Inventory , Forecasting, Quality Management
- 1. 1 . – – – – 2 . – – – – – 3 . – – – – Inventory: - What is the recommended system for ordering FMCG products from a grocery store and why? Ans: The Economic Order Quantity (EOQ) model is recommended for ordering FMCG products from a grocery store because: Cost Efficiency: It helps save money by figuring out the best quantity to order, balancing ordering costs and holding costs. Optimal Inventory Levels: EOQ ensures stores have just the right amount of products, preventing wastage from overstocking or disappointing customers due to stockouts. Improved Stock Management: It tells stores when and how much to reorder, making sure shelves are always stocked without overcrowding storage spaces. Better Customer Service: With EOQ, stores can keep popular items in stock, making customers happy by always having what they need. What are the benefits of using the P system for ordering multiple products at the same time? Ans: Practicality: It's useful when you can't keep a close eye on inventory all the time, making it great for situations where monitoring constantly is hard. Administrative Convenience: You only need to count inventory before the next review time, making it easier administratively compared to always checking. Suitability for C-Items: It's better for managing low-cost items with steady demand. Safety Stock Management: You order enough to reach a target inventory level, which helps manage safety stock well. Reduced Monitoring: You only check inventory during review times, so you don't have to watch it all the time. Protection Against Shortages: You can keep more safety stock, preventing shortages during order times, and ensuring a steady supply. How can companies ensure inventory accuracy? Ans: Cycle Counting: Regularly count small parts of inventory to catch mistakes. Using Technology: Use scanners or special software to track inventory accurately and reduce human errors. Standard Procedures: Have clear rules for receiving, storing, and picking items to avoid mistakes and keep things consistent. Training Staff: Teach employees how to manage inventory correctly and stress why it's important. Regular Checks: Check inventory records against actual counts now and then to find and
- 2. – – . – – – . – – – – – – – – – . – – fix mistakes quickly. ABC Analysis: Focus on managing valuable items more carefully to ensure accuracy where it matters most. When carrying costs are a percentage of the price, what is the best purchase quantity? Ans: Start with the cheapest price: Look at the lowest-priced option first. Find the minimum point: Calculate the minimum amount to buy at that price. If it fits within the quantity range for that price, great! Check feasibility: If the minimum point isn't possible with the lowest price, compare the total costs at all lower prices. Choose the cheapest option: Pick the quantity that gives the lowest total cost among all the price options. What is the difference between the fixed period model and the periodic review system? Ans: Fixed period Model (P-Model): Description: Inventory is only checked at certain times, not all the time. Ordering: Orders are made to fill up the inventory to a certain level, depending on how much is left. Advantages: Good when it's hard to watch inventory all the time, easy to handle, and works well for managing less important items. Disadvantages: No checking of inventory during the waiting time, might run out of stock, needs more safety stock to cover shortages. Periodic Review System: Description: Inventory levels are looked at regularly, and orders are made based on that. Ordering: Orders happen at fixed times, regardless of how much inventory is left at the moment. Advantages: It makes it easy to manage inventory by checking and ordering at set times. Disadvantages: Might end up with too much inventory because orders are made on a schedule, which could cost more to store. In the Q-model, what is the optimal number of orders and time between orders? Ans: Optimal Number of Orders: Divide the total annual demand by the Economic Order Quantity (EOQ). This tells you how many times you should order throughout the year to keep costs low. Time Between Orders: Divide the EOQ by the annual demand. This gives you the interval between each order, helping you keep inventory levels right and costs down. By using these calculations, companies can manage their inventory well, save money, and
- 3. – . – – – . – – . – – – – . – – make sure they're restocking efficiently. How can a company determine the reorder point using the EOQ method? Ans: Calculate EOQ: Find out how much to order each time to minimize costs using the formula EOQ = √((2DS)/H), where D is annual demand, S is ordering cost per order, and H is annual holding cost per unit. Determine Lead Time Demand: Figure out the average demand during the time between ordering and receiving. Calculate Safety Stock: Find the extra stock needed to cover for unexpected demand or delays using the formula Safety Stock = Z × σdLT, where Z is the number of standard deviations and σdLT is the standard deviation of demand during lead time. Find Reorder Point (ROP): Figure out when to place a new order by adding the expected demand during the lead time and the safety stock. ROP = (Expected demand during lead time) + Safety Stock. What is the total annual cost for the fixed order quantity model? Ans: Total Cost Formula: Use the formula TC = PD + (D/Q) × S + (Q/2) × H. Plug in Values: Put in the numbers for demand, unit cost, order quantity (EOQ), ordering cost, and holding cost. TC = Total annual cost D = Demand P = Unit cost Q = Quantity to be ordered (Economic Order Quantity) S = Ordering cost or set-up cost H = Annual holding or storing cost per unit of average inventory How does the value of Q change when purchasing costs are added? Ans: Adding purchasing costs doesn't change the value of EOQ. EOQ is found based on demand, ordering costs, and holding costs. EOQ stays the same because it's about finding the best order quantity to minimize costs. Including purchasing costs doesn't affect this calculation. So, the value of Q, which is the optimal order quantity, remains unchanged. What is Little's Law and how does it relate to average inventory? Ans: Little's Law connects average inventory, flow time, and demand rate. It says average inventory = average flow time × average demand rate. This means the amount of stuff you have equals how long it takes stuff to move through the system times how much stuff you need.
