1.3. Comparison of Productivity for 5 and 6 Scrapers

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, obviously scrapers are more critical, but a pusher will have the ideal time. Your pusher will wait for the scraper. So, unless a scraper is available you cannot complete the job. So, here the scraper will be controlling the production as a scraper is a lesser in number, but the pusher will have the ideal time. So, now, let us see the productivity in this case of n equal to 5 scrapers.

In this chunk, we discuss the scenario where only 5 scrapers are used. The idea here is that if the number of scrapers is below the optimal number, one scraper becomes critical for production. Since there are fewer scrapers, they can control the production line. The pusher, a supporting mechanism, will often end up waiting for the scraper to finish its task before it can proceed. Thus, the scarcity of scrapers can lead to slower productivity overall.

Imagine a factory with only 5 workers assigned to build a product, whereas 10 are needed. The workflow slows down as tasks pile up because each worker (like the scraper in this scenario) becomes essential for the production line. If one worker is busy, the others wait, leading to inefficiency. Thus, having fewer scrapers can hold back overall production, much like the factory's limited workforce.

The volume of your bowl volume per load, you know the value of 19.82 bank cubic meter. Production (Scraper controlling) efficiency, hr = × number of scrapers × vol. per load / cycle time of scraper, min:

= (50 min/hr) × 5 × 19.82 bcm / 7.78 min = 636.89 bcm/hr.

In this section, we calculate the productivity of using 5 scrapers. We start with a known volume capacity for the load, which is 19.82 bank cubic meters. The productivity is calculated based on this volume, the number of scrapers, and the cycle time for each scraper. By applying the formula presented, we find that using 5 scrapers yields a production rate of 636.89 bank cubic meters per hour, illustrating the output if we opt for this setup.

Think of a bus that can carry 19.82 cubic meters of passengers. If there are 5 buses operating together (scrapers), but each bus can only make stops every 7.78 minutes, then by coordinating their trips, the total number of passengers moved in one hour (636.89) is calculated based on how many trips each bus makes.

If n is greater than the balance 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. So, unless the pusher is available, I cannot complete the job.

This chunk discusses the implications of using 6 scrapers instead of 5. Here, we have more scrapers than what is optimal. As a result, the scrapers wait for the pusher to be free. In this setup, the roles shift because the pusher becomes the critical component that dictates the flow of work. If the pusher is busy or unavailable, it doesn't matter how many scrapers are ready; work cannot continue.

Imagine a restaurant with 6 chefs (scrapers) preparing meals, but only one waiter (pusher) to serve them. If the waiter is busy serving other customers, the chefs, despite being ready, cannot send out any orders. This highlights how additional resources don’t always mean increased speed if the bottleneck is at a different point in the system.

Production (Pusher controlling) = × vol. per load / cycle time of pusher, min:

= (50 min/hr) × 19.82 bcm / 1.37 min = 723.36 bcm/hr.

In this section, we calculate the productivity when using 6 scrapers. The formula used here highlights that the output now depends on the pusher's cycle time instead of the scrapers'. The resulting productivity is 723.36 bank cubic meters per hour. This illustrates that with 6 scrapers, production is now limited by how quickly the pusher can operate.

Returning to our earlier restaurant analogy: if we have 6 chefs all cooking meals but only 1 waiter who takes 1.37 minutes to serve each meal, the restaurant's ability to serve customers becomes limited by the waiter's speed. Hence, even with many chefs, the overall meal delivery rate (723.36 meals per hour) relies on the waiter's efficiency.

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 make a direct comparison between the productivity outputs when using 5 scrapers versus 6 scrapers. The conclusion is straightforward; 6 scrapers yield a higher productivity rate. Thus, if maximizing output is the goal, the choice clearly favors the combination with 6 scrapers, as it means completing jobs faster.

If you run two delivery teams, one with less capacity (5 vans) and one with greater (6 vans), and you find that the second team delivers more packages per hour. The choice is simple: invest in the second team for greater output, as it directly translates to better business efficiency.

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