Consider The Following Information Regarding The Expected De ✓ Solved

Consider the following information regarding the expected de

Consider the following information regarding the expected demand for November 2020 and all costs involved in the operation at HP Production Plant (all prices in Euros). Products: HP Pavilon and HP Spectre. Demand (units): 600 and 750 respectively. Working days: Monday to Saturday (assume real working days in November 2020). Hours per shift: 6 hours each shift. Number of stations: 7. Number of workers required per station: 1 (assume workers cannot help others since the process is made by a robot). Wages: 950 per worker monthly. Sales price per unit: 1,000 and 1,400 respectively. Raw material cost per unit: 390 and 410 respectively. Utilities, maintenance and other expenses: 660 per hour (assume the company will work 24 hours a day, Monday to Saturday). Depreciation cost: 4,400 per station monthly. Administrative expenses: 255,000 monthly. Advertising costs: 6% of total sales. Insurance costs: 4,000 per month. Process time (in minutes) per station per product: Station A: HP Pavilon 5, HP Spectre 3. Station B: HP Pavilon 3, HP Spectre 5. Station C: HP Pavilon 2, HP Spectre 1. Station D: HP Pavilon 1, HP Spectre 2. Station E: HP Pavilon 2, HP Spectre 2. Station F: HP Pavilon 1, HP Spectre 1. Station G: HP Pavilon 3, HP Spectre 3. Rejection rates per station: A 5%, B 7%, C 5%, D 1%, E 8%, F 10%, G 15%. Availability rates per station: A 98%, B 95%, C 95%, D 99%, E 99%, F 96%, G 92%. Performance rates per station: A 96%, B 97%, C 90%, D 100%, E 100%, F 98%, G 97%. Given the previous information, answer the following questions: 1 Calculate the takt time to produce all the required units (in minutes). 2 Calculate the inputs table showing the production rate in minutes (minutes per unit). 3 Calculate the OEE per station. 4 Calculate the production table per product per process required to comply with the demand (required time per product per station). 5 Calculate the total required capacity per station. 6 Calculate the available time. 7 Calculate the capacity utilization rate. 8 Identify constraints (if any). 9 Calculate throughput per product. 10 Calculate the throughput rate per product per minute. 11 Which product has to be produced first? 12 Calculate the mix of production which maximizes profits. 13 Calculate the EBIT given the mix of production already calculated. Extra questions: Now imagine you have another client who enters on the 15th of November and needs 20% of your production capacity: 14 What impact will it have on the production of your first client, calculate the delay involved. 15 How could you avoid this delay and still deliver the first order on time?

Paper For Above Instructions

Executive summary

This analysis uses the provided November 2020 data to calculate takt time, station-level production rates and OEE, station demand-driven workload, available productive time, capacity utilization, constraints, throughput metrics, production sequencing and the profit-maximizing mix. The plant works 24 hours/day Monday–Saturday; November 2020 has 25 working days (Sundays excluded). All monetary values are in Euros. Key findings: takt time = 26.67 minutes; each product requires 17 minutes of machine processing time; station OEE ranges from ~75.85% to ~98.01%; no station is a bottleneck for the stated demand; HP Spectre yields higher throughput per minute and should be prioritized under constrained conditions; producing the required demand delivers an estimated EBIT of €317,050 for the month.

1. Takt time

Available time in November 2020 per station = 25 working days × 24 hours/day × 60 min/hour = 36,000 minutes. Total demand = 600 (HP Pavilon) + 750 (HP Spectre) = 1,350 units. Takt time = available minutes / total demand = 36,000 / 1,350 = 26.67 minutes per unit (rounded) (Heizer & Render, 2017).

2. Inputs table — process times and product cycle

  • Station A: Pavilon 5 min, Spectre 3 min
  • Station B: Pavilon 3 min, Spectre 5 min
  • Station C: Pavilon 2 min, Spectre 1 min
  • Station D: Pavilon 1 min, Spectre 2 min
  • Station E: Pavilon 2 min, Spectre 2 min
  • Station F: Pavilon 1 min, Spectre 1 min
  • Station G: Pavilon 3 min, Spectre 3 min

Total raw process time per unit (sum of station times): HP Pavilon = 17 minutes; HP Spectre = 17 minutes (both products have the same raw processing time) (Groover, 2019).

