By Mike Hart
While I was waiting for my order at In-N-Out Burger today, I observed their process workflow from a manufacturing perspective.
In-N-Out is a highly successful hamburger chain located here in the western United States. One of their sites is within walking distance from my house. From what I can tell, a typical operation deploys 15-25 people performing various functions very much like those in a factory.
In fact, food preparation is an extreme form of make to order, just in time manufacturing. The production process is driven by customer orders, which are received through a drive through line and walk up cash registers, which function as the order department.
Custom orders are readily accepted, such as with onions or without, as well as a variety of off-menu variations, such as burgers “animal style”, “protein style”, “well done” fries --- customized options that change each job’s specifications. Each “job”, of course, is the customer order, which is assigned an order number.
A mix of “push” and “pull” planning is used to shorten delivery times. On the push side, burger patties are made to stock in anticipation of future demand, which is fairly predictable during peak lunch and dinner hours. On the pull side, french fry cooking is triggered by bin level replenishment, kanban style, and is a batch process. Potatoes are fresh cut as needed to replenish staging areas.
The key to maximizing throughput is to identify bottlenecks and remove their associated constraints. In-N-Out has recognized that the drive through line is a bottleneck, constrained by the single line that creates occasionally long queues. To moderate this constraint, at peak times order takers walk out to the queue and take orders well in advance of cars reaching the order station. Production begins immediately, in advance of payment.
The ultimate capacity constraint is the grill. Only so many patties can fit on the grill at one time and require a minimum cooking time. To speed up cooking times, In-N-Out uses thin patties, six to a pound, and doubles or triples them up for customers who want a bigger burger. During peak hours, the grill is never left idle or unmanned, because ultimately, what determines the speed of all customer deliveries is the ability for burgers to emerge from the grill.
Another constraint is the time it takes to fill drinks. In the sit down area inside the restaurant, this constraint was removed by giving customers their own cups and letting them fill their own drinks.
Another constraint to throughput can be product complexity. Making pizza, fried chicken, and burgers within the same factory is a challenging proposition. In-N-Out has resisted the temptation to offer something for everyone and only manufactures burgers, fries, and drinks – period. Not only does this simplify planning and execution, the narrow product focus creates a perception among customers that In-N-Out is the burger specialist, the best at what they do.
To maximize efficiency, productivity is measure by overall throughput, and not by individual worker. The drink filler, for example, needs to fill drinks rapidly as demand materializes. The demand flow is not under that worker’s control. From time to time, the worker may be idle. If the worker was expected to fill so many drinks per hour as a mistaken notion of efficiency, the worker would respond by making as many drinks as possible to look good on paper. The result, though, would be to create excess inventory of pre-filled drinks, which would clog up staging areas to the detriment of the factory as a whole.
Workers are paid well above the fast food industry average. In-N-Out recognizes that the way to reduce labor costs is not to pay lower wages, but to increase volume so that the fixed cost of a labor crew can be spread over more units of production.
IT ties this all together. In-N-Out uses point of sale technology for order entry, product configuration, and production scheduling.
In-N-Out is a simplified version of what happens in a real factory, but the principles are the same – identify bottlenecks, remove constraints, maximize throughput to reduce labor and overhead costs, measure system throughput rather than local optima, shorten delivery times with push and pull planning focus on profitable products.
So what did I order today? I try to stay low-carb, so I went for a double-double burger, protein style and animal style. They took my special order without blinking an eye and delivered it perfectly cooked, about seven minutes later. And that is what efficient manufacturing is all about.
Mike Hart is the co-founder and President of DBA Software Inc., a leading provider of manufacturing software for small businesses.
Custom orders are readily accepted, such as with onions or without, as well as a variety of off-menu variations, such as burgers “animal style”, “protein style”, “well done” fries --- customized options that change each job’s specifications. Each “job”, of course, is the customer order, which is assigned an order number.
A mix of “push” and “pull” planning is used to shorten delivery times. On the push side, burger patties are made to stock in anticipation of future demand, which is fairly predictable during peak lunch and dinner hours. On the pull side, french fry cooking is triggered by bin level replenishment, kanban style, and is a batch process. Potatoes are fresh cut as needed to replenish staging areas.
The key to maximizing throughput is to identify bottlenecks and remove their associated constraints. In-N-Out has recognized that the drive through line is a bottleneck, constrained by the single line that creates occasionally long queues. To moderate this constraint, at peak times order takers walk out to the queue and take orders well in advance of cars reaching the order station. Production begins immediately, in advance of payment.
The ultimate capacity constraint is the grill. Only so many patties can fit on the grill at one time and require a minimum cooking time. To speed up cooking times, In-N-Out uses thin patties, six to a pound, and doubles or triples them up for customers who want a bigger burger. During peak hours, the grill is never left idle or unmanned, because ultimately, what determines the speed of all customer deliveries is the ability for burgers to emerge from the grill.
Another constraint is the time it takes to fill drinks. In the sit down area inside the restaurant, this constraint was removed by giving customers their own cups and letting them fill their own drinks.
Another constraint to throughput can be product complexity. Making pizza, fried chicken, and burgers within the same factory is a challenging proposition. In-N-Out has resisted the temptation to offer something for everyone and only manufactures burgers, fries, and drinks – period. Not only does this simplify planning and execution, the narrow product focus creates a perception among customers that In-N-Out is the burger specialist, the best at what they do.
To maximize efficiency, productivity is measure by overall throughput, and not by individual worker. The drink filler, for example, needs to fill drinks rapidly as demand materializes. The demand flow is not under that worker’s control. From time to time, the worker may be idle. If the worker was expected to fill so many drinks per hour as a mistaken notion of efficiency, the worker would respond by making as many drinks as possible to look good on paper. The result, though, would be to create excess inventory of pre-filled drinks, which would clog up staging areas to the detriment of the factory as a whole.
Workers are paid well above the fast food industry average. In-N-Out recognizes that the way to reduce labor costs is not to pay lower wages, but to increase volume so that the fixed cost of a labor crew can be spread over more units of production.
IT ties this all together. In-N-Out uses point of sale technology for order entry, product configuration, and production scheduling.
In-N-Out is a simplified version of what happens in a real factory, but the principles are the same – identify bottlenecks, remove constraints, maximize throughput to reduce labor and overhead costs, measure system throughput rather than local optima, shorten delivery times with push and pull planning focus on profitable products.
So what did I order today? I try to stay low-carb, so I went for a double-double burger, protein style and animal style. They took my special order without blinking an eye and delivered it perfectly cooked, about seven minutes later. And that is what efficient manufacturing is all about.
Mike Hart is the co-founder and President of DBA Software Inc., a leading provider of manufacturing software for small businesses.
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