Conquering Manufacturing Bottlenecks with Industrial IoT

Conquering Manufacturing Bottlenecks with Industrial IoT
Conquering Manufacturing Bottlenecks with Industrial IoT
What I find interesting in the manufacturing industry is that while technology is continually changing, the basic principles of how to run an effective plant floor operation have not changed very much. 
These foundational principles were articulately described in the 1984 book “The Goal: Excellence In Manufacturing,” written by Eliyahu M. Goldratt and Jeff Cox. The book tells the story of a manufacturing production manager named Alex who is managing a production process suffering from high inventories, lack of production predictability and unhappy customers caused by fulfillment delays. Alex has been given 3 months to turn his plant around and his prospects for doing so are looking grim. By chance, Alex meets his former high school physics teacher in an airport, who then begins to mentor Alex regarding the key fundamentals required to run an effective plant. These fundamentals, which continue to resonate in today’s manufacturing environment, teach Alex how to improve throughput through bottleneck optimization.
While bottlenecks still exist in today’s manufacturing environment, the good news is that the Industrial Internet of Things (IoT) is providing new ways to solve them.


What is the major challenge on the factory floor?

The factory floor’s main challenge is the optimization of throughput to reduce inventory and reduce operational expense. Those in the manufacturing industry may be familiar with these terms, but for those of you that may not be as familiar I’ll take a moment to help define them. Throughput is the rate the system generates money through sales. Inventory is all of the money invested in things that are intended to be sold.  Operational expense is the money spent to turn inventory into throughput. It is worth noting that the word “money” recurs in each of these definitions.
Most Industry 4.0 initiatives are built around the goal of optimizing throughput, and for good reason. During the journey of throughput optimization many benefits are realized.  When throughput becomes optimized, inventory reductions occur, both at the start of the line (raw materials) waiting to enter the system, as well as inventory waiting in the system (or work in process inventory). Less inventory means less money tied up and more money available for investment.
Additionally, optimizing throughput leads to more effective maintenance programs for plant floor machines, which results in less unscheduled downtime, which in turn leads to a more accurate prediction of the rate of completion for product being generated by the system. This predictability allows safety stock to be reduced at various points throughout the line, as well as in the finished inventory area, which again reduces the cost of carrying work in process and finished goods. Additionally, less downtime means less idle time of workers and machines, which reduces the cost per finished good, putting more money as profit into the business.
In order to optimize throughput, the bottlenecks in the production system or manufacturing process must be identified and addressed. A bottleneck is defined as “any resource of which its capacity to produce is less than the demand placed on it” and is the slowest part of a given system or process (Figure 1). 

Figure 1: A bottleneck is any resource whose capacity to produce is less than the demand placed on it in the assembly line.
Some causes of bottlenecks include machines that do not have enough natural capacity to handle the demands being placed on it, machines that break down often or require extensive downtime which negatively impact throughput, or machines that require non-value-added tasks that slow the process down.  These non-value-added tasks may include manual set up delays, overly complex procedures, or excessive manual intervention.

Finding and Fixing the Bottleneck

A common pitfall that often occurs with Industry 4.0 initiatives is applying shiny new technology into the plant floor without addressing the bottleneck in the system. If you don’t address the bottlenecks in your process you might as well not implement any changes at all. You can champion continuous improvement, but if you’re not addressing the bottleneck your returns will be limited.
So, how are bottlenecks found? Most often, production processes will fall into two possible scenarios. The first scenario contains processes that already collect a myriad of data points through manufacturing execution systems, statistical process control systems and SCADA systems. I call these “data dense” processes.
The second scenario deals with production processes that have little or no data collection. We call these processes “data poor”. We’ll talk about each scenario, starting with the data poor processes.
In my discussions with manufacturing customers, I often find that many of these customers have little or no data collection from their manufacturing and industrial processes. These are not just small companies, many of these companies have a multi-national presence with hundreds or thousands of employees. In these cases, basic data collection using an IoT platform can make a huge difference in identifying their bottlenecks and the barriers to throughput optimization.
Typically, customers like these will begin to collect operational equipment efficiency data like cycle time, time blocked, time starved, part counts, fault codes, downtime and uptime.  They also implement basic alerting to inform key personnel when certain operations go down. The basic data collection allows these customers to begin to paint a picture of what is happening on the shop floor and will help to identify bottlenecks in the process. Basic alerting helps avoid situations where machines go down and no one knows until someone walks by the machine and sees that it is not working – often indicated by a puddle of fluid on the floor by the machine (Figure 2).

Figure 2: Factories who are “data poor” can start with simple data collection and mobile alerts to develop a picture of what is happening inside of their production processes.
Once basic data collection and alerting are implemented, the information that is collected can be used to help identify bottlenecks in the process and direct resources to address the right process areas, thus maximizing return on investment by improving those areas of the production process that will do the most to increase throughput.
On the other side of the coin are our customers who are “data dense”. These customers have data from a number of different sources, but often times this data is siloed and kept segregated from people who would benefit from using it.  These customers also realize tangible benefits from the implementation of an IIoT platform as well. We call this moving from data dense to being “data precise”.
When data is siloed, it often creates organization silos as well. These data silos then inhibit collaboration and holistic problem solving across a production system.  Decisions are often made using a subset of the data that people are comfortable with and have access to, rather than fostering a system thinking approach to problem solving. Next generation throughput improvement requires “all hands-on deck” and people working together with all data that is available.
Using an IIoT platform can provide a consolidated data picture from all sources. This breaks down the barriers of previously siloed teams by allowing them to immediately use all available data to identify specific changes which can improve overall performance across the operations. Additionally, the high level of transparency through data exposure will increase the trust between all teams, breaking down the barrier of suspicion and enable all teams to effectively collaborate to improve overall operational efficiency. Data silos become joined to provide a full 360 degree of operations on the plant floor.  All resources will be able to see the big picture instead of a limited view contained within their data silos (Figure 3).

Figure 3: An edge computing platform can give a plant management a full picture of the process data.
The increased data visibility also helps to accelerate the DIKW (Data, Information, Knowledge, Wisdom) cycle. This in turn accelerates the feedback loop to the processes on the shop floor, which expediates throughput optimization, leading to reduced costs and faster inventory turnaround. As well, as the levels of visibility and transparency of production data continues to rise better decision-making techniques occur.

Figure 4: Edge computing platforms help the entire manufacturing team work together to improve the process.
No matter if an organization is data dense or data poor, using an IIoT platform can help them with throughput optimization. Facilitating data sharing at any level will enable the breakdown of organizational barriers which will result in optimum decision-making by the organization. This will help avoiding the tendency to throw technology at a perceived problem without understanding where the problem is coming from or even what the problem is. This data sharing mentality will also institutionalize methods for continuous process improvement as you tackle the never-ending bottleneck challenges within your processes.
In conclusion, throughput optimization is the key to reduce inventory and reduce operational expense, while maximizing cash flow. Throughput is optimized by finding and addressing bottlenecks. Finding and optimizing bottlenecks requires data collection and analysis, and this is best achieved through data sharing to enable horizontal collaboration within the factory. If you are “data poor”, take the first steps to achieve this using an IIoT platform like LoopEdge. If you are “data dense”, bring together disparate sources of data for a 360-degree view of your operations so you can transition to being data precise. 

About The Author

Marc Dekker is the Senior Technical Account Manager at Litmus. He has 29 years of experience in the manufacturing and IT industry, a degree in Business Administration from Husson University, and a diploma in Information Systems from Niagara College. He can be reached at

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