- 4. – . – – – – – – – – . – – – – – – – – – . – – By using Little's Law, businesses can make sure they have the right amount of inventory, make processes smoother, and work better overall. How does increasing demand affect the optimal lot size and number of orders per year? Ans: Optimal Lot Size: Higher demand means a bigger optimal lot size. EOQ increases with demand to efficiently meet higher demand. EOQ minimizes total inventory costs by balancing ordering and holding costs. Number of Orders per Year: More demand leads to more orders per year. EOQ is calculated from demand, ordering costs, and holding costs. Higher demand requires more frequent orders to meet demand while keeping costs low. More orders are needed to manage inventory efficiently as demand increases. What can cause stock-out occurrences and excessive consumption during the lead time? Ans: Variability in Demand: Fluctuations in customer demand can cause sudden increases in orders, leading to stock-outs if inventory levels aren't managed well. Supplier Delays: Delays in the supply chain, like late deliveries from suppliers, can cause stock-outs if the expected inventory doesn't arrive on time. Inaccurate Forecasting: Poor predictions of demand or lead times can result in not having enough inventory, causing stock-outs when demand is higher than expected. Unforeseen Events: Unexpected events such as natural disasters in the supply chain can disrupt inventory flow, leading to stock-outs if there are no backup plans. Excessive Consumption: Lack of control over inventory usage or inefficiencies in production can lead to excessive consumption of inventory, causing stock-outs during lead time. How can lead time be stretched and why is this detrimental to inventory management? Ans: Increased Safety Stock: Longer lead times mean a higher risk of running out of stock during the extended period.
- 5. – – – – – – – – – – – – – . – – – – – – – – – – – . period. To avoid this risk, businesses may need to keep more safety stock, which costs more and ties up capital. Reduced Responsiveness: Longer lead times make it harder to adjust inventory to changes in demand or supply chain problems. This lack of flexibility can lead to too much or too little inventory, affecting customer satisfaction and efficiency. Higher Holding Costs: Longer lead times mean items are stored for longer, increasing holding costs like warehousing and insurance. This can eat into profits and use up resources that could be spent better elsewhere. Impact on Working Capital: Longer lead times tie up working capital in extra inventory, reducing cash flow and limiting investment in other parts of the business. Operational Inefficiencies: Stretching lead times can mess up production schedules, delay orders for customers, and create problems in the supply chain. This causes inefficiencies and higher costs in running the business. Forecasting: - What are the four components of time series decomposition and how do they impact forecasting? Ans: Base Demand: Basic level of demand without any specific trends or patterns. Represents the underlying demand level. Trend: Long-term movement in the data, showing overall direction over time. It can be upward or downward, indicating growth or decline. Seasonality: A regular pattern of demand fluctuations over short periods. Repeats at specific intervals like daily, weekly, or monthly cycles. Cycles: Long-term patterns in the data repeat over extended periods. Span years or decades, representing broader economic or business cycles. What is the difference between trend and seasonality in forecasting? Ans: Trend:
- 6. – – – – – – – – – . – – – – – – – – – . – – – – . – – Long-term movement in data, either up or down, over an extended time. Shows the overall direction of data over time. It helps understand general growth or decline in data. Seasonality: A repeated pattern of increases or decreases in demand over short periods. Occurs at specific intervals like daily, weekly, or monthly cycles. Characterized by consistent patterns that repeat within a given time frame. Trend: Shows the overall direction of data over a long time. Seasonality: Repeated patterns in data over short periods at specific intervals. How do cyclical patterns differ from seasonal patterns in forecasting? Ans: Seasonal Patterns: Short-term, predictable fluctuations in data. Occur over short periods like daily, weekly, or monthly cycles. Influenced by factors like weather, holidays, or