Wednesday January 11, 2017
Twin Killers in Manufacturing
As every production manager is aware, it’s not easy to achieve a smooth and continuous flow of products in concert with market demand. Although managers are aware of this, what is generally unknown or recognized is the major obstacles to running an effective operation all boils down to two basic phenomena that exists in all manufacturing operations. These two phenomena are referred to as dependent events which include the interactions between resources and products and statistical fluctuations which causes excessive amounts of variation. In order to improve our decision-making ability, it’s necessary to understand the nature of both dependencies and variation that permeate all production firms. So let’s explore both of these phenomena in more detail.
In any manufacturing operation there are numerous dependencies that exist. Dependencies are those sequence of operations or activities in any plant that cannot occur until some other operation takes place and has been completed. These interactions between dependent events play a vital role in the smooth flow of materials through a manufacturing process. If a preceding operation is delayed, then many times the end product will be delayed and shipment to the customer could be late. There are many examples of these dependencies within every manufacturing facility. For example, the production process cannot begin until the raw materials are received, accepted, and sent to the first step in the process. Likewise, the raw materials cannot be received until an order is placed, the raw material is produced, payment is received, etc. And what happens when a quality problem is detected in any step in the process? The root cause of the problem must be found, impacted materials must be reworked (or scrapped) or new materials must be supplied.
The significance of dependencies within the manufacturing process is magnified immensely by another reality, the unavoidable existence of variability in the form of both random events and statistical fluctuations. Random events are those activities occurring at unpredictable times that have a significant disruptive effect on the entire manufacturing process. Random events occur in the process from many different sources. For example, suppose a customer order is suddenly canceled? What problems result from this cancellation?
In any manufacturing facility there are events referred to as statistical fluctuations that wreak havoc on the shop floor. It’s important to understand that these fluctuations occur that cause higher levels of variability. Typical examples include things like actual customer orders being different than what has been forecasted; the time required to process materials at a work station is different from the planned time; or even the time to set-up a machine is different than the predicted amount.
Although random events and statistical fluctuations are different phenomena, they both cause variability and if the process is poorly controlled, they both send shock waves throughput the system. Production managers are constantly on guard for both of these variation producers that cause disruptions and adjustments to their planned activities. So what’s the impact of dependency and variability?
Dependency and Variability Impact
In order to demonstrate the damaging effects of dependency and variability in a manufacturing system, let’s create a simple analogy. Imagine a basic production line that only produces a single product, uses only one raw material, and has a single sequence of dependent operations. The figure below describes this simple process.
Raw material is received, processed by the first resource, transferred to the second resource, then the third and so on until a finished product is created. Each step is dependent upon the preceding step. And the impact of any variation in time or quality between steps is felt by all downstream operations. Downstream resources fall further and further behind the work schedule as the disruptions and negative fluctuations accumulate throughout the system. One obvious effect is the accumulation of work-in-process inventory which translates into lost or reduced throughput. One important point is that negative variations from the scheduled flow of product will accumulate more readily than positive variations will. The definitive result is that as the flow of products is disrupted, throughput decreases, excess work-in-process forms and operating expenses increases.
One of the teachings of Lean Manufacturing is the concept of the balanced manufacturing plant, meaning that all process steps are close to having the same processing times. Is this a good idea?
In my next post, we will discuss the balanced plant concept in more detail as well as the effect of disruptions in a balanced plant. As always, if you have any questions or comments about any of my posts, leave me a message and I will respond.
Until next time.
 L. Srikanth and Michael Umble, Synchronous Management – Profit-Based Manufacturing for the 21st Century, Volume One – 1997, The Spectrum Publishing Company, Wallingford, CT