4.2. Defender and Challenger Comparison

So, downtime cost per hour equal to 3% of your equipment cost. Equipment cost is nothing but 900 rupees per hour.

Downtime cost per hour = (3/100) × (900) = 27 rupees per hour.

Your machine is going to operate in a year for 2000 hours. So, what is your yearly downtime cost? Yearly downtime costs for the first year is,

Downtime cost per year = 27 × 2000 = 54,000 rupees.

In this chunk, we start by calculating the downtime cost per hour. Downtime cost refers to the loss incurred for every hour a machine is not operational. It is expressed as a percentage of the equipment cost, which in this case is 900 rupees per hour. We calculate 3% of this cost to find the downtime cost per hour (27 rupees). Then, we can extrapolate this figure to find the annual impact by multiplying the hourly cost by the total number of operational hours per year (2000), leading us to a total yearly downtime cost of 54,000 rupees.

Imagine a car that costs 900 rupees to operate for each hour. If it breaks down for 3% of the time, you're losing 27 rupees for every hour it's out of commission. If the car is expected to run for 2000 hours a year, you lose out on 54,000 rupees of potential earnings because the car was not operating.

Similarly, calculate the downtime costs for the second year; in the second year, the downtime percentage is 6%. So, downtime cost is 6% of your equipment cost, equipment cost is 900 rupees per hour.

Downtime cost per hour = (6/100) × (900) = 54 rupees per hour.

Downtime cost per year = 54 × 2000 = 1,08,000 rupees.

Here, we compute the downtime cost for the second year where the downtime percentage has increased to 6%. Again, we find the downtime cost per hour by calculating 6% of the equipment cost. This results in 54 rupees per hour. To find out the impact for the whole year, we multiply this hourly rate by the 2000 operational hours to yield a total downtime cost of 1,08,000 rupees for the second year.

Continuing with our car analogy, if the same car now has issues that make it unusable for 6% of the time, the cost for the downtime rises to 54 rupees per hour. Over a year of 2000 driving hours, the total impact would be 1,08,000 rupees lost due to these issues, more than doubling the previous year's loss.

To find the cumulative downtime cost, add the costs from both years. Cumulative downtime cost by adding it: 54,000 + 1,08,000 = 1,62,000 rupees. Similarly, you calculate this for all the years for the entire life of the machine.

In this portion, we introduce the concept of cumulative downtime costs by adding the costs from each year. After calculating the yearly costs, we sum them to get a total of 1,62,000 rupees. This cumulative cost will continue to grow as we calculate it year by year, which provides insights into the overall financial impact of downtime across the machine's lifespan.

Think of it like saving for a vacation: in the first year, you save 54,000 rupees, and in the second year, you save another 1,08,000 rupees. Adding these together means you now have a total savings of 1,62,000 rupees, showing how each consecutive year can build a larger financial impact either positively or negatively.

Now, you have to account for the loss in productivity. The loss in productivity results in increased downtime cost because the machine has spent time in the repair yard.

Here, we highlight the impact of lost productivity on costs. If a machine is in repair, it can't perform its tasks, which means the productivity drops and costs increase. Organizations often need to allocate extra resources, like more machines or workers, to make up for that lost time, driving up costs even more.

Imagine a bakery that relies on an oven. If the oven breaks down and needs repairs, productivity drops, as fewer baked goods can be produced. To compensate for this, the bakery may hire additional workers to speed up the process later on, which increases operational costs overall.

So, every year your obsolescence factor is increasing as the machine becomes more obsolete. Costs increase from retaining old machines that produce at a lower productivity rate.

This chunk discusses obsolescence costs, which occur as machines age and become less productive. As new models are introduced with better technologies, older machines may need more repairs and could perform at reduced efficiency compared to their newer counterparts. This results in escalating costs associated with maintaining old equipment.

Consider an old smartphone compared to the latest model. The older phone might struggle to run modern apps or may break down more often, leading you to spend more on repairs instead of enjoying the benefits of a newer, more efficient model. In terms of business, sticking with the old phone would mean missing out on productivity gains that come with the new model.

When you add all the total costs, what is the trend we see? For the first year the cost is high. Initially, the cost is high, then the cost reduces and reaches a minimum point, then again starts to increase.

In this part, we analyze the overall cost trend associated with machine ownership over time. The costs often start high due to depreciation and investment but may decrease as initial costs are spread over greater usage. Eventually, as maintenance, downtime, and obsolescence costs rise, the total cost begins to increase again, forming a parabolic shape in graphing terms.

Think of this like the cost of a car over its lifespan. Initially, the price might be steep due to financing and new ownership costs. Over time, while these costs are amortized, they can dip as the car becomes paid off. However, as it gets older, the maintenance costs start creeping up, making you reconsider the value of holding onto the vehicle.

The economic life is the period during which the cost associated with the machine is minimum. It indicates when the asset should be replaced to minimize costs.

The chunk concludes with the idea of economic life, which refers to the optimal duration of machine ownership where costs are minimized. After this period, the combined costs of maintenance, obsolescence, and downtime typically increase, indicating the need for replacement. This critical decision helps organizations avoid unnecessary expenses and maintain productivity.

Just like deciding when to replace an aging smartphone, businesses must evaluate how long to keep their machines before they become too expensive to maintain. A wise decision is made when the total expenses of keeping the old device exceed the benefits of acquiring a new, more efficient model.

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