1.2. Productivity Estimation

Now let us consider the economics of going for 5 scrapers. So, 5 in the sense you are going to use lesser than what is needed, you are assuming 5 that means you are going to use the number of scrapers lesser than what is needed. So, when the number of scrapers are lesser than the balanced number so obviously scrapers are more critical, but a pusher will have the ideal time. Your pusher will wait for the scraper.

In this chunk, we explore the scenario of having 5 scrapers for a job. Here, the assumption is that this number is less than what's ideal. If there are fewer scrapers than necessary, they become more critical because their availability determines the pace of work. Conversely, the pusher, which is responsible for pushing the scrapers to load materials, will often have idle time since it will have to wait for the scrapers to be ready. Therefore, it illustrates how the correct balance of machinery is essential for efficient production.

Think of a restaurant where you have fewer chefs than customers demanding food. The chefs (scrapers) will be overwhelmed and unable to meet the demand quickly, while the waitstaff (pusher) will end up waiting for dishes to be prepared rather than serving customers.

So, now, let us see the productivity this case of n equal to 5 scrapers. How to estimate the production of this scraper? The volume of your bowl volume per load, you know the value of 19.82 bank cubic meter. Production (Scraper controlling) = (Efficiency, min/hr × no. of scrapers × vol. per load) / (Cycle time of scraper, min). Production = (50 min/hr × 5 × 19.82 bcm) / 7.78 min = 636.89 bcm/hr.

To estimate productivity for 5 scrapers, we need to factor in several elements: the efficiency of the machine (which is how much time it can operate in reality), the number of scrapers, and the volume of the load they can carry (in this case, 19.82 bank cubic meters per load). Here, we calculate the production by multiplying the efficiency by the number of scrapers and the volume of each load, then dividing this by the cycle time for one scraper. This results in a production rate of 636.89 cubic meters per hour, demonstrating how productivity can be quantified.

Imagine a factory assembly line where each worker (scraper) assembles toys (volume per load) efficiently for a certain time (efficiency). If one worker can make a certain number of toys in a minute, you calculate the total output by multiplying how many workers there are and how many toys they can produce before taking a break (cycle time). The formula allows you to see how effective the workers are as a team.

If n is greater than the balanced number that means you are going to use more number of scrapers, then what is indicated by the balance number. In this case, scrapers will have the ideal time. Scrapers are not critical. So, the scraper will be waiting for the pusher. Pusher is critical here.

This part discusses the case where the number of scrapers exceeds the ideal balance point. In this case, the scrapers are not the bottleneck anymore; they become idle because they have to wait for the pusher to be available to continue working. This indicates a different control over production, where now the cycle time of the pusher becomes critical.

Consider a race where you have more athletes than available bicycles. If more athletes are waiting for bikes (scrapers), the race can't continue until a bike is freed up. The athletes (scrapers) can only run as fast as the bike (pusher) allows, leading to inefficiencies.

How to calculate the unit production cost? Total unit cost of production for combination = (Cost of Pusher with operator + Cost of scraper with operator × number of scrapers) / Job production. It gives us unit cost per bank meter cube.

In this section, we are calculating the cost associated with the production per load. It involves adding the costs of both the pusher and scrapers (including operator costs) and dividing it by the productivity achieved. This metric helps in assessing whether the investment in resources leads to economical production by comparing costs efficiently across different scenarios.

Think of budgeting for a team project where you gather costs from various resources (like supplies, labor, etc.). To assess how cost-effective your project is, you need to see how much you spend (total cost) against how much output (finished products) you generate. It’s like finding out the cost to complete each product.

Based on productivity if I select obviously I have to go for 6 number of scrapers per pusher, because 5 scrapers is giving you 636.89, 6 scrapers is giving you 723.36 bank cubic meters per hour.

Here, we see a decision-making scenario. Given that the productivity of using 6 scrapers is higher (723.36 bcm/hr) compared to just 5 scrapers (636.89 bcm/hr), it is logical to prefer the more efficient option if the aim is maximized productivity. This insight demonstrates the need to balance resource allocation for optimal results.

Imagine a team project where more collaborators lead to faster completion of tasks, just like adding more scrapers improves production. If you’re trying to meet tight deadlines, enlisting more hands would help, similar to our scrapers helping increase output to meet project demands.

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