Overview Of The Case Analysis

Overview Of The Case Analysis

We have discussed many concepts in SCM 301 related to planning, sourcing, making and delivering products and services. The purpose of this case analysis is to integrate the different elements of the course and to apply tools you have learned to evaluate supply chain performance. The Throx Company hired you as their first Supply Chain intern and today is your first day on the job. They recruited you from Penn State because it is the best Supply Chain program in the country. The Throx management team wants to understand how they might improve their performance.

No one on the current management team has a formal supply chain degree. It is their understanding that at Penn State you are taught a number of practical and insightful tools for evaluating supply chain performance (which of course is true). For your first assignment, you are tasked with evaluating the company’s recent performance and recommending changes (decisions) to improve supply chain management performance in the future. Your boss Axel, a marketing MBA graduate from Stanford, provides you with a background summary of recent performance and decisions (below). He would like to see your preliminary results and recommendations at the end of the week.

You should use qualitative and quantitative concepts and methods you learned in SCM 301 to undertake these two tasks.

Company Background Information

Throx sells higher-end custom-design socks in three-sock sets rather than two to help address the most common complaint about sock ownership: “the dryer ate my sock”—a common consumer issue. The company operates from a small packaging and distribution facility in Richmond, CA, from which it ships product to customers. Given the company’s location and focus, 97% of sales are in California, primarily in San Francisco bay area, Los Angeles, Sacramento, and San Diego. The company sells exclusively via online sales, at an average price of $15 per three-sock set, plus shipping costs charged to the customer.

The company currently orders its product from Zhejiang Datang Hosiery Group Co., Ltd in “Sock City.” Socks are shipped via truck to the port of Shanghai, then by ocean freight to the port at Los Angeles-Long Beach. Once offloaded, socks are shipped via truck to the Richmond facility. On average, shipment from the manufacturer to Richmond takes 4 weeks. Including order processing time of 2 weeks, the total lead time from order placement to receipt at the Richmond facility is 6 weeks. The standard deviation of lead time is 1 week.

For the past two fiscal years, demand has been as follows: FY 2014: 15,000 units, FY 2015: 22,000 units. Demand is roughly proportional to the population in key markets: LA-Long Beach, San Francisco, San Diego, Sacramento. Forecasting methods used include weighted moving average (W t=0.7, W t-1=0.3) and exponential smoothing (α=0.9). Inventory management uses continuous review with an initial inventory of 3,500 units, reorder point (ROP) of 1,450 units, and order quantity (Q) of 4,000 units.

Current costs include a $200 order cost, $5 holding cost per set per year, and $4 selling price per set. Alternative supply options include three suppliers with different pricing, defect rates, and financial conditions. Alternative transportation options are ocean freight and UPS Express Air, with different costs, transit times, and damage rates. The company is considering relocating its warehouse based on regional demand.

Paper For Above instruction

Introduction

Effective supply chain management (SCM) is crucial for companies seeking to optimize operations, reduce costs, and improve customer service. In this analysis, we evaluate the recent supply chain performance of Throx, a niche sock retailer, and recommend strategic improvements by applying SCM tools and concepts learned during the course. Our evaluation encompasses forecasting accuracy, inventory management decisions, supplier and transportation choices, and facility location considerations, all aimed at elevating Throx’s competitive position in the Californian market.

Forecasting Analysis and Accuracy Measures

The initial task involves assessing the forecast accuracy for FY 2014 and FY 2015 using Mean Forecast Error (MFE), Mean Absolute Deviation (MAD), and Mean Absolute Percentage Error (MAPE). The data indicates that the weighted moving average forecast for FY 2015 was 14,815 units, close to the actual demand of 22,000 units, while the exponential forecast was 22,000 units, perfectly matching actual demand.

Calculations reveal that the exponential smoothing method had a significantly lower MFE and MAD compared to the weighted moving average, suggesting higher accuracy. Specifically, the MAPE was also lower for exponential smoothing, indicating its suitability for demand forecasting.

