Is 100% occupancy possible? You bet it is, but only if there are no people involved!
Imagine driving past a parking facility and seeing a variable message sign that states, “$1 = 60 mins.” Just a different way of viewing the pricing proposal, I guess, or a different way of advertising the price. Then the sign changes to “$1 = 30 mins.”
Parking operators are manufacturers of parking opportunities. This approach requires us to think about parking as a multi-dimensional product. It’s sold as a “slot in time,” rather than as an objective, tangible product that can be held or touched, (i.e., parking is not a plot of land). The goal is “utilization,” because the resource is still there after the customer goes, but the product is spent there in that moment in time.
If that’s the goal, then there is the argument that we should apply some of the principles of manufacturing to parking, rather than those of the property industry. The principle most applicable here is the theory of constraints, as parking is an industry where we try to get as many people through a car park in a short 8- to 10-hour period.
To increase utilization of the available time, we therefore have our supply constrained by bottlenecks in production of that time. Sometimes we cannot produce enough parking opportunities to satisfy our customers’ demands. Parking is about the manufacturing of opportunities.
Bottlenecks are one of the biggest issues in manufacturing. They can be created by operational issues, poor management, older technology, supply and logistical issues, competition, internal business issues, etc. Because manufacturing is a multibillion-dollar industry, there are many solutions.
I read a wonderful book by Eliyahu Goldratt, called “The Goal,” while completing my business degree. He wrote about the “theory of constraints” and wound the lesson up inside the envelope of a
fictional story.
The theory of constraints is all about understanding and determining what your constraints, or bottlenecks, are in the process, then working through a systematic process of improving throughput.
The process is:
1. Identify constraints – identify the stage of the process where the constraint is found.
2. Resource the constraints – Focus your attention on the constraint to improve it.
3. Re-balance the manufacturing process – Make all of your changes permanent.
4. Go back to No. 1 – Start all over again, because you now have a new constraint in a different place.
So how does this relate to parking? Let’s look at an example. We have a 150-space car park. It’s open nine hours a day and gets about 600 customers through a day. Each stays for about 100 minutes on average at a time.
• The throughput is 600 (cars)
x 100 (mins) = 60,000 mins of parking used.
• This gives us a capacity of 150 (spaces) x 9 (hours) x 60 (mins) = 81,000 mins of parking.
• This actually gives us a system load or “occupancy” of 81,000 mins / 60,000 mins = 74%.
If every car arrived at exactly the right time – so that as a car exited the process, another entered the process – then the system would be burdened at only three-quarters load.
But we know that the customers never come when we want them to. In fact, most tend to come within a seven-hour period, between
10 a.m. and 5 p.m. So, in reality, that makes our sums look like this:
• Throughput remains (mostly) 600 (cars) x 100 (mins) =
60,000 mins of parking used.
• Capacity changes to 150 (spaces) x 7 (hours) x 60 (mins) = 63,000 mins of parking.
• This actually gives us a system load or “occupancy” of
63,000 mins / 60,000 mins = 95%.
In fact, there is an overload of arriving inventory (cars) over those leaving – that is, two cars arriving for every one leaving – between 9:15 and 11 a.m. This changes the picture a lot. So how do we get them all in?
The answer is to look at the whole picture differently. If it were a manufacturing process, you would look at the constraint and focus
your time and resources trying to improve and speed up that constraint. The whole system only allows the throughput at the level the
constraint allows.
So what is the constraint? For a lot of reasons, I’m going to suggest that it’s the average length of stay (100 minutes). It’s difficult to increase your capacity (that is, you need to add more spaces), and the customers will come when the time suits them.
So I suggest that after a customer arrives, he needs to be allowed a slot in time, and must stick to that as the space he utilizes becomes someone else’s space after his period of time is up, resulting in more throughput. The customer has a slot in the process set aside for them. They go through that process at the speed of the constraint, which is a cycle time of 100 minutes, but as the process speeds up, we must get more cars through the process.
If the occupancy goal is 87%, as a standard goal for the day, then the calculations change to:
• Suggested process load or occupancy of 87%, then X = 63,000 x 87% = 54,810 mins of parking.
• Average length of stay becomes X = 54,810 / 600 = 91 mins.
But at the peak, we are getting two cars arriving to every one leaving! That situation would be OK if the capacity of the car park is not threatened by the influx. But if it is, then length of stay must come down to fit everyone in. If we have to get 200 cars in to those spots in the next 60 mins, then
• Capacity changes to 150 (spaces) x 1 (hour) x 60 (mins) =
9,000 mins of parking.
