Laboratory Operations Analysis: a Report

Categories: Chemistry

Analysis, Pages 4 (807 words)

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Abstract

In this report, we discuss the strategies and decisions made by our team to optimize laboratory operations and maximize profitability. Our approach involved identifying key parameters, designing a dashboard for analysis, and making strategic decisions based on demand analysis, station utilization, and cost-benefit analysis. We also address the outcomes of our decisions, including the impact on profits and lessons learned.

Introduction

From the outset, our team adopted an aggressive strategy to excel in laboratory operations, even though it came with higher risks and potential costs.

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To achieve our goals, we focused on identifying critical parameters and developing a dashboard for efficient decision-making. The primary parameters we monitored included demand (jobs accepted), station utilization, and lead times throughout the process. Our initial objective was to balance the production line and meet the growing demand.

Materials and Methods

Our approach involved several key components:

Demand Analysis: We aimed to predict future demand flows and align them with kit orders. To do this, we created a model that considered the median demand of the last two weeks, projected with their growth rate.

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This allowed us to anticipate stock requirements and the need for additional machines.
Utilization of Stations: To meet the demand and ensure balanced capacity across our three stations, we closely monitored station utilization. We initiated the purchase of new machines when the utilization of any station consistently exceeded 80%. These decisions were justified through cost-benefit analysis.
Cost-Benefit Analysis: We conducted a cost-benefit analysis to evaluate the investment in new machines. This analysis considered different demand scenarios (30, 60, and 90) and calculated the payback time for each investment, assuming ideal conditions.

Once we achieved process balance, we began to intervene in contracts.

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Contract 3 offered the highest profitability when the lab could meet a promised lead time of 0.5 days. However, we remained cautious about switching to contracts 2 or 1 if the promised lead time could not be met due to process conditions. We optimized profitability by modifying contracts based on specific criteria related to machine 1's workload at the end of the previous week.

On day 150, we implemented an "all in" strategy by investing $160,000 in a new machine for station 1 and 2. This decision was made in response to a demand projection of 91 jobs and station 1's utilization exceeding 80% between days 143 and 149.

Results

The following table summarizes the sources and uses of cash and provides an analysis of key items:

Description	Amount, $	Comments
Starting Cash	+ 1,000,000
Revenue	+2,770,670	- 493, 226 & 1981 jobs were accepted under contract 1, 2 & 3 respectively.
		- $ 3,072,000 was the maximum possible revenue.
Interest	+81,993
Station Purchases	-560,000	- 4 stations Nº1 were bought on days 61, 115, 141, and 150.
		- 2 stations Nº2 were bought on days 116 and 150. - All stations were bought at a certain time which ensures that the investment were payed back before the day 314 considering a pay back period 10-29 days for each station (see cost-benefit analysis).
Inventory	-1,704,600	- 2,841 kits were bought (including kits ordered by default). - 2,566 kits were ordered on the review period corresponding to day 7. - 2,700 jobs were accepted.
		Inefficiencies: - 134 kits were needed but not ordered (2,700-2,566 kits). They represent maximum losses of $ 167,500 (134 x $ 1,250) - 141 kits were ordered but not needed (2,841-2,700 kits). They represent losses of $ 84,600 (141 x $ 600)
Cash Balance	1,588,064	Analysis: The cash balance shows that investments in machines and kits were paid back, but better profitability was not achieved due to lower than predicted orders (80/week instead of 91/week as projected on day 150).

Discussion

The analysis of our laboratory operations revealed several key insights and lessons learned:

Delay in purchasing the first four machines negatively impacted the payback period and profitability.
The purchase of the last two machines (station 1 and 2) was unnecessary, as the demand after day 150 was overestimated. This resulted in an avoidable expenditure of $160,000.
Contract interventions were not timely, leading to a maximum loss of $301,220.
Kits were ordered based on the number of jobs waiting for kits at the end of each week, without realizing that some kits were ordered by default. This oversight could have saved up to $84,600.
Considering the lead time of 0.5 days under contract 3, a Last-In-First-Out (LIFO) system for kit management would have been more efficient, given that kits queued for station 1 were often already late.

Conclusion

In conclusion, our laboratory operations analysis revealed both successes and areas for improvement. The delayed purchase of machines and unnecessary investments in station 1 and 2 impacted profitability. Timely contract interventions and improved kit ordering practices could have resulted in higher profits.

Recommendations

Based on our findings, we offer the following recommendations for future laboratory operations:

Ensure timely investment in machines to optimize payback periods and profitability.
Conduct thorough demand projections to avoid overestimating equipment needs.
Implement proactive contract interventions to maximize profitability.
Review and revise kit ordering practices to eliminate unnecessary costs.
Consider a Last-In-First-Out (LIFO) system for kit management to improve efficiency.

By implementing these recommendations, we can enhance laboratory operations and achieve better financial results in future endeavors.