When Increasing Capacity Becomes a Priority

One Sunday morning, a homeless woman at a traffic intersection was approaching cars and begging for alms.  Some drivers give but most don’t.  But the woman persists anyway; she shows a sign saying she’s homeless and asks for money for food. 

I thought as I observed the homeless woman:  if the government could spend so much setting up facilities to quarantine patients infected with the CoVID-19 virus, couldn’t it also spend a little more to house the homeless who roam the streets?  At least the government could provide a place to sleep and some food and water to homeless people even for a brief time so they’d be able to find work or resolve whatever issue that brought them there in the first place? 

The frequent answer to such a suggestion is a lack of resources.  The government would say they don’t have the budget to provide such for homeless people. 

How then were they able to provide for CoVID-19 infected people?  That’s different, a government person may say.  It was a national emergency and the virus is dangerous and life-threatening. 

But isn’t being homeless and without food dangerous and life-threatening too? And at this point, the government would not pursue the argument.  With a wave of a hand, they’d just say there is no justification to provide the resources. 

Not only governments but also enterprises hesitate to provide resources even when the demand would be there.  Executives would cite limitations in budgets or capital.  They would prefer that the operations spearheading supply reach their points of maximum capacities before asking for any investment in additional capacities. 

And even if they are at that point of maximum utilisation, executives would require proof that operations are also at their highest efficiencies.  Executives by common practice, will want an organisation to exhaust all of its means before considering any investment in additional capacities. 

If executives can avoid investing in new capacities, they will.     

This is why factories run flat-out to supply products before companies realise they need to build new production lines.  This is why airlines wait until flights are overbooked or ticket counters turn away passengers before they add new planes to their fleets.  This is why some restaurants don’t expand their dining areas or hire new staff until they see patrons waiting for tables or walking away because they couldn’t get a waiter to serve them.    And this is why internet companies don’t install new equipment until they see slowdowns in downloading speed or backlogs of subscribers. 

It doesn’t occur to many enterprises that investments in additional resources should ideally happen before operating limits are reached. 

At the height of the CoVID-19 pandemic in New York City, USA, in late March of 2020, the US Navy dispatched the USNS hospital ship, Comfort, to assist the city’s medical services. 

New York was low on available beds and staff to treat a surge of CoVID-19 patients.  New York City welcomed the Comfort, with its 1,000 beds and trained medical staff.   

But instead of helping, the Comfort would only take non-CoVID-19 patients.  The US Navy thought that by taking in patients not infected by the virus, New York could free up space in their hospitals for the CoVID-19 patients.  The Comfort’s beds were also spaced closely side by side so in the first place, the ship could not enforce social distancing for CoVID-19 patients.

The Comfort ended up treating 20 patients at its first week as New York’s hospitals continued to struggle with crowded wards and weary staff.  After so many weeks of sitting idly by hardly utilised, the Comfort departed New York City. 

The story of the Comfort was a lesson for leaders, if not an eye-opener for executives.  One should not wait till an issue becomes extremely urgent before acting.  

But nobody did learn.  Nobody did realise.  Many executives of governments and enterprises have forgotten the story of the Comfort. It has been relegated as one more passing tale of the pandemic era.  Meanwhile, wave after wave of virus infections months after the story of the Comfort have led to overcrowded hospitals and more deaths due to patients unable to be admitted for treatment.   

We shouldn’t wait till the last minute before deciding we need more capacity.  It isn’t complicated to calculate how much more we need if we can see the rate of  growing demand and anticipate when our resources will no longer be enough. 

I gave money to the homeless woman that day but I saw more homeless people begging on the street corners the weeks afterward. 

We still haven’t learned. 

About Overtimers Anonymous

Do We Really Need New Capacity?

Do we really need a new highway?

Metropolitan Manila, Philippines, has one of the worst urban traffic congestions on Earth (at least before the pandemic of 2020 forced people to stop travelling).  This has led to a number of corporations and wealthy individuals to propose new roads, bridges, and tunnels. 

The proposals cite the obvious problem of traffic gridlock and the resulting negative effect on productivity.  The city’s leading agency, the Metro-Manila Development Authority (MMDA) believes the country’s economy loses an estimated PhP 3.5 billion ($USD 70 million) daily due to traffic congestion.

For so many years, the Philippine government has extended expressways north and south of Manila, built or refurbished new bridges, and even initiated river ferries to reduce travel times within the city.  At the end of 2019, however, people were still complaining about being stuck in traffic for hours.  This has led the government, investors, and private corporations to propose and initiate new projects.  These include elevated expressways, more bridges, and a subterranean commuter train that will traverse underneath the city. 

Government and some so-called experts believe these new projects when completed will ease traffic and alleviate the woes of commuters and automobile drivers. 