• Average length of stay becomes X = 9,000 / 200 = 45 mins.
Now you obviously can’t throw people out as soon as their time is up, but you can discourage them from staying by pricing in a manner that encourages turnover. The parking rates might look like this,
We have seen the coming of systems where occupancies can determine prices. Those prices can set the expectations of the parking operator over the desires of the customers, to achieve a set occupancy.
With “cloud-based” technology, the prices can be adjusted continually, through ongoing calculations responding to inputs of occupancy and pricing. If we mark the set point at 100% occupancy, we can guide customers to slot into their niche in the process and charge a lot more if they want to stay beyond their allotted time.
One-hundred percent occupancy is possible, but it requires a system to manage the changes instantly, in effect, to micro-manage the parking business. Everything needs to be right, including different ways of looking at the whole process, such as viewing parking as a factory of parking opportunities and solving the constraints.
Clever marketing also is required, so that customers can see that their dollar buys a changing amount of time, and 100% occupancy needs a reversed rate structure where it gets more expensive as the duration goes on.
You may not consistently hit that mark, but you can get close to
“El Dorado.”
Imagine driving past a parking facility and seeing a variable message sign that states, “$1 = 60 mins.” Just a different way of viewing the pricing proposal, I guess, or a different way of advertising the price. Then the sign changes to “$1 = 30 mins.”
Parking operators are manufacturers of parking opportunities. This approach requires us to think about parking as a multi-dimensional product. It’s sold as a “slot in time,” rather than as an objective, tangible product that can be held or touched, (i.e., parking is not a plot of land). The goal is “utilization,” because the resource is still there after the customer goes, but the product is spent there in that moment in time.
If that’s the goal, then there is the argument that we should apply some of the principles of manufacturing to parking, rather than those of the property industry. The principle most applicable here is the theory of constraints, as parking is an industry where we try to get as many people through a car park in a short 8- to 10-hour period.
To increase utilization of the available time, we therefore have our supply constrained by bottlenecks in production of that time. Sometimes we cannot produce enough parking opportunities to satisfy our customers’ demands. Parking is about the manufacturing of opportunities.
Bottlenecks are one of the biggest issues in manufacturing. They can be created by operational issues, poor management, older technology, supply and logistical issues, competition, internal business issues, etc. Because manufacturing is a multibillion-dollar industry, there are many solutions.
I read a wonderful book by Eliyahu Goldratt, called “The Goal,” while completing my business degree. He wrote about the “theory of constraints” and wound the lesson up inside the envelope of a
fictional story.
The theory of constraints is all about understanding and determining what your constraints, or bottlenecks, are in the process, then working through a systematic process of improving throughput.
The process is:
1. Identify constraints – identify the stage of the process where the constraint is found.
2. Resource the constraints – Focus your attention on the constraint to improve it.
3. Re-balance the manufacturing process – Make all of your changes permanent.
4. Go back to No. 1 – Start all over again, because you now have a new constraint in a different place.
So how does this relate to parking? Let’s look at an example. We have a 150-space car park. It’s open nine hours a day and gets about 600 customers through a day. Each stays for about 100 minutes on average at a time.
• The throughput is 600 (cars)
x 100 (mins) = 60,000 mins of parking used.
• This gives us a capacity of 150 (spaces) x 9 (hours) x 60 (mins) = 81,000 mins of parking.
• This actually gives us a system load or “occupancy” of 81,000 mins / 60,000 mins = 74%.
If every car arrived at exactly the right time – so that as a car exited the process, another entered the process – then the system would be burdened at only three-quarters load.
But we know that the customers never come when we want them to. In fact, most tend to come within a seven-hour period, between
10 a.m. and 5 p.m. So, in reality, that makes our sums look like this:
• Throughput remains (mostly) 600 (cars) x 100 (mins) =
60,000 mins of parking used.
• Capacity changes to 150 (spaces) x 7 (hours) x 60 (mins) = 63,000 mins of parking.
• This actually gives us a system load or “occupancy” of
63,000 mins / 60,000 mins = 95%.
In fact, there is an overload of arriving inventory (cars) over those leaving – that is, two cars arriving for every one leaving – between 9:15 and 11 a.m. This changes the picture a lot. So how do we get them all in?
The answer is to look at the whole picture differently. If it were a manufacturing process, you would look at the constraint and focus
your time and resources trying to improve and speed up that constraint. The whole system only allows the throughput at the level the
constraint allows.
So what is the constraint? For a lot of reasons, I’m going to suggest that it’s the average length of stay (100 minutes). It’s difficult to increase your capacity (that is, you need to add more spaces), and the customers will come when the time suits them.
So I suggest that after a customer arrives, he needs to be allowed a slot in time, and must stick to that as the space he utilizes becomes someone else’s space after his period of time is up, resulting in more throughput. The customer has a slot in the process set aside for them. They go through that process at the speed of the constraint, which is a cycle time of 100 minutes, but as the process speeds up, we must get more cars through the process.
If the occupancy goal is 87%, as a standard goal for the day, then the calculations change to:
• Suggested process load or occupancy of 87%, then X = 63,000 x 87% = 54,810 mins of parking.
• Average length of stay becomes X = 54,810 / 600 = 91 mins.
But at the peak, we are getting two cars arriving to every one leaving! That situation would be OK if the capacity of the car park is not threatened by the influx. But if it is, then length of stay must come down to fit everyone in. If we have to get 200 cars in to those spots in the next 60 mins, then
• Capacity changes to 150 (spaces) x 1 (hour) x 60 (mins) =
9,000 mins of parking.
• Average length of stay becomes X = 9,000 / 200 = 45 mins.
Now you obviously can’t throw people out as soon as their time is up, but you can discourage them from staying by pricing in a manner that encourages turnover. The parking rates might look like this,
We have seen the coming of systems where occupancies can determine prices. Those prices can set the expectations of the parking operator over the desires of the customers, to achieve a set occupancy.
With “cloud-based” technology, the prices can be adjusted continually, through ongoing calculations responding to inputs of occupancy and pricing. If we mark the set point at 100% occupancy, we can guide customers to slot into their niche in the process and charge a lot more if they want to stay beyond their allotted time.
One-hundred percent occupancy is possible, but it requires a system to manage the changes instantly, in effect, to micro-manage the parking business. Everything needs to be right, including different ways of looking at the whole process, such as viewing parking as a factory of parking opportunities and solving the constraints.
Clever marketing also is required, so that customers can see that their dollar buys a changing amount of time, and 100% occupancy needs a reversed rate structure where it gets more expensive as the duration goes on.
You may not consistently hit that mark, but you can get close to
“El Dorado.”
Parking Consultant Kevin Warwood of Christchurch, New Zealand, blogs at parkingithere.blogspot.com. He can be reached at kevin.warwood@gmail.com.
Is 100% occupancy possible? You bet it is, but only if there are no people involved!
Imagine driving past a parking facility and seeing a variable message sign that states, “$1 = 60 mins.” Just a different way of viewing the pricing proposal, I guess, or a different way of advertising the price. Then the sign changes to “$1 = 30 mins.”
Parking operators are manufacturers of parking opportunities. This approach requires us to think about parking as a multi-dimensional product. It’s sold as a “slot in time,” rather than as an objective, tangible product that can be held or touched, (i.e., parking is not a plot of land). The goal is “utilization,” because the resource is still there after the customer goes, but the product is spent there in that moment in time.
If that’s the goal, then there is the argument that we should apply some of the principles of manufacturing to parking, rather than those of the property industry. The principle most applicable here is the theory of constraints, as parking is an industry where we try to get as many people through a car park in a short 8- to 10-hour period.
To increase utilization of the available time, we therefore have our supply constrained by bottlenecks in production of that time. Sometimes we cannot produce enough parking opportunities to satisfy our customers’ demands. Parking is about the manufacturing of opportunities.
Bottlenecks are one of the biggest issues in manufacturing. They can be created by operational issues, poor management, older technology, supply and logistical issues, competition, internal business issues, etc. Because manufacturing is a multibillion-dollar industry, there are many solutions.
I read a wonderful book by Eliyahu Goldratt, called “The Goal,” while completing my business degree. He wrote about the “theory of constraints” and wound the lesson up inside the envelope of a
fictional story.
The theory of constraints is all about understanding and determining what your constraints, or bottlenecks, are in the process, then working through a systematic process of improving throughput.
The process is:
1. Identify constraints – identify the stage of the process where the constraint is found.
2. Resource the constraints – Focus your attention on the constraint to improve it.
3. Re-balance the manufacturing process – Make all of your changes permanent.
4. Go back to No. 1 – Start all over again, because you now have a new constraint in a different place.
So how does this relate to parking? Let’s look at an example. We have a 150-space car park. It’s open nine hours a day and gets about 600 customers through a day. Each stays for about 100 minutes on average at a time.
• The throughput is 600 (cars)
x 100 (mins) = 60,000 mins of parking used.
• This gives us a capacity of 150 (spaces) x 9 (hours) x 60 (mins) = 81,000 mins of parking.
