🔮Forecasting Unit 10 – Inventory & Supply Chain Forecasting

Key Concepts and Definitions

• Inventory refers to the goods and materials a company holds for sale or use in production
• Supply chain encompasses the network of organizations, people, and resources involved in the production and distribution of a product or service
• Forecasting predicts future demand, sales, or other business metrics based on historical data and trends
• Lead time represents the period between the initiation and completion of a process, such as the time between placing an order and receiving the goods
• Safety stock acts as a buffer to mitigate the risk of stockouts due to unexpected demand or supply chain disruptions
  • Calculated based on factors such as lead time, demand variability, and desired service level
• Economic order quantity (EOQ) determines the optimal order size that minimizes total inventory holding and ordering costs
• Reorder point (ROP) indicates when to place a new order to maintain adequate inventory levels
  • Considers factors such as lead time, average demand, and safety stock

Inventory Management Basics

• Inventory management involves overseeing and controlling the flow of goods from manufacturers to warehouses and from these facilities to point of sale
• Effective inventory management balances the costs of holding inventory with the benefits of having sufficient stock to meet customer demand
• Inventory types include raw materials, work-in-progress (WIP), and finished goods
  • Raw materials are components used in production (steel, wood)
  • WIP refers to partially completed products in various stages of the production process (unfinished furniture)
  • Finished goods are completed products ready for sale to customers (smartphones, clothing)
• Inventory costs comprise holding costs (storage, insurance), ordering costs (transportation, processing), and shortage costs (lost sales, customer dissatisfaction)
• ABC analysis categorizes inventory items based on their value and importance
  • A items are high-value, critical items that require close monitoring
  • B items are moderate-value items with less stringent controls
  • C items are low-value, high-volume items that are managed with minimal oversight
• Just-in-time (JIT) inventory management aims to minimize inventory by receiving goods only as they are needed for production or sale
• Vendor-managed inventory (VMI) involves suppliers taking responsibility for maintaining inventory levels at the customer's location

Supply Chain Fundamentals

• Supply chain management (SCM) oversees the flow of goods, information, and finances from raw material suppliers to end customers
• The supply chain typically consists of five stages: plan, source, make, deliver, and return
  • Plan involves forecasting demand, planning production, and managing inventory
  • Source refers to procuring raw materials and components from suppliers
  • Make encompasses the manufacturing or assembly of products
  • Deliver involves the transportation and distribution of finished goods to customers
  • Return handles the reverse flow of products (defective items, excess inventory)
• Upstream supply chain refers to the activities and processes that occur before the focal company (suppliers, manufacturers)
• Downstream supply chain refers to the activities and processes that occur after the focal company (distributors, retailers, customers)
• Bullwhip effect describes the amplification of demand variability as it moves up the supply chain, leading to increased inventory levels and costs
• Supply chain visibility enables real-time tracking and monitoring of goods, information, and financial flows across the supply chain
• Collaborative planning, forecasting, and replenishment (CPFR) involves cooperation between supply chain partners to improve demand forecasting and inventory management

Forecasting Methods and Models

• Qualitative forecasting methods rely on expert judgment, market research, and other non-statistical techniques (Delphi method, market surveys)
• Quantitative forecasting methods use historical data and mathematical models to predict future demand (moving average, exponential smoothing)
• Time series forecasting models analyze patterns and trends in historical data to predict future values
  • Moving average calculates the average of a specified number of past periods to forecast the next period
  • Exponential smoothing assigns higher weights to more recent data points, assuming recent trends are more relevant for future predictions
• Causal forecasting models examine the relationship between the variable being forecasted and other relevant factors (regression analysis)
• Hybrid forecasting combines qualitative and quantitative methods to improve forecast accuracy
• Forecast accuracy measures the difference between forecasted and actual values using metrics such as mean absolute deviation (MAD) and mean absolute percentage error (MAPE)
• Collaborative forecasting involves input from multiple departments (sales, marketing, finance) and supply chain partners to generate more accurate and comprehensive forecasts

Data Analysis Techniques

• Descriptive analytics summarizes historical data to provide insights into past performance (sales reports, inventory turnover)
• Diagnostic analytics examines data to identify the causes of observed outcomes or behaviors (root cause analysis)
• Predictive analytics uses statistical models and machine learning algorithms to forecast future trends and outcomes (demand forecasting, inventory optimization)
• Prescriptive analytics recommends actions or decisions based on the insights generated from descriptive, diagnostic, and predictive analytics
• Data mining uncovers hidden patterns, correlations, and insights from large datasets
  • Techniques include clustering, association rule mining, and decision trees
• Big data analytics processes and analyzes large volumes of structured and unstructured data to support decision-making
• Data visualization presents data in graphical or pictorial form to facilitate understanding and communication of insights (dashboards, heat maps)

Inventory Control Strategies

• Continuous review system monitors inventory levels continuously and places an order when the quantity reaches the reorder point
• Periodic review system checks inventory levels at fixed intervals and places an order to bring the quantity up to a predetermined target level
• Two-bin system uses two containers for each inventory item, with a new order placed when the first bin is empty
• Consignment inventory is owned by the supplier but held at the customer's location until used or sold, reducing the customer's inventory carrying costs
• Cross-docking minimizes handling and storage by transferring incoming shipments directly to outgoing vehicles, bypassing warehouse storage
• Inventory pooling consolidates inventory from multiple locations to reduce overall inventory levels and improve service levels
• Postponement delays final product configuration until customer orders are received, allowing for greater flexibility and reduced inventory risk

Technology and Software Tools

• Enterprise resource planning (ERP) systems integrate and automate various business processes, including inventory management, procurement, and financial management (SAP, Oracle)
• Warehouse management systems (WMS) optimize warehouse operations, including receiving, putaway, picking, and shipping (Manhattan Associates, HighJump)
• Transportation management systems (TMS) plan, execute, and optimize the movement of goods across the supply chain (BluJay Solutions, MercuryGate)
• Inventory optimization software uses advanced algorithms to determine the optimal inventory levels and replenishment strategies (LLamasoft, ToolsGroup)
• Radio-frequency identification (RFID) uses electromagnetic fields to automatically identify and track tags attached to objects, enabling real-time inventory visibility
• Barcoding and scanning technologies automate data capture and improve inventory accuracy
• Cloud-based solutions provide scalable and accessible platforms for inventory management and supply chain collaboration (Netsuite, Deskera)

Real-world Applications and Case Studies

• Walmart's vendor-managed inventory (VMI) program has suppliers responsible for managing inventory at Walmart's warehouses, reducing stockouts and improving efficiency
• Toyota's just-in-time (JIT) inventory system minimizes waste and inventory holding costs by synchronizing production with customer demand
• Amazon's predictive analytics and machine learning algorithms optimize inventory placement and improve demand forecasting accuracy
• Zara's agile supply chain enables quick response to changing fashion trends by postponing final product configuration until close to the point of sale
• Intel's collaborative planning, forecasting, and replenishment (CPFR) initiative has improved forecast accuracy and reduced inventory levels across its supply chain
• UPS's transportation optimization tools and real-time tracking capabilities enhance supply chain visibility and efficiency
• Procter & Gamble's data-driven inventory management approach has reduced inventory levels while maintaining high service levels
• Cisco's inventory pooling strategy has lowered overall inventory costs and improved customer service by consolidating inventory across multiple locations