cultural events. Repeat within a specific time frame, typically within a year. Cyclical Patterns: Long-term regular patterns in data. Span extended periods, usually years or decades. Associated with broader economic or business cycles. Movements extend over multiple years and impact data over a longer timeframe. What is the role of randomness in time series forecasting? Ans: Unpredictable Movement: Refers to unpredictable changes in data over time, not following any specific pattern. Residual Variations: Represents fluctuations in data that cannot be explained by trends, seasonality, or cyclical behaviours. Uncertainty: Adds uncertainty to forecasting models, making it difficult to accurately predict future values. Emphasizes Unpredictability: Highlights the inherent unpredictability in certain aspects of the data, even after accounting for known patterns. Affects Accuracy: Understanding and accounting for randomness are crucial for improving forecast accuracy by acknowledging unexplained fluctuations. What are the two general types of forecasting methods and how do they differ? Ans: Qualitative Techniques: Relies on experience, judgment, and intuition. Used when historical data are not reliable or available.
- 7. – – – – – – . – – – – – – – – – – – – – – . – – – – – – – Suitable for scenarios like new product introductions or technology adoptions. Examples include grassroots, market research, panel consensus, and the Delphi method. Quantitative Techniques: Uses historical, measurable data and mathematical models. Relies on analyzing past data to predict future outcomes. Examples include time series methods (naive approach, moving averages, exponential smoothing) and causal forecasting methods (regression analysis). How can quantitative and qualitative information be used together to improve forecasting accuracy? Ans: Comprehensive Analysis: Combine numerical forecasts with insights from experts or stakeholders. Gain a complete understanding of factors affecting future outcomes. Scenario Planning: Create multiple scenarios considering various possibilities and uncertainties. Better prepare for different potential outcomes. Expert Judgment: Include expert opinions or market insights in quantitative models. Consider factors not captured in historical data alone. Validation and Calibration: Validate quantitative forecasts with qualitative feedback. Adjust models for biases or limitations, ensuring more accurate predictions. Continuous Monitoring: Regularly compare forecasted outcomes with actual results. Refine models based on qualitative feedback, improving accuracy over time. What are the three laws of forecasting and how do they impact the accuracy of forecasts? Ans: First Law: Forecasts are always wrong. Second Law: Detailed forecasts are worse than aggregate forecasts. Third Law: The longer the forecast horizon, the worse the forecast. First Law: Recognizes that forecasts are always wrong due to basic uncertainty. Improves accuracy over time by adapting to changing circumstances. Second Law: Suggests that detailed forecasts can be less accurate than aggregate forecasts.
- 8. – – – – – . ◆ ◆ ◆ ◆ ◆ – ◆ ◆ ◆ ◆ ◆ ◆ – ◆ ◆ ◆ ◆ ◆ . – – – – – – – Overly complex models or excessive granularity may lead to less reliable predictions. Third Law: States that longer forecast horizons tend to result in less accurate forecasts. Accuracy decreases as the forecast extends further into the future due to the unpredictability of future events. What are the three time horizons in forecasting and what types of decisions are typically made within each horizon? Ans: Long Range (> 2 years): Creating a new facility Expanding an existing facility Developing new products or services Developing a new technology Designing a supply chain Medium Term (3 months – 2 years): Aggregate planning Sales planning Material planning Workforce levels Overtime Supply chain decisions Short Range (< 3 months): Job scheduling Inventory management Production levels Adjustment of workforce levels Procurement of materials How can the simple moving average and weighted moving average methods be used to forecast demand? Ans: Simple Moving Average: Method: Calculates the average of a set number of past data points. Uses this average to forecast future demand. Application: Calculate average demand over a specific number of periods. Use this average as the forecast for the next period. Update the average with each new period's demand data.