• This actually gives us a system load or “occupancy” of 81,000 mins / 60,000 mins = 74%.
If every car arrived at exactly the right time – so that as a car exited the process, another entered the process – then the system would be burdened at only three-quarters load.
But we know that the customers never come when we want them to. In fact, most tend to come within a seven-hour period, between
10 a.m. and 5 p.m. So, in reality, that makes our sums look like this:
• Throughput remains (mostly) 600 (cars) x 100 (mins) =
60,000 mins of parking used.
• Capacity changes to 150 (spaces) x 7 (hours) x 60 (mins) = 63,000 mins of parking.
• This actually gives us a system load or “occupancy” of
63,000 mins / 60,000 mins = 95%.
In fact, there is an overload of arriving inventory (cars) over those leaving – that is, two cars arriving for every one leaving – between 9:15 and 11 a.m. This changes the picture a lot. So how do we get them all in?
The answer is to look at the whole picture differently. If it were a manufacturing process, you would look at the constraint and focus
your time and resources trying to improve and speed up that constraint. The whole system only allows the throughput at the level the
constraint allows.
So what is the constraint? For a lot of reasons, I’m going to suggest that it’s the average length of stay (100 minutes). It’s difficult to increase your capacity (that is, you need to add more spaces), and the customers will come when the time suits them.
So I suggest that after a customer arrives, he needs to be allowed a slot in time, and must stick to that as the space he utilizes becomes someone else’s space after his period of time is up, resulting in more throughput. The customer has a slot in the process set aside for them. They go through that process at the speed of the constraint, which is a cycle time of 100 minutes, but as the process speeds up, we must get more cars through the process.
If the occupancy goal is 87%, as a standard goal for the day, then the calculations change to:
• Suggested process load or occupancy of 87%, then X = 63,000 x 87% = 54,810 mins of parking.
• Average length of stay becomes X = 54,810 / 600 = 91 mins.
But at the peak, we are getting two cars arriving to every one leaving! That situation would be OK if the capacity of the car park is not threatened by the influx. But if it is, then length of stay must come down to fit everyone in. If we have to get 200 cars in to those spots in the next 60 mins, then
• Capacity changes to 150 (spaces) x 1 (hour) x 60 (mins) =
9,000 mins of parking.
• Average length of stay becomes X = 9,000 / 200 = 45 mins.
Now you obviously can’t throw people out as soon as their time is up, but you can discourage them from staying by pricing in a manner that encourages turnover. The parking rates might look like this,
We have seen the coming of systems where occupancies can determine prices. Those prices can set the expectations of the parking operator over the desires of the customers, to achieve a set occupancy.
With “cloud-based” technology, the prices can be adjusted continually, through ongoing calculations responding to inputs of occupancy and pricing. If we mark the set point at 100% occupancy, we can guide customers to slot into their niche in the process and charge a lot more if they want to stay beyond their allotted time.
One-hundred percent occupancy is possible, but it requires a system to manage the changes instantly, in effect, to micro-manage the parking business. Everything needs to be right, including different ways of looking at the whole process, such as viewing parking as a factory of parking opportunities and solving the constraints.
Clever marketing also is required, so that customers can see that their dollar buys a changing amount of time, and 100% occupancy needs a reversed rate structure where it gets more expensive as the duration goes on.
You may not consistently hit that mark, but you can get close to
“El Dorado.”
Imagine driving past a parking facility and seeing a variable message sign that states, “$1 = 60 mins.” Just a different way of viewing the pricing proposal, I guess, or a different way of advertising the price. Then the sign changes to “$1 = 30 mins.”
Parking operators are manufacturers of parking opportunities. This approach requires us to think about parking as a multi-dimensional product. It’s sold as a “slot in time,” rather than as an objective, tangible product that can be held or touched, (i.e., parking is not a plot of land). The goal is “utilization,” because the resource is still there after the customer goes, but the product is spent there in that moment in time.
If that’s the goal, then there is the argument that we should apply some of the principles of manufacturing to parking, rather than those of the property industry. The principle most applicable here is the theory of constraints, as parking is an industry where we try to get as many people through a car park in a short 8- to 10-hour period.
To increase utilization of the available time, we therefore have our supply constrained by bottlenecks in production of that time. Sometimes we cannot produce enough parking opportunities to satisfy our customers’ demands. Parking is about the manufacturing of opportunities.
Bottlenecks are one of the biggest issues in manufacturing. They can be created by operational issues, poor management, older technology, supply and logistical issues, competition, internal business issues, etc. Because manufacturing is a multibillion-dollar industry, there are many solutions.
