one bin used while the other is being replenished
  • When the first bin is empty, an order is placed to refill it
  • Ensures continuous availability of inventory and minimizes stockouts
• Consignment inventory: inventory is held by the customer but owned by the supplier until used or sold
  • Reduces inventory carrying costs for the customer and improves cash flow

Inventory Costs and Trade-offs

• Holding costs: costs associated with storing and maintaining inventory
  • Include storage space, insurance, obsolescence, and opportunity costs of tied-up capital
  • Increase with higher inventory levels and longer holding periods
• Ordering costs: costs incurred when placing an order for inventory replenishment
  • Include administrative costs, transportation, and handling fees
  • Decrease with larger order quantities and less frequent ordering
• Shortage costs: costs resulting from insufficient inventory to meet customer demand
  • Include lost sales, expedited shipping, and damage to customer relationships
  • Increase with lower inventory levels and higher stockout frequencies
• Trade-offs: balancing the different inventory costs to minimize total costs
  • Increasing order quantities reduces ordering costs but increases holding costs
  • Decreasing safety stock reduces holding costs but increases the risk of stockouts

Inventory Control Techniques

• ABC analysis: categorizing inventory items based on their value and importance
  • A items: high-value, critical items that require close monitoring and control
  • B items: moderate-value items with less stringent control requirements
  • C items: low-value, non-critical items that can be managed with simple techniques
• Economic Order Quantity (EOQ) model: determines the optimal order quantity to minimize total inventory costs
  • Balances ordering and holding costs to find the most economical order size
  • Assumes constant demand, lead time, and costs
• Reorder point (ROP) system: triggers a new order when inventory reaches a predetermined level
  • Considers lead time and safety stock to prevent stockouts
  • Can be combined with EOQ to optimize inventory levels and ordering frequency
• Just-in-Time (JIT) inventory: aligning inventory arrivals with production or customer demand
  • Minimizes inventory holding costs and reduces waste
  • Requires accurate demand forecasting and reliable suppliers

Demand Forecasting for Inventory

• Qualitative methods: rely on expert judgment, market research, and customer surveys
  • Suitable for new products or markets with limited historical data
  • Include Delphi method, market surveys, and panel consensus
• Quantitative methods: use historical data and mathematical models to predict future demand
  • Time series analysis: identifies patterns and trends in past demand data
    • Moving average, exponential smoothing, and trend projection
  • Causal models: examine the relationship between demand and external factors
    • Regression analysis, econometric models, and machine learning algorithms
• Collaborative forecasting: involves sharing information and insights between supply chain partners
  • Improves forecast accuracy and aligns inventory levels across the supply chain
  • Techniques include Collaborative Planning, Forecasting, and Replenishment (CPFR)

Technology in Inventory Management

• Barcode scanning: automates data capture and improves inventory tracking accuracy
  • Enables real-time updates of inventory levels and movements
  • Reduces manual errors and speeds up inventory processes
• Radio Frequency Identification (RFID): uses radio waves to track and identify inventory items
  • Provides real-time visibility and enables automated inventory tracking
  • Improves inventory accuracy, reduces stockouts, and facilitates inventory optimization
• Warehouse Management Systems (WMS): software that optimizes warehouse operations and inventory control
  • Supports receiving, putaway, picking, and shipping processes
  • Integrates with other systems (ERP, TMS) for end-to-end inventory visibility
• Cloud-based inventory management: provides real-time access to inventory data from anywhere
  • Enables collaboration and information sharing across the supply chain
  • Offers scalability, flexibility, and cost-effectiveness compared to on-premise solutions

Real-World Applications and Case Studies

• Walmart: implemented RFID technology to improve inventory accuracy and reduce stockouts
  • Achieved 99% inventory accuracy and reduced out-of-stocks by 30%
  • Enabled real-time inventory tracking and replenishment across its supply chain
• Toyota: pioneered the Just-in-Time (JIT) inventory system in its manufacturing operations
  • Minimized inventory holding costs and reduced waste by aligning inventory with production
  • Achieved higher efficiency, quality, and responsiveness to customer demand
• Amazon: uses advanced algorithms and machine learning for demand forecasting and inventory optimization
  • Analyzes vast amounts of data to predict customer demand and optimize inventory levels
  • Enables fast delivery times and high customer satisfaction through efficient inventory management
• Zara: employs a responsive inventory system to quickly adapt to changing fashion trends
  • Continuously monitors sales data and customer feedback to adjust inventory levels
  • Achieves high inventory turnover and minimizes markdowns through rapid response to demand