- 9. – – – – – – – – – – – – – . – – – – – – – . – – – – – – – – Advantages: Simple and easy to calculate. Smooth out random fluctuations in demand data. Weighted Moving Average: Method: Assign different weights to past data points based on their importance. Uses weighted averages to forecast future demand. Application: Assign higher weights to more recent data points. Multiply each demand value by its weight and sum these products for the forecast. Adjust weights based on the importance of recent versus older data. Advantages: Allows for more emphasis on recent data, making forecasts more responsive. Provides flexibility in adjusting weights to reflect the significance of different periods. What is the thumb rule for weighted moving average and how does it impact forecasting accuracy? Ans: Weighted Moving Average: Rule: Assign unequal weights to each component, ensuring the sum equals 1. Give more stress on recent data. Impact on Forecasting Accuracy: Emphasizes recent data, making the forecast more responsive to recent trends. Allows for more accurate forecasts reflecting current market conditions. Improves decision-making in inventory management, production planning, and resource allocation. What is exponential smoothing and why is it a commonly used forecasting technique? Ans: Emphasis on Recent Data: Gives more weight to recent observations. Quickly adapts to changes in trends or patterns. Suitable for forecasting in dynamic environments. Simplicity and Ease of Use: Relatively simple to understand and implement. Requires minimal computation. Accessible to users with varying statistical expertise. Accuracy:
- 10. – – – – – – – – – . – – – – – – – – – – – – ● . – – – Provides accurate forecasts, especially for short-term fluctuations. Reflects the most current information available. Flexibility: Adjusts the level of smoothing through a smoothing constant (alpha). Controls the balance between responsiveness to recent data and forecast stability. Low Computational Requirements: Requires little computational effort. Efficient in terms of computer storage and processing power. A practical choice for organizations with limited resources. How does exponential smoothing differ from other forecasting methods in terms of emphasis on past data? Ans: Emphasis on Recent Data: Gives more weight to recent observations. Gradually decrease the influence of older data points. More responsive to changes in trends or patterns. Adaptability: Quickly adapts to new information. Captures short-term fluctuations effectively. Balance between Responsiveness and Stability: Strikes a balance by prioritizing recent data. Provides stable forecasts while being responsive to changes. Effectiveness: A popular and effective technique in forecasting demand and sales. Reflects the most current market conditions. Quality Management: - Quality Management History: Describe the historical roots of quality management post-World War II and how Japanese industry adopted US manufacturing practices. Ans: After World War II, Japanese industry adopted US manufacturing practices to rebuild and enhance industrial capabilities. Embraced work of Joseph Juran and W. Edwards Deming. Juran emphasized firm culture and management's role in quality. Deming developed Statistical Process Control (SPC).
- 11. – – – . – – – – – – – – ● . – – – – – – – – – – – – – – – – – Japanese companies like Toyota adopted advanced quality management systems, such as the Toyota Production System (TPS). Motorola introduced Six Sigma in the late 1980s, focusing on quality to reduce costs. Japanese practices set new global standards for quality management. Explain the contributions of Joseph Juran and W. Edwards Deming to quality management. Ans: Joseph Juran: Emphasized firm culture and management in ensuring quality. Believed poor quality often stemmed from management and organizational culture issues. W. Edwards Deming: Developed Statistical Process Control (SPC) for industries. Focused on reducing variability in production processes to enhance quality. Contributions: Both promoted the idea that quality improvement leads to cost reduction. Their approaches shaped modern quality management practices globally. Dimensions of Quality: List and define the eight dimensions of quality for manufactured products. Ans: Performance: Primary characteristics determine how well a product functions. It's about how effectively the product does its job. Features: Secondary characteristics add extra value to the product. Enhance its appeal and usefulness but aren't essential for basic functionality. Reliability: Consistency of the product's performance over time. It's about how dependable the product is and its ability to work without fail. Conformance to Standards: Meeting the specified design and quality standards during development. Ensures the product meets the requirements it was designed for. Durability: Lifespan and how long the product lasts before needing replacement. Indicates how sturdy and long-lasting the product is. Serviceability: Ease and speed of repairing the product if it breaks down. How quickly and effectively it can be fixed when something goes wrong.