I read a wonderful book by Eliyahu Goldratt, called “The Goal,” while completing my business degree. He wrote about the “theory of constraints” and wound the lesson up inside the envelope of a
fictional story.
The theory of constraints is all about understanding and determining what your constraints, or bottlenecks, are in the process, then working through a systematic process of improving throughput.
The process is:
1. Identify constraints – identify the stage of the process where the constraint is found.
2. Resource the constraints – Focus your attention on the constraint to improve it.
3. Re-balance the manufacturing process – Make all of your changes permanent.
4. Go back to No. 1 – Start all over again, because you now have a new constraint in a different place.
So how does this relate to parking? Let’s look at an example. We have a 150-space car park. It’s open nine hours a day and gets about 600 customers through a day. Each stays for about 100 minutes on average at a time.
• The throughput is 600 (cars)
x 100 (mins) = 60,000 mins of parking used.
• This gives us a capacity of 150 (spaces) x 9 (hours) x 60 (mins) = 81,000 mins of parking.
• This actually gives us a system load or “occupancy” of 81,000 mins / 60,000 mins = 74%.
If every car arrived at exactly the right time – so that as a car exited the process, another entered the process – then the system would be burdened at only three-quarters load.
But we know that the customers never come when we want them to. In fact, most tend to come within a seven-hour period, between
10 a.m. and 5 p.m. So, in reality, that makes our sums look like this:
• Throughput remains (mostly) 600 (cars) x 100 (mins) =
60,000 mins of parking used.
• Capacity changes to 150 (spaces) x 7 (hours) x 60 (mins) = 63,000 mins of parking.
• This actually gives us a system load or “occupancy” of
63,000 mins / 60,000 mins = 95%.
In fact, there is an overload of arriving inventory (cars) over those leaving – that is, two cars arriving for every one leaving – between 9:15 and 11 a.m. This changes the picture a lot. So how do we get them all in?
The answer is to look at the whole picture differently. If it were a manufacturing process, you would look at the constraint and focus
your time and resources trying to improve and speed up that constraint. The whole system only allows the throughput at the level the
constraint allows.
So what is the constraint? For a lot of reasons, I’m going to suggest that it’s the average length of stay (100 minutes). It’s difficult to increase your capacity (that is, you need to add more spaces), and the customers will come when the time suits them.
So I suggest that after a customer arrives, he needs to be allowed a slot in time, and must stick to that as the space he utilizes becomes someone else’s space after his period of time is up, resulting in more throughput. The customer has a slot in the process set aside for them. They go through that process at the speed of the constraint, which is a cycle time of 100 minutes, but as the process speeds up, we must get more cars through the process.
If the occupancy goal is 87%, as a standard goal for the day, then the calculations change to:
• Suggested process load or occupancy of 87%, then X = 63,000 x 87% = 54,810 mins of parking.
• Average length of stay becomes X = 54,810 / 600 = 91 mins.
But at the peak, we are getting two cars arriving to every one leaving! That situation would be OK if the capacity of the car park is not threatened by the influx. But if it is, then length of stay must come down to fit everyone in. If we have to get 200 cars in to those spots in the next 60 mins, then
• Capacity changes to 150 (spaces) x 1 (hour) x 60 (mins) =
9,000 mins of parking.
• Average length of stay becomes X = 9,000 / 200 = 45 mins.
Now you obviously can’t throw people out as soon as their time is up, but you can discourage them from staying by pricing in a manner that encourages turnover. The parking rates might look like this,
We have seen the coming of systems where occupancies can determine prices. Those prices can set the expectations of the parking operator over the desires of the customers, to achieve a set occupancy.
With “cloud-based” technology, the prices can be adjusted continually, through ongoing calculations responding to inputs of occupancy and pricing. If we mark the set point at 100% occupancy, we can guide customers to slot into their niche in the process and charge a lot more if they want to stay beyond their allotted time.
One-hundred percent occupancy is possible, but it requires a system to manage the changes instantly, in effect, to micro-manage the parking business. Everything needs to be right, including different ways of looking at the whole process, such as viewing parking as a factory of parking opportunities and solving the constraints.
Clever marketing also is required, so that customers can see that their dollar buys a changing amount of time, and 100% occupancy needs a reversed rate structure where it gets more expensive as the duration goes on.
You may not consistently hit that mark, but you can get close to
“El Dorado.”
Parking Consultant Kevin Warwood of Christchurch, New Zealand, blogs at parkingithere.blogspot.com. He can be reached at kevin.warwood@gmail.com.
