Answer All Four Parts From Section A, One Question From Sect
Answer all FOUR parts from Section A, ONE question from Section B and ONE question from Section C
Answer all FOUR parts from Section A, ONE question from Section B and ONE question from Section C. All sections carry equal marks. All sources used, except for lecture materials, should be cited and referenced in a reference list (references do not count in the word limit). Lecture materials, if used, should not be cut and pasted but should be rewritten or redrawn. Any diagrams, figures or illustrations used from other sources should be referenced.
Sample Paper For Above instruction
Part A: Product Structure Diagram and Its Importance for MRP
The development of a product structure diagram (also known as a bill of materials or BOM tree) for an advanced smartphone involves detailing all components and their subassemblies, along with associated lead times. For the smartphone, the primary subassemblies include the display screen, battery, and camera modules. The display screen subassembly incorporates the display screen and four microprocessors, requiring 2 weeks for assembly. The battery assembly involves a lithium-ion battery with a 2-week lead time. The camera assembly includes two camera modules, taking 3 weeks. The main assembly combines these subassemblies along with the printed circuit board (PCB), two case and housing units, and a charger and accessories, with a lead time of one week.
The product structure diagram visually maps these relationships, illustrating how each subassembly feeds into the final product, including lead times. Such a diagram is vital for Material Requirements Planning (MRP) calculations because it helps identify when components need to be ordered or produced to meet production schedules. It clarifies dependencies, ensures timely procurement, and minimizes delays caused by component shortages, thereby supporting efficient inventory control and production flow management.
Part B: Inventory Management Calculation
Given the demand follows a normal distribution with a mean (μ) of 180 units and a standard deviation (σ) of 40 units, and with a lead time (L) of 1.5 months, the reorder quantity (Q) and reorder point (R) can be calculated using economic order quantity (EOQ) and safety stock principles.
First, the EOQ is computed:
\[ EOQ = \sqrt{\frac{2DS}{H}} \]
where D = annual demand, S = ordering cost, H = holding cost per unit per year.
Annual demand:
\[ D_{year} = 180 \text{ units/month} \times 12 = 2160 \text{ units} \]
Holding cost per unit per year:
\[ H_{\text{annual}} = 4 \times 12 = £48 \]
Ordering cost:
\[ S = £250 \]
Reorder quantity:
\[
Q^* = \sqrt{\frac{2 \times 2160 \times 250}{48}} \approx \sqrt{\frac{1,080,000}{48}} \approx \sqrt{22,500} \approx 150 \text{ units}
\]
Safety stock is determined using the service level (97%), corresponding to a z-score of approximately 1.88:
\[
SS = z \times \sigma_{L}
\]
where
\[
\sigma_{L} = \sigma \times \sqrt{L} = 40 \times \sqrt{1.5} \approx 40 \times 1.225 = 49
\]
\[
SS = 1.88 \times 49 \approx 92 \text{ units}
\]
Reorder point:
\[
R^* = \text{Demand during lead time} + SS = 180 \times 1.5 + 92 = 270 + 92 = 362 \text{ units}
\]
Thus, the company should reorder approximately 150 units when stock drops to about 362 units to achieve the desired service level.
Part C: Flow Shops Versus Job Shops
Flow shops are production systems where products follow a fixed, linear sequence of operations, typically with standardized products and a dedicated route. They are easier to plan and manage because the process flow is predictable, capacities are well-defined, and scheduling can be streamlined. For example, an automobile assembly line, where every car moves through predefined stages such as chassis assembly, painting, and interior installation, exemplifies a flow shop.
In contrast, job shops handle customized or small batch production, with flexible setups and varied routes based on specific customer requirements. Each job may require a different sequence of operations and routings, making planning and scheduling more complex. For example, a machine shop producing custom metal parts for different clients faces varying job sequences, complicating capacity management.
The simplicity of flow shop management stems from uniform workflows and predictable cycle times, enabling efficient scheduling. Conversely, job shops require complex scheduling algorithms, frequent adjustments, and detailed planning to accommodate job variability, increasing management complexity.
Part D: Capacity Estimation and Factors
To effectively estimate capacity over the next three months, the food company should analyze historical production data, considering average output levels, machine availability, and workforce productivity. Key factors include equipment uptime, maintenance schedules, employee shifts, and potential bottlenecks in the mixing, baking, and packaging stages.
The company should conduct a capacity review by calculating the maximum theoretical capacity (based on machine cycle times and staffing levels) and adjusting it downward for planned maintenance, absenteeism, and unforeseen disruptions. It is also important to factor in demand fluctuations, seasonal effects, and the introduction of new products or process improvements.
Moreover, assessing workforce availability, skill levels, and overtime possibilities can help refine capacity estimates. By integrating these considerations into a rolling forecast model, the company can develop a realistic workload plan, optimize resource utilization, and ensure it meets the projected demand efficiently.