But will it, really? 

Los Angeles County, California, USA, has lots of highways.  Over many years, the city has seen more freeways built, expanded, and improved.  It takes only minutes to travel from one place to another, even if the distance is 30 to 40 miles (48 to 64 kilometres) across the county.  LA’s freeways is just a subset of the United States’ Interstate expressway system which allows people to drive across the country seamlessly. 

In recent years, however, traffic along the Los Angeles freeways have gotten worse.  Gridlocks are common not only during morning and evening rush hours but also even during weekends.  And even as the state of California adds more to its freeways, the traffic has grown longer year after year. 

Expanding road capacity doesn’t necessarily reduce traffic; it actually may increase it.  It’s called Induced Travel Demand (ITD).  As more roads are built, more drivers together with their automobiles emerge and eat up the added capacity. 

ITD has been proven in urban centres not only at Los Angeles but in cities around the world such as Beijing and London.  It puts truth to the adage: “if you build it, they will come.”

The phenomenon of ITD, however, doesn’t apply to all places.  The Louisville-Southern Indiana Ohio River Bridges Project in the USA, for example, saw travel demand reducing instead of increasing despite forecasts to the contrary.  

In brief, increasing capacity can cause a corresponding increase in demand.  In contrast, increasing capacity can result in decreasing demand.  In both scenarios, engineers don’t get what they expected in terms of beneficial results. 

Adding capacity always seemed to be an obvious solution to increasing demand.  In reality, however, it’s not. 

Enterprises often invest in additional capacity when supply chains fall short in deliveries due to reasons such as production not being able to keep up with orders or because there were not enough trucks to load items. 

It’s obvious, the executives would say.  Manufacturing is running flat-out and we need more trucks. 

It goes further.  Logistics managers would say they need more storage space and more forklifts, on top of more trucks.  Purchasing managers ask to hire more staff to find more vendors.  Manufacturing would want their facilities expanded to accommodate more equipment. 

Justification is supposedly straightforward.  Engineers extrapolate present-day numbers with trends into the future.  Space will run out.  Production capacity will continue to run behind.  There will be more truckloads. 

The extrapolations that engineers use to justify added capacities aren’t even based on simplified mathematics but on elaborate algorithms certified by experts and established as valid for years. 

So, why then do projects fail to reap benefits?  Why does demand suddenly increase and traffic get worse?  Or why does demand fall off after new capacity is installed?

Because the forecasts, simply put and for what they were worth, are wrong.  Or to put it more concisely:  the forecasting is wrong. 

Urban planners often don’t take into account Induced Travel Demand because their forecasting only considers historic trends in demand.  They fail to see that sociological factors come into play when new roads and bridges are built.  Simply put, when people see a new road, they want to drive on it.   And the more a city boasts about the added capacity of a new road, the more the people are encouraged to change their habits of commute to use it.  More people drive and more cars are bought.  Congestion on the new road increases. 

In the Louisville-Southern Indiana Ohio River Bridges Project, the forecasting model engineers used assumed traffic would increase because there would be more commuters and motorists.  The forecasting model was based on surveys and mathematical algorithms. 

What the forecasting model didn’t take into account was that people’s behaviours change.  They don’t remain necessarily constant.  Hence, as the Louisville economy and demographics evolved, the patterns of commuters and motorists also changed.  Demand actually fell.  The forecast was wrong.

Engineers in the United States now realise that justifying new capacities whether it be for new highway infrastructure or for new machinery for manufacturing should not be based on forecasting alone.

It should be based on what the problem is. 

Capacity is not the problem when it comes to traffic.

Capacity is not the problem when supply is not meeting customer demand. 

For urban planners, it should start from questions like:

  • Where do we want people to reside and work?
  • How much public transportation do we want?
  • How many high-rise buildings should we allow to be built and where?
  • Where do we locate our airports and seaports?
  • Do we really need a new highways and airports?

For supply chains, questions arising about capacity shortfalls should focus toward:

  • Do we need more or less customers to deliver to? 
  • Who do we want to sell and deliver to in the future?
  • Who are the customers that are buying more or less of our products? 
  • Do we need to sell more new products or do we need to sell fewer?
  • Are our manufacturing facilities too big and too far from customers?
  • Should we centralise or put up satellite depots and service centres?
  • Are we satisfied with our transportation set-up? 

Forecasts are the bases of justification for new capacity projects.  Unfortunately, forecasts cannot be depended on to be precisely accurate.  In the first place, forecasting by itself, despite the modern-day algorithms, can never be counted on to provide a clear picture of the future. 

The error in justifying new capacity is in using the shortfall of capacity alone as a reason.  Engineers have realised that they should look at the causes behind the capacity shortfall and not by its symptoms.