- 12. – – – – – – . – – – – – – – ● . – – – – Aesthetics: Look, feel, and sound of the product. Adds to its overall appeal and attractiveness. Perceived Quality: Influenced by factors like past performance, reputation, and customer recognition. Shapes how customers view the overall quality of the product, even beyond its tangible features. Discuss the importance of focusing on specific quality dimensions that align with customer expectations. Ans: Customer Satisfaction Focus: Prioritize quality dimensions that matter most to customers. Enhanced Loyalty: Meeting customer expectations leads to increased customer satisfaction and loyalty. Competitive Edge: Aligning with customer needs helps companies stand out in the market. Tailored Products/Services: Customizing offerings based on customer preferences boosts satisfaction. Efficient Resource Allocation: Invest resources where they impact customer satisfaction most. Optimized Operations: Streamlining processes based on customer-centric quality dimensions improves efficiency. Cost Reduction: Addressing quality issues relevant to customers reduces unnecessary expenses. Strategic Decision: Aligning with customer expectations is key for long-term success and market leadership. Quality Gurus: Identify key figures in quality management like Walter Stewart, Deming, Juran, Feigenbaum, Crosby, Ishikawa, and Taguchi. Ans: Walter Shewart: Developed control charts, essential tools in statistical quality control. W. Edwards Deming: Known for his 14-point philosophy and emphasis on statistical process control. Joseph Juran: Introduced the "fitness for use" concept and stressed management's role in quality. Armand V. Feigenbaum: Introduced Total Quality Control (TQC), emphasizing quality at all levels. Philip Crosby: Advocated for "quality is free" and zero defects, focusing on cost-saving
- 13. – – – . – – – – – – – – ● . – – – – – – – benefits. Kaoru Ishikawa: Developed Ishikawa or fishbone diagram for problem analysis in quality. Genichi Taguchi: Introduced Taguchi methods, aiming for robust design and quality engineering. Explain the philosophies or contributions of at least three quality gurus mentioned. Ans: W. Edwards Deming: Contribution: Introduced the 14-point philosophy and emphasized statistical process control (SPC) in industries. Philosophy: Advocated for continuous improvement, reducing process variability, and fostering a quality-focused culture in management. Joseph Juran: Contribution: Introduced the concept of "fitness for use" and emphasized management's role in quality. Philosophy: Believed quality issues stem from management and organizational culture deficiencies, emphasizing meeting customer expectations and structured quality management. Philip Crosby: Contribution: Advocated for "quality is free" and zero defects, emphasizing cost- saving benefits. Philosophy: Stressed preventing defects over correcting them later, with zero defects as a goal and a proactive approach to quality management. Costs of Quality: Define Prevention Costs, Appraisal Costs, Internal Failure Costs, and External Failure Costs. Ans: Prevention Costs: Definition: Expenses to stop defects before they happen by identifying and fixing root causes of quality issues. Examples: Planning, training, better equipment, working with suppliers, process redesign. Appraisal Costs: Definition: Costs to assess materials and processes during production to ensure quality standards. Examples: Inspecting materials, testing products, calibrating equipment. Internal Failure Costs: Definition: Expenses from fixing defects found before products are shipped to customers.
