• Exam Structure:

    • A formula sheet will be provided during the exam to assist students in calculations and concepts.

    • Types of questions:

      • Math and Calculation Questions: These will involve numerical problems requiring calculations based on the course material.

      • Conceptual Questions: Focus on the understanding of core concepts discussed throughout the course.

      • Full Conceptual Questions: These require comprehensive answers demonstrating a deeper understanding of multiple concepts and their interconnections.

  • Supply Chain Club Meeting Details:

    • Time: 05:15 PM
      to promote punctuality.

    • Location: Sark Lecture Hall, a central location on campus ideal for gatherings.

    • Dinner Provided: Free Pizza will be served during the meeting to encourage attendance and engagement.

    • Officer Application for Next Year Announced: Details regarding the applications for leadership positions within the club will be discussed; all interested students are encouraged to attend and participate.

  • Economic Order Quantity (EOQ):

    • Purpose: Focused on minimizing total costs associated with inventory management, optimizing purchase and storage strategies.

    • Total Cost (TC): Comprises two main components:

      • Ordering Cost: Calculated as (Number of Orders per Year) × (Cost per Order).

        • Number of Orders: Determined by the formula, Annual Demand (D) / Order Quantity (Q), which highlights the efficiency of order quantities.

      • Holding Cost: Calculated as (Average Inventory) × (Holding Cost per Unit).

        • Average Inventory: The average inventory level is derived from Quantity (Q) / 2, affecting storage costs.

    • As Quantity (Q) increases:

      • Ordering Costs Decrease: Fewer orders are made throughout the year, reducing transaction costs.

      • Holding Costs Increase: As more inventory is held, costs related to storage, insurance, and deterioration rise.

      • Total Cost Graph Dynamics: The total cost associated with inventory shows a U-shape curve; the lowest point indicates the optimal order quantity (EOQ), which balances both ordering and holding costs.

  • EOQ Formula Derivation:

    • EOQ Equation:

      • EOQ = √((2 × D × S) / H)

      • Where:

        • D: Annual Demand (total units expected to be sold)

        • S: Cost per Order (total cost incurred each time an order is placed)

        • H: Holding Cost per Unit (cost of holding one unit of inventory over a specific period).

    • Assumptions for EOQ Model:

      • Demand remains constant and known throughout the year.

      • Lead time for receiving new orders is consistent and known.

      • No discounts are offered for larger quantities purchased, keeping unit prices stable.

      • Only a single SKU (Stock Keeping Unit) is considered for simplicity.

      • The budget is unlimited for payments and purchasing.

      • Both holding and ordering costs remain constant, ensuring stability in calculations.

      • At the EOQ, the cost of ordering is equal to the cost of holding inventory, creating an optimal balance.

  • Reorder Point Calculation:

    • Reorder Point (ROP): Indicates when new inventory must be ordered to replenish stock before depletion.

    • ROP Formula: Calculated as:

      • ROP = Demand During Lead Time (the time it takes for new stock to arrive).

      • ROP Calculation: Can be represented as: ROP = Demand/Lead Time.

    • Importance of ROP: Ensures timely delivery and stock replenishment to avoid back orders or stock shortages, contributing to maintaining customer satisfaction.

  • Safety Stock Concept:

    • Purpose of Safety Stock: Acts as a buffer against variability in demand or lead times, ensuring availability even when unexpected events occur.

    • Buffer Insights: Provides additional coverage beyond the average inventory to prevent stockouts during fluctuations in demand or delays in supply chain processes.

    • Standard Deviation and Demand During Lead Time:

      • If both demand and lead time are variable, calculate safety stock using standard deviations, accommodating for unexpected increases in demand.

      • The objective is to establish service levels by determining necessary safety stock amounts based on the variability of the demand.

  • Normal Distribution and Stockouts:

    • Understanding Normal Distribution:

      • Mean represents the average outcome while standard deviation (σ) reflects variations from the mean.

      • In a normal distribution:

        • 68% of outcomes fall within ±1 standard deviation of the mean.

        • 95% of outcomes fall within ±2 standard deviations of the mean.

      • These statistical measures help establish safety stock needs based on standard deviation calculations, ultimately managing probabilities of stockouts effectively.

  • Z Scores and Service Levels:

    • Z Scores Explained: Correspond to specific desired service levels (e.g., achieving a 95% service level typically corresponds to a z score of approximately 1.65).

    • Application of Z Scores: Used to adjust safety stock calculations to meet desired stockout probabilities, indicating that a higher service level will necessitate a greater safety stock to minimize stockout risks.

  • Inventory Holding Costs:

    • Total Holding Costs Characterization:

      • The total holding cost encompasses both cycle stock and safety stock contributions.

      • Average Inventory for Cycle Stock: Calculated as Q/2, presents a standard way to project basic holding costs for regular inventory.

      • Safety Stock: Counts fully in calculations as it isn't segmented into cycles, emphasizing its cost burden completely on the inventory management.

  • Calculating Holding Costs Example:

    • When average demand is known and safety stock is established, overall holding costs can be projected:

      • Total Holding Cost = (Average Cycle Stock Cost + Safety Stock Cost).

  • Expected Demand and Forecasting:

    • Independent Demand Management: Often requires accurate forecasts, noting that these forecasts can introduce variances in expected versus actual demand outcomes.

    • Mean Absolute Deviation (MAD): A tool used to measure forecasting accuracy, serving a fundamental role in calculating standard deviations that are essential for determining safety stock levels based on demand forecasts.