We won’t ease traffic if we just build more roads. 

We won’t benefit by just adding more machines or trucks. 

We need to address the causes that underlie the effects. 

We build not only to accommodate.  We build to solve. 

About Overtimers Anonymous

The Three Capacity Types

How much can we make?

How much can we buy?

How much can we deliver?

These are typical questions executives ask their managers all the time.  Executives often want straightforward answers; they’d rather be spared the complicated assumptions behind any of them. 

Calculating capacities can be a headache.  It’s never really as straightforward as a machine’s rate of production or how many items a person makes in a day.  Operators sometimes slow machines down or speed them up.  A shorter person may not make as much as a taller person.  Raw materials from one vendor may lead to higher output than that from another supplier. 

How executives view an enterprise’s supply chain capacity is also often different from that of employees.  Executives usually prefer what’s the most that can be produced and delivered.  Employees typically equate capacity with how much they have delivered in reality. 

Answering the questions of capacity therefore requires knowing what assumptions to base on and what data and formulae to use. 

I usually propose three types of capacities for enterprises:

  1. Maximum Capacity
  2. Operating Capacity
  3. Demonstrated Capacity

Maximum capacity is how much an operation can make or deliver assuming it runs at its highest designed rate all the time, that is, 24 hours a day, seven days a week, 365 days a year (366 if it’s a leap year).  No breaks, no shutdowns. 

maximum capacity = design rate x 24 hours/day x 365 days/year

Note that it involves the highest designed rate, that is, what the operation is engineered to do.  The design rate isn’t what it can actually do but what it’s supposed to be capable of. 

Operating Capacity is how much an operation can make or deliver assuming it runs at its highest designed rate based on a schedule.  Operating capacity computations are based on planned timetables but regardless of downtimes.

operating capacity = design rate x scheduled operating time

Note that operating capacity uses the highest design rate and 100% of the scheduled time.  Operating capacity does not take into account planned or un-planned downtimes, such as break-times or time lost during an operation for whatever reason.  For example, in a production process that has a design rate of 100 pcs per minute and is scheduled to run eight hours a day but with allowed breaks totalling 1-1/2 hours, the operating capacity would be:

operating capacity = 100 pcs/minute x 8 hours/day x 60 minutes/hour = 48,000 pcs/ day

Operating capacity does not factor in the break-time.  It does not consider any slow-down from the design rate. 

Demonstrated Capacity is based on the actual output of an operation.  It is determined by multiplying the actual operating time with the actual operating rate

demonstrated capacity = actual operating time x actual operating rate

The actual operating rate is the regular rate of output or what an operator or supervisor establishes as the equipment’s or workplace’s attainable output of items.  The actual operating time is the total amount of time the operation was running after deducting planned and un-planned downtimes.  For a production process that has a design rate of 100 pcs per minute, but an actual output of 5,000 pcs per hour that has a schedule of one eight-hour shift a day with 1-1/2 hour breaks, the demonstrated capacity would be: 

demonstrated capacity = (8 – 1.5 hours) x 5,000 pcs/hr = 32,500 /day

Demonstrated capacity does not take into account the design rate or the total eight (8) hour scheduled shift.  It only considers the actual operating time and actual rate of output.  It does not, however, deduct any unacceptable output (e.g. scrap, rejects). 

The Three Types of Capacity

Executives, especially financial managers, prefer maximum capacity when it comes to assessing how well an enterprise is utilising its assets.  If an enterprise’s supply chain schedules an operation at one (1) shift a day, it would be utilising at most one-third of an operations assets’ capability, which reduces the potential return on investment for the assets.  For an enterprise’s owners, that would be tantamount as wasted opportunity. 

Supply chain managers favour operating capacities in measuring efficiencies.  Operating capacities would be the baselines to determine how reliable operations are. 

Many operators and supervisors like demonstrated capacities for performance measurement.  Some would see operating and maximum capacities as unreachable parameters.  They’d instead measure their output against what they can attain, which would be demonstrated capacities.    

When it comes to determining what the capacity of an operation is, one has to be aware of who’s asking and what is being looked for.  Is it how much an operation is capable of? (Maximum Capacity).  Is it how much can be achieved at full efficiency over a planned time frame?  (Operating Capacity).  Or is it how much can one realistically count on to attain? (Demonstrated Capacity).

Enterprise executives, managers, and engineers may have their own versions on capacities.  It should be based on what one is after.  An executive seeking the best return on investment would have a different perspective from an operator who wants to know how much can really be done. 

Capacities apply to every operation.  Variables such as design rates can be tricky to determine, especially if the design rate is to be determined from labourers or logistics.  Supply chain engineers can help provide the data. 

That’s what they’re there for. 

About Overtimers Anonymous