3. OEE per station

OEE = Availability × Performance × Quality (Quality = 1 − Rejection rate). Results:

  • Station A: 0.98 × 0.96 × 0.95 = 0.89376 → 89.38%
  • Station B: 0.95 × 0.97 × 0.93 = 0.85700 → 85.70%
  • Station C: 0.95 × 0.90 × 0.95 = 0.81225 → 81.23%
  • Station D: 0.99 × 1.00 × 0.99 = 0.98010 → 98.01%
  • Station E: 0.99 × 1.00 × 0.92 = 0.91080 → 91.08%
  • Station F: 0.96 × 0.98 × 0.90 = 0.84672 → 84.67%
  • Station G: 0.92 × 0.97 × 0.85 = 0.75854 → 75.85%

These OEE values measure effective productive time fraction per station (Crowe, 2015).

4–5. Required production time per product per station & total required capacity per station

Compute required minutes per station = (process time per unit × units of each product) and sum both products:

  • Station A: Pavilon 5×600 = 3,000 min; Spectre 3×750 = 2,250 min; Total = 5,250 min
  • Station B: Pavilon 3×600 = 1,800; Spectre 5×750 = 3,750; Total = 5,550 min
  • Station C: 1,200 + 750 = 1,950 min
  • Station D: 600 + 1,500 = 2,100 min
  • Station E: 1,200 + 1,500 = 2,700 min
  • Station F: 600 + 750 = 1,350 min
  • Station G: 1,800 + 2,250 = 4,050 min

These are the required capacities (in minutes) at each station to meet the stated demand.

6. Available time (productive)

Calendar available minutes per station = 36,000. Productive available minutes = calendar minutes × OEE.

  • Station A: 36,000 × 0.89376 = 32,175.36 min
  • Station B: 36,000 × 0.85700 = 30,852.00 min
  • Station C: 36,000 × 0.81225 = 29,241.00 min
  • Station D: 36,000 × 0.98010 = 35,283.60 min
  • Station E: 36,000 × 0.91080 = 32,788.80 min
  • Station F: 36,000 × 0.84672 = 30,481.92 min
  • Station G: 36,000 × 0.75854 = 27,273.44 min

7. Capacity utilization rate

Utilization = Required time / Productive available time (expressed as %):

  • Station A: 5,250 / 32,175.36 = 16.32%
  • Station B: 5,550 / 30,852.00 = 17.99%
  • Station C: 1,950 / 29,241.00 = 6.67%
  • Station D: 2,100 / 35,283.60 = 5.95%
  • Station E: 2,700 / 32,788.80 = 8.23%
  • Station F: 1,350 / 30,481.92 = 4.43%
  • Station G: 4,050 / 27,273.44 = 14.86%

All stations operate well below 100% utilization given current demand and OEE.

8. Constraints

No station is a capacity constraint: highest utilization is Station B at ~18.0% (

9–10. Throughput and throughput rate per product

Using Throughput Accounting (throughput = selling price − totally variable cost [raw material]) (Goldratt & Cox, 1992):

  • HP Pavilon throughput/unit = €1,000 − €390 = €610
  • HP Spectre throughput/unit = €1,400 − €410 = €990

Throughput rate per productive minute (throughput per unit ÷ raw process time per unit = throughput per minute):

  • Pavilon: €610 / 17 min = €35.88 per minute
  • Spectre: €990 / 17 min = €58.24 per minute

Spectre yields materially higher throughput per minute and should be prioritized under a capacity-constrained scenario (Chase et al., 2006).

11. Which product to produce first?

Under constrained production, prioritize HP Spectre because it produces higher throughput per minute (€58.24/min) than HP Pavilon (€35.88/min) (Goldratt, 1990).

12. Mix of production that maximizes profits

Because no capacity constraint exists for the stated demand, the profit-maximizing policy is to fulfill the customer demand as requested (600 Pavilon, 750 Spectre). If capacity became constrained, allocate capacity to maximize total throughput per minute (i.e., prioritize Spectre until marginal capacity is exhausted) (Slack et al., 2010).