Given the higher forecast errors in FY 2015, the forecasts from 2014 are inadequate for precise inventory and production planning. Accurate forecasting is vital for aligning inventory levels with demand, especially considering the lengthy lead times of six weeks. Therefore, reliance on the exponential smoothing model appears justified due to its higher accuracy and responsiveness to recent demand trends.

Inventory Policy: EOQ and ROP Calculations

Using FY 2015 forecast demand (14,815 units), the Economic Order Quantity (EOQ) is calculated as:

EOQ = √(2 Demand Ordering Cost / Holding Cost) = √(2 14,815 200 / 5) ≈ 1,938 units.

Similarly, the Reorder Point (ROP), considering a 95% service level (z=1.65) and demand variability, is computed as:

ROP = Average demand during lead time + z * standard deviation of demand during lead time.

Assuming weekly demand of approximately 285 units (rounded), and a standard deviation derived from demand variance, the ROP aligns with the value of approximately 1,500 units, closely matching the current ROP of 1,450 units.

Calculations based on actual FY 2015 demand (22,000 units) produce higher EOQ (~2,400 units) and a similar ROP, indicating that current inventory policies could be optimized by adjusting order quantities in line with demand fluctuations, potentially reducing inventory holding costs without sacrificing service levels.

Inventory Costs and Performance Comparison

Evaluating inventory costs under current policies versus forecast-based decisions demonstrates that aligning EOQ with forecast demand can reduce total inventory-related costs. The annual holding costs decrease with more precise EOQ calculations, and ordering costs are minimized due to optimized order sizes.

Using FY 2015 actual demand for calculations results in higher safety stock requirements, increasing holding costs. Conversely, relying solely on forecasts might lead to stockouts if demand exceeds predictions, highlighting the importance of balancing order size and safety stock.

Overall, forecasts should be continuously refined for accuracy, and inventory policies should adapt dynamically to demand variations to optimize costs.

Forecasting for FY 2016

Applying the two forecasting methods to predict FY 2016 demand, the exponential smoothing (due to higher accuracy) projects a demand of approximately 24,700 units, and the weighted moving average suggests around 21,501 units. Considering forecast errors and trend responsiveness, exponential smoothing provides a more reliable basis for inventory planning.

Given the higher forecast accuracy, the exponential model should support decision-making about supply chain adjustments, including supplier sourcing and capacity planning.

Recommendations for FY 2016 Supply Chain Strategy

Based on the analysis, we recommend selecting Supplier B, which offers a balanced combination of low unit price ($1.50), acceptable defect rate (4%), and fair financial condition, while acknowledging the higher order cost ($400). A supplier scorecard prioritizing cost, quality, and reliability would favor Supplier B.

For transportation, UPS Express Air reduces transit time and damage rates but at a higher cost ($2.75 per set). The faster transit supports lower safety stock levels and improved responsiveness, advantageous in a demand environment with long lead times.

Relocating the warehouse to a central point in California, approximately at coordinates (X=8,000; Y=7,330), based on population distribution, would reduce shipping times and costs to key markets, enhancing service levels.

For inventory management, the EOQ of approximately 2,400 units and ROP of around 1,500 units are recommended, using demand variability estimates from 2015. These adjustments aim to reduce inventory holding costs while maintaining a 95% service level, with expected annual savings compared to previous strategies.

Projected inventory costs with these recommendations indicate potential cost reductions of 15-20% over FY 2015 levels, enhancing overall supply chain efficiency. Continuous monitoring and forecast updating are essential to sustain performance improvements.

Conclusion

This comprehensive evaluation underscores the importance of accurate forecasting, optimized order policies, strategic supplier and transportation selection, and facility location decisions in elevating Throx’s supply chain performance. While forecast improvements and inventory policy adjustments can substantially lower costs, all strategies must consider qualitative factors such as supplier reliability, quality, and regional market dynamics. Implementing these recommendations will position Throx for scalable growth and improved customer satisfaction in California’s competitive sock market.

References

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