Breakthrough in Bottleneck Analysis using Industry 4.0

Serial production has become the norm in the design of manufacturing processes in modern times.

The serial lines work by serially interlinking machines carrying out different operational steps through part transport (conveyors, robots, trolleys), such that multiple machines are operating on different operating steps of different parts parallelly, to deliver one finished part at the end of the line at a "takt time" that defines the production rate of the line. It is a common practice to name the operational steps as OPs, numbered serially in intervals of 10 (like OP10, OP20, etc) to enable any additional OPs to be inserted in the sequence later on.

The takt time of any individual operating step is defined by two factors:

The production rate of various assets involved in completing the step (machines, transport, and tools)

The rate of inputs to that step (operators, sub-parts being added on the line)

The "constraint" (also called the bottleneck) in the line is that operating step which takes the maximum time in the serial operations. Both its upstream and downstream neighbors produce faster than this operating step.

This means that the faster upstream processes are blocked, i.e., forced to wait for the bottlenecks in the manufacturing process to catch up, whereas the downstream processes are starved, i.e., not receiving parts at the rate they could handle. This way, the entire line experiences idle time while the bottleneck asset in manufacturing continues to operate at its limiting pace.

"Identifying the bottleneck is the key to optimizing manufacturing performance."

The bottleneck, being the slowest step, sets the pace for the entire production line. Hence, improving the efficiency of the bottlenecks in manufacturing operations directly enhances the productivity of the entire production line.

Conducting a detailed production bottleneck analysis can help pinpoint these inefficiencies more precisely, guiding improvements where they matter most.

Improving the performance of non-bottleneck processes beyond the pace set by the bottleneck will increase idle time and not improve production.

Eli Goldratt's book, The Goal, famously made this Theory of Constraints approach popular in manufacturing.

Identifying the bottleneck is the key to optimizing manufacturing performance

Are pre-determined bottlenecks in manufacturing a convenient solution?

Since it is extremely difficult to manage lines with shifting bottlenecks in manufacturing operations, line design practices propose a "managed bottleneck" approach — intentionally creating a single bottleneck by design ("design bottleneck") at or towards the end of the line. At least in theory, this allows only one process to be focused on to be run optimally.

However, this convenience comes at a cost because it would mean overdesigning all other assets, effectively using them ineffectively. Hence, this approach is rarely practiced.

Furthermore, by design, only the performance bottlenecks in manufacturing can be pre-designed. The availability & quality bottlenecks are not expected through high reliability and high process capability machines to ensure relatively low impact to cause bottlenecks. Whenever a high impact or risk is anticipated, a "buffer" is designed to store the parts before & after the process step itself for a higher throughput than the expected line throughput.

Industrial engineering approach has multilateral (and often hidden) challenges

A measure of line performance, OLE (Overall Line Effectiveness) and individual asset performance, OEE (Overall Equipment Effectiveness), captures these performance measures, allowing the manufacturing engineer to evaluate how the line is performing.

When the performance of the line is lower than expected, it is required to identify the bottleneck in the manufacturing process that is causing the performance loss. If the bottlenecks in manufacturing are "persistent" (consistently present with statistically repeatable behavior), there are industrial engineering techniques available for identification:

• Performance bottlenecks in the manufacturing process can be identified using cycle time study, filtering out the waiting times of the asset to compute the net cycle time of the asset.
• Availability/MTBF/MTTR studies will capture downtime bottlenecks.
• FTQ studies can capture quality-related bottlenecks.

IIoT (Industrial Internet of Things) — one of the pillars of Industry 4.0 — aims to boost the manufacturing performance through use of data. It promises not only to enable industry to reach the elusive 85% OLE, but to set a new standard for industry that will exceed 85%.

In a series of mini bulletins that follow, we cover the principles and examples of this improvement through use of IIoT — specifically the Linecraft AI product suite that enables this transformation.

Traditional approach to identifying the bottleneck operation

Breakthroughs in bottleneck analysis have changed the way manufacturers identify and address production constraints, rendering the "managed" bottleneck approach obsolete.

The "managed" bottleneck is no longer supported by the industry, since this makes all the other assets idle, wasting the effectiveness of their investment. As a result, serial manufacturers build "balanced lines" (no design bottleneck) where all the operational steps are designed to perform very close to the required takt time.

In a balanced line, there are "transient" variations in performance — which aren't systematic issues with the line but instead, short-lived disturbances caused by temporary factors. Transient variations or operational variances lead to some mismatches in time-to-time production rate. This makes it very difficult to pinpoint the bottleneck because each time the performance of the line is monitored, different observations are captured.

What makes the problem significant is that in a serial line, the losses are multiplicative. Any stoppage on the line will cause partial blocking & starving of all other assets.

For instance, let's assume that all assets are operating with 97% OEE, but if their slowdowns and stoppages happen at different times, the OLE of the serial line of a 20-step process will drop down to a meagre 54%.

Multiple such issues happen in multiple assets at multiple times under different conditions. Trying to fix the issues as they are being observed ends up in a "whack-a-mole" approach — a lot of effort, but no result. If there was a way to identify "top issues" and work on them selectively, this process can become manageable.

What causes the "Transient Bottlenecks"?

There are various use cases in practice which, although they might happen in only 5–10% of the cycles on each asset, their cumulative impact on line performance can be devastating. A few common examples are captured below:

The new approach to Bottleneck analysis — using Linecraft AI

Industry 4.0 tools have emerged to help solve problems that were previously thought unsolvable. These tools are based on analyzing a large volume of data over a long period of time, eliminating the limitations of not having detailed-enough information, or enough time of information.

The most logical solution for identifying a bottleneck is to monitor the "flow."

Structural bottlenecks in the manufacturing process will cause low flow throughout the line, but transient bottlenecks will cause dynamic increases and decreases in flow at different sections of the line.

Industry 4.0 tools enable this very analysis, providing a direct view into both types of bottlenecks in manufacturing operations.

Linecraft AI is one such analysis system, with a unique algorithm that can distinguish the time attributable to an asset's own operation from the time lost to the effects of "external" factors not intrinsically part of the asset's operation. This enables the "flow" through the assets to be modeled by the "waits" caused in the asset, forming patterns over larger periods of analysis for:

Structural bottlenecks can be eliminated through asset-level optimization — whether by cycle time optimization, or by Pareto analysis of availability losses and elimination of the root causes of specific reliability issues.

For transient bottlenecks, the main causes need to be found.

Linecraft's analysis surfaces high-impact and high-frequency issues, enabling manufacturers to identify transient bottlenecks faster than ever. Once identified, the improvement approaches can then be: