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2018

Many of our clients have fast-paced operations and are experiencing rapid growth. They frequently struggle, in the midst of all of this, with figuring out their operational capacities and production schedules.

 

Here is a letter we recently sent to just such a client.

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Dear [client EVP]:

….

 

I recall [from our conversations] that some of the items that concerned you were the matters of capacities and scheduling regarding your production. You said, if I recall, that you had several people who were attempting to tackle the whole matter of the capacity/scheduling conundrum with less than the success you would like.

 

First of all, let me say that this is not at all unusual. And there are many reasons for such poor outcomes.

 

Unfortunately, most folks today are still trying to schedule using methods and algorithms that were designed for an entirely different set of market circumstances—namely, for the 1950s and 1960s. In those days, in most industries, market demand still outstripped production capacities; hence, whatever was produced could generally be sold in the not-too-distant future. It was a world where product variety was typically small; supply chains were typically short; and the consumer’s buying options were fairly limited.

 

In today’s world, all of that has dramatically changed: with extended supply chains; huge product variety and customization offerings; and customers can buy from nearly anyone in the world who has a product to meet their requirements.

 

Nevertheless, even in “the good old days,” production scheduling had its challenges. These challenges are well articulated in Factory Physics:

 

[U]nfortunately, most real-world [factory scheduling] problems violate the assumptions made in the classic scheduling theory literature [and their implementations in software] in at least the following ways:

        1. There are always more than two machines. Thus [the] algorithm for minimizing makespan and its many variants are not directly useful.
        2. Process times and demand are not deterministic. …[We have] learned that randomness and variability contribute greatly to congestion in manufacturing systems. By ignoring this, scheduling theory is based on an unrealistic model of system behavior.
        3. All jobs are not ready at the beginning of the problem. New jobs do arrive and continue arriving during the entire life of the plant. To pretend that this does not happen or assume that we "clear out" the plant before starting new work is to deny a fundamental aspect of plant behavior.
        4. Process times are frequently sequence-dependent. Often the number of setups performed depends upon the sequence of the jobs. Jobs of like or similar parts can usually share a setup while dissimilar jobs cannot. This can be an important concern in scheduling the bottleneck process.

 

[Hopp, Wallace J., and Mark L. Spearman. Factory Physics. Long Grove, IL: Waveland Press, 2011.]

 

Using traditional approaches to capacity management and production planning, your schedulers need to know—with reasonable accuracy—the following factors for every order that will pass through your production floor:

        1. Queue time – the average amount of time a production unit (or job) spends waiting for processing at each work center, or to be moved to the next work center
        2. Setup time – the average amount of time a production unit (or job) spends waiting for the work center to be set up for processing the unit (or job)
        3. Move time – the average amount of time a production unit (or job) spends being moved from the previous work center to the current work center in a routing
        4. Process time – the averaged amount of time a production unit (or job) spends actually being processed by the work center
        5. Wait times
          1. Wait-to-batch time – the average amount of time a production unit (or job) spends waiting to form a batch for either simultaneous processing or moving
          2. Wait-in-batch time – the average amount of time a production unit (or job) spends waiting in a process batch for its actual processing
          3. Wait-to-match time – the average amount of time a production unit (or job) spends waiting for other components for an assembly operation

 

Furthermore, you will note the repeated use of the term “average amount of time.” This highlights the fact that, for these times to be meaningful in calculating schedules and capacities, your processes must be in statistical control—that is, variations from the mean are small and clustered around the mean, as opposed to variations being large and not clustered around a mean.

 

In most cases, we find that 1) organizations do not know these “average times,” and 2) their processes are not in statistical control. (And, as you so accurately pointed out while I was on-site, time-studies are of little value as they generally do not reflect the real-world scenarios of day-to-day production cycles.)

 

While traditional scheduling methodologies attempt to produce a “precisely right” schedule based on calculations made from these supplied times (see above), the result is a schedule that is always precise and always wrong!

 

As a result of these recognized failing in traditional methods, by the mid 1980’s a new scheduling methodology was emerging. This method is commonly referred to today as “DBR” or “Drum-Buffer-Rope,” and it was articulated in the well-known business novel entitled The Goal—A Process of Ongoing Improvement by Eli Goldratt and Jeff Cox.

 

All DBR requires in order to provide you with an accurate assessment of your capacities and schedules in an “approximately right” (maybe, slightly liberal) estimate of the processing time on your system’s CCR(s)—capacity constrained resource(s). The CCR becomes the “drum,” setting the pace for all other operations, and a “rope” ties the drum to other operations. The “rope” is actually a time buffer, which becomes a buffer of inventory (in some cases). (Inventory is simply a way of storing the combination of time and capacity it took to produce the inventory.)

 

In a simplified schematic, DBR takes your operations, and they turn out looking like this:

DBR scenarios

 

A quick assessment of your shop floor has suggested to me that you have a flexible workforce that may be assigned to various work centers or work orders on an as-needed basis. If that is true, then the “Shipping Buffer” model would probably be most appropriate in your circumstances (provided, of course, that your wave solder machine, for example, does not become an internal CCR at any point).

 

Using the “shipping buffer” model, your total capacity could be calculated as the total number of working hours (man-hours) available for production work over any given period of time.

 

The shop floor load can be calculated as the sum of number of units * drum-hours per unit of production time. In your case, your “drum-hours” would be an “approximately right” or “safe estimate” of the time it takes for shop floor processing, plus any other task-related times.

Buffer - Day 1

 

Calculation of the load is pretty simple and, since [your ERP system] doesn’t do this natively, it could be done in Microsoft® Excel™ using data extracted via Excel’s data access capabilities from [your ERP database]. It might look something like this:

Drum Schedule as Table

In your case, “drum-hours” would be the total estimated load on your shop floor (as depicted in the “Constraint is the Market” scenario in the first diagram).

 

There’s more to this, of course, but I think you can get the general picture about how being “approximately right” using a DBR approach beats all attempts to be “precisely wrong” using traditional approaches to capacity planning and scheduling.

 

This approach gives you the wonderful combination of being both simpler and more accurate in assessing your real capacities to take on new work and setting estimated delivery dates.

 

We can discuss this further if you are interested. Just let me know.

 

I would also highly recommend the following reading. I think you will find some or all of it valuable and full of fresh insights for your management team.

 

Sincerely,

 

etc., etc.

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Is your internal supply chain too complex? Are you facing big challenges trying to figure out production floor capacities and schedules that actually work?

 

Don’t feel alone. There are many in this same boat, and there are excellent and well-proven solutions available. We can help.

 

Leave your comment below, or feel free to contact us directly, if you prefer.

 

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In working with our manufacturing and supply chain clients, I frequently encounter folks who simply are unaware of the effects that production batch and transfer batch sizes have on other elements of their business that they are trying to manage.Little's Law

 

If you don’t understand the cause-and-effect of your decisions, you will frequently make decisions that produce unexpected negative effects (UDEs or UnDesirable Effects). Worse! Your management team may not even be able to correlate Decision A with Effect B.

 

Here’s an excerpt from a letter to one of our clients as an example:

 

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…[I]f these deliveries are due on different dates, why does [company name redacted] want to enter a production work order for the full quantity? Typically, any given work order may have only one Required Date—and these quantities are clearly required on different dates.

 

I’m not a big fan of traditional MRP (Material Requirements Planning). Nevertheless, if it were to be used, even traditional MRP would see the demand on different dates and, depending upon the MRP options on the routing (e.g., Batch Size, Max Qty, Min Qty) and the Planning Period Days, MRP would then suggest several different production work orders to satisfy the demand based on the required dates.

 

AVOIDING GREEN GIANTS

Satisfying the demand incrementally actually may help prevent another issue from arising, as well: Big orders are generally good for business. However, a big order may also end up becoming a “green giant” on the shop floor. A “green giant” is a big order being processed in a single batch that is blocking the timely completion and delivery of several other customers’ orders that may be smaller, and could easily have been delivered on time, were it not for the “green giant” standing in the way and occupying the required resources. Building the large order incrementally ends the “green giant” scenario.

 

Does your company build the whole order in a single processing batch?

 

UN-DESIRABLE EFFECTS (UDEs)

If it does, the large batch size is also tending to increase your WIP (work in process) inventory. This, in turn, according to Little’s Law, increases your cycle time (CT) for production, as well. This is especially true if, as you say you do, you make your transfer batch between work centers the same size as your production batch.

 

Little’s Law states that WIP is directly correlated to throughput time and cycle time, where throughput time (TT) is the average output of a process per unit of time, and cycle time (CT) is the average amount of time from when a job is released for production until it exits the production process. The formula is:

 

WIP = TT * CT

 

For example, if a process produces 3.1 units per hour (TT), and the total time from release to the process until the exit from the process (CT) is 2.8 hours, then the average queue length or amount of WIP is 3.1 * 2.8 = 8.68 units.

 

However, if the cycle time (CT) is 20 hours, then the average WIP (or queue length) must be 3.1 * 20 = 62 units.

 

USING LITTLE’S LAW TO REDUCE CYCLE TIME

Little’s Law may be rewritten as:

CT = WIP / TT

 

Therefore, you can manage WIP lower (by releasing the shop floor and processing in smaller batches), and as long as TT remains unchanged, CT also decreases.

 

Why?

 

Because as WIP falls, queue time and wait time decrease. Things being processed on the shop floor spend less time sitting—non-value-added time—and more time actually being worked on—value-added time. This is one of the clear principles of LEAN, but it doesn’t require a LEAN implementation to gain the benefit. It simply requires understanding Little’s Law and a willingness to reduce process and transfer batch sizes.

 

WHAT ABOUT SET-UP TIME and EFFICIENCIES

Could reducing batch sizes lead to more set-up time?

 

Clearly, the answer is, “Yes.”

 

However, additional set-up time is only critical at two points:

      1. At a CCR (capacity constrained resource)
      2. At a non-CCR when the sum of run time and set-up time begins to exceed available time (in which case you begin making the non-CCR a new CCR)

 

At a non-CCR, set-ups are essentially “free,” in the sense that (until the point of number 2 above is reached) additional set-ups add nothing to your operating expenses or to your truly variable costs of production. “Efficiencies” at non-CCRs are a mirage and earn nothing for the company. Not doing additional set-ups at a non-CCR actually saves the company no money at all.

 

I know this is a long answer to your question, but I believe these matters are worthy of consideration and that how a company executes is even more important than how they configure their ERP system.

 

Let me know if you have further questions or concerns on this matter.

 

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Now it’s your turn

So, how are you managing batch sizes, WIP inventory levels, and cycle time? We would be delighted to hear your comments. Please leave them below, or feel free to contact us directly, if you prefer.

 

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