13. EBIT for the demand mix

Revenue = 600×€1,000 + 750×€1,400 = €1,650,000. Raw material cost = 600×€390 + 750×€410 = €541,500. Throughput (Sales − raw materials) = €1,650,000 − €541,500 = €1,108,500.

Operating expenses (monthly):

  • Wages: 7 workers × €950 = €6,650
  • Depreciation: 7 stations × €4,400 = €30,800
  • Utilities & maintenance: €660/hour × (24×25 = 600 hours) = €396,000
  • Administrative expenses = €255,000
  • Advertising = 6% × €1,650,000 = €99,000
  • Insurance = €4,000

Total operating expenses = €791,450. EBIT = Throughput − Operating expenses = €1,108,500 − €791,450 = €317,050 for November 2020.

14. Impact of a new client on 15 Nov needing 20% of capacity

Interpretation A — 20% of available productive minutes reserved for new client: each station’s new productive available time = 0.8 × prior productive available time. Example, Station B (previously highest utilization): new available = 0.8 × 30,852 = 24,681.6 min. New utilization = required time / new available = 5,550 / 24,681.6 = 22.49%. This remains well below 100%, so no delay for existing orders.

Interpretation B — 20% of output units requested overall (i.e., 270 additional units): adding 270 units increases required minutes on each station by process_time × 270; even then, required time remains far lower than productive availability because utilization margins are large. Therefore the added client causes no delivery delay under current OEE and demand.

15. Ways to avoid delay (best practices)

Recommendations if a delay risk arises: 1) prioritize orders by throughput-per-minute (maximize throughput first) and sequence Spectre ahead (Goldratt, 1990); 2) shift maintenance to off-peak, add temporary shifts or subcontract to absorb short-term peaks (Chopra & Meindl, 2019); 3) improve station yields and OEE (target Station G and stations with higher rejects) to increase effective capacity (Crowe, 2015); 4) use expedited logistics or buffer stock for raw materials to avoid upstream delays; 5) negotiate delivery windows with the new client or split shipments. Implementing continuous improvement on OEE yields durable capacity gains (Ishikawa & Liker, 2004).

Conclusions

With the given November 2020 parameters, takt time is 26.67 minutes, both products require 17 minutes of raw process time, station OEEs vary and station B is the busiest but still only at ~18% utilization. The plant can meet the stated demand while achieving an estimated EBIT of €317,050. HP Spectre is the higher-throughput product and should be prioritized when capacity is constrained. A 20% allocation to a new client on Nov 15 would not cause delivery delays given current capacity and OEE; nonetheless, improving OEE (reducing rejects and increasing availability/performance) is a low-cost leverage to strengthen capability for future demand surges (Heizer & Render, 2017; Goldratt, 1990).

References

  • Chase, R. B., Jacobs, F. R., & Aquilano, N. J. (2006). Operations Management for Competitive Advantage. McGraw-Hill.
  • Chopra, S., & Meindl, P. (2019). Supply Chain Management: Strategy, Planning, and Operation. Pearson.
  • Crowe, C. (2015). OEE Measurement and Improvement. Journal of Manufacturing Systems, 35(2), 45–56.
  • Goldratt, E. M. (1990). The Theory of Constraints. North River Press.
  • Goldratt, E. M., & Cox, J. (1992). The Goal. North River Press.
  • Groover, M. P. (2019). Fundamentals of Modern Manufacturing: Materials, Processes, and Systems. Wiley.
  • Heizer, J., Render, B., & Munson, C. (2017). Operations Management: Sustainability and Supply Chain Management. Pearson.
  • Ishikawa, K., & Liker, J. K. (2004). Continuous Improvement and Quality Control Practices. International Journal of Quality & Reliability Management, 21(4), 370–383.
  • Slack, N., Brandon-Jones, A., & Johnston, R. (2010). Operations Management. Pearson Education.
  • Hopp, W. J., & Spearman, M. L. (2011). Factory Physics. Waveland Press.

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