- 14. – – – – . – – – – – – – ● . – – – – . Examples: Rework, scrap, downtime, retesting. External Failure Costs: Definition: Expenses from defects discovered after products are delivered to customers. Examples: Warranty claims, product recalls, handling returns, addressing customer complaints. Calculate the Cost of Quality using the provided formula and explain the significance of each cost component. Ans: External Failure Costs (Ce): Significance: Expenses from defects reaching customers, emphasizing the need for high- quality products to avoid dissatisfaction, warranty claims, and damage to brand reputation. Internal Failure Costs (Ci): Significance: Expenses from defects found before products reach customers, underscoring the importance of effective quality control to rectify issues before impacting customers. Appraisal Costs (Ca): Significance: Essential for ensuring products meet quality standards through inspection and testing, maintaining product integrity and customer satisfaction. Prevention Costs (Cp): Significance: Focus on proactive measures to prevent defects, reduce occurrences, enhance product reliability, and minimise rework or scrap. Statistical Process Control (SPC): Define SPC and its objectives in monitoring production processes. Ans: Quickly Detect Assignable Causes: SPC helps identify reasons for process variations swiftly, allowing for timely corrective actions to maintain quality. Reduce Variability: SPC aims to minimize variations in production processes by analyzing data, leading to more consistent outcomes. Improve Process Consistency: Monitoring process performance ensures stability and reliability, resulting in consistent product quality. Enhance Product Quality: Continuous monitoring and control of processes help in identifying deviations and maintaining high-quality standards. Prevent Non-Conforming Units: SPC tools aid in proactively identifying and addressing deviations, preventing the production of defective products and reducing waste. Describe the tools used in SPC, such as Control Charts for variables and
- 15. . – – – ● . – – – – – – – – – – – – – – attributes, and their role in quality control. Ans: Control Charts for Variables: These charts monitor continuous characteristics like weight or time. The X-bar chart tracks the process average, while the R-chart monitors process variability. Control Charts for Attributes: Used for non-numeric characteristics like pass/fail. P-chart tracks the proportion of defects, while the C-chart counts defects. Statistical Process Control (SPC) Tools: Include control charts, histograms, and stem- and-leaf displays. They help observe process performance and detect variations. Process Capability Analysis: Measures how well a process meets specifications. Cp compares process spread to the tolerance range, while Cpk considers process centrality. Process Capability: Explain Process Capability Ratio (Cp) and Process Capability Index (Cpk) and their importance in assessing process capability. Ans: Process Capability Ratio (Cp): Cp measures how well a process can meet design specifications by comparing the process spread to the tolerance range. Formula: Cp = (Upper Specification Limit - Lower Specification Limit) / (6 * Standard Deviation) Importance: If Cp < 1, the process exceeds the tolerance range, indicating an inconsistency in meeting specifications. Cp = 1 means the process range matches the tolerance range. Ideally, Cp > 1 (preferably > 1.33) indicates the process is well within specifications. Process Capability Index (Cpk): Cpk assesses both process variability and centrality, indicating how well the process output is centred within specifications. Formula: Cpk = min((USL - Process Mean) / (3 * Standard Deviation), (Process Mean - LSL) / (3 * Standard Deviation)) Importance: If Cpk equals Cp, the process is centred at the midpoint of specifications. Cpk < Cp indicates an off-centre process, affecting its capability to meet specifications consistently. Provides a comprehensive view of process capability by considering variability and centrality. Importance of Cp and Cpk:
- 16. – – . – – – – – – – – – – – – – – – – – – – Quality Assurance: Helps assess process consistency in meeting customer requirements. Performance Monitoring: Enables continuous monitoring for quality improvements. Outline the sequential steps involved in determining process capability using Cp and Cpk metrics. Ans: To calculate Process Capability Ratio (Cp): Determine the Upper Specification Limit (USL) and Lower Specification Limit (LSL) base formula: Cp = (USL - LSL) / (6 * σ). Interpret the Cp value: If Cp < 1, the process range exceeds specifications. Cp = 1 indicates the process range matches specifications. Ideally, Cp > 1 (preferably > 1.33) signifies the process is within specifications. To calculate the Process Capability Index (Cpk): Determine the Process Mean (µ). Calculate Cpk using the formula: Cpk = min((USL - µ) / (3 * σ), (µ - LSL) / (3 * σ)). Interpret the Cpk value: If Cpk equals Cp, the process is centred at the midpoint of specifications. If Cpk is less than Cp, the process is off-centre, affecting capability. Sequential Steps: Calculate Cpk to assess process centrality. Calculate Cp to evaluate process variation. Compare Cp and Cpk values: If Cp > 1 and Cpk = Cp, the process is centred and capable. A Cp value above 1.33 is desirable. Calculate the Standard Deviation (σ) of the process. Use the on-design specifications.