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Section B: Time Series Forecasting and Inventory Management in Furniture Retailing
The quarterly sales data for Trent Homes, a furniture retailer, over the past three years reveals significant insights into its demand patterns. The data, when analyzed through simple moving averages (SMA) and exponential smoothing (ES), indicates limitations and suitability issues for these methods.
Applying SMA smooths short-term fluctuations but assumes flat demand over the averaging period, making it less effective for data exhibiting trends or seasonality. For instance, if sales demonstrate upward or downward trends or seasonal peaks, SMA often lags, providing inaccurate forecasts. Similarly, ES assigns exponentially decreasing weights to older data, which suits data with no strong trend or seasonal component but may underperform in the presence of such patterns.
Given the sales pattern exhibits trends and potential seasonality, neither SMA nor standard ES without seasonal adjustments would produce reliable forecasts. More advanced methods such as Holt-Winters exponential smoothing with seasonality adjustments or ARIMA models should be considered for better accuracy.
Calculating one-step-ahead forecasts using Holt’s method involves updating the level and trend components iteratively:
\[
S_t = \alpha \times y_t + (1 - \alpha)(S_{t-1} + G_{t-1})
\]
\[
G_t = \beta \times (S_t - S_{t-1}) + (1 - \beta) G_{t-1}
\]
where \( \alpha=0.3 \), \( \beta=0.1 \), and initialized at \( S_4=35 \), \( G_4=6 \).
Using these, forecasts for quarters 6-10 are computed sequentially, with each subsequent forecast derived from updated level and trend estimates. For example, forecast for quarter 6:
\[
\hat{y}_6 = S_5 + G_5
\]
Similarly, MAD and MAPE provide measures of forecast accuracy:
\[
MAD = \frac{1}{n} \sum | y_t - \hat{y}_t |
\]
\[
MAPE = \frac{100}{n} \sum \frac{| y_t - \hat{y}_t |}{y_t}
\]
Calculations indicate the forecast errors, with lower MAD and MAPE values implying better forecast performance. For predicting quarter 13, the last estimated level \( S_5 \) and trend \( G_5 \) are used in Holt’s equation to generate the forecast.
The forecast for quarter 13 at quarter 10 demonstrates the model's predictive capacity. However, forecast inaccuracy can occur due to unforeseen demand variations, shifts in consumer preferences, or data anomalies. To enhance forecast reliability, integrating seasonal ARIMA models or adjusting Holt’s parameters based on recent demand patterns might be effective.
Section C: Operational Strategies for Diverse Product Companies
Company A, specializing in high-cost, customized refrigeration systems, benefits from a Make-to-Order (MTO) approach. Each system is bespoke, designed according to specific customer requirements, often involving complex engineering and long lead times. This approach reduces inventory costs but requires flexible manufacturing and detailed planning. Company B, producing sofas, armchairs, and suites with numerous variants, is suitable for an Assemble-to-Order (ATO) strategy, where components are stocked, and final assembly is triggered by customer orders, balancing inventory investment with customization flexibility. Company C, which mass-produces flavored milk drinks, aligns with a Make-to-Stock (MTS) approach, manufacturing based on forecasted demand to maintain high service levels with minimal lead times.
Assumptions include the degree of customization, lead times, and inventory holding costs; further information such as demand variability, production lead times, and customization levels would refine strategy choices. Additionally, understanding each company's supply chain flexibility, lead time constraints, and customer service expectations is essential before finalizing operational approaches.
Customer Order Decoupling Point (CODP) Differences
The CODP marks where postponement in production occurs, aligning inventory positioning with customer demand characteristics. For Company A, the CODP is at the engineering or design stage, since production begins after order confirmation. For Company B, the CODP is at component assembly, which can be stocked in advance, but final assembly depends on customer orders. For Company C, CODP is at the point of manufacturing after forecasting; finished products are stocked, and customer orders specify delivery times.
Diagrams would illustrate these points, showing the flow from raw materials to finished goods, and indicating the decoupling point where customer orders influence production. For Company A, the CODP is at the engineering phase; for B, at component assembly; and for C, after finished goods are stocked and dispatched based on forecasts.
Section C: Maximizing Throughput Through Constraints Management
Speciality Materials UK faces capacity constraints primarily at the baking oven stage. Implementing a Theory of Constraints (TOC) approach using Drum-Buffer-Rope (DBR) principles involves identifying the constraint (the oven), establishing a drum (his pace for production), and regulating flow with buffers. The drum ensures production aligns with oven capacity, while buffers protect against variability, and the rope synchronizes upstream processes to prevent overproduction.
Applying DBR includes determining the buffer size based on demand variability and processing times, scheduling jobs to the constraint to avoid idle time, and controlling work in progress to maximize throughput. Regularly monitoring the constraint’s utilization allows the company to identify potential improvements, such as adding capacity or optimizing existing processes.
To handle erratic demand for coated products, the company should work collaboratively with customers by establishing stable order commitments, implementing demand smoothing techniques, and possibly creating safety stock for high-margin items. This partnership approach reduces order variability, improves supply chain stability, and ensures profitable product availability.
References
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