Theory of Constraints, The Critical Chain and Manufacturing Analytics

The theory of constraints (TOC) is an overall management philosophy introduced by Eliyahu M. Goldratt in his 1984 book titled The Goal, that is geared to help organizations continually achieve their goals. Goldratt adapted the concept to project management with his book Critical Chain, published in 1997.

Identifying the primary constraint and the critical chain is an important step in improving the operational effectiveness of a manufacturing plant. A plant manager is not just concerned with improving the OEE of individual machines. His primary concern is ensuring that high quality products are shipped from the plant on schedule by keeping costs in control. Typically, in manufacturing plants, we have lines of machines operating in sequence such that the output from one machine is consumed by the next finally resulting in a finished product. A simple example would be casting followed by machining, heat treatment and finally coating. For products involving assembly of multiple components, the assembly process could be dependent on output from multiple machines. Improving the efficiency or effectiveness of individual machines would definitely increase their output but that is not the goal. What is more important is shipping high quality end products as per the required schedule.

Applying TOC and the critical chain principles helps us achieve these goals with a well-defined approach.

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Step 1 – As a first step, one must identify the various chains (machines/equipment operating in sequence – output of one machine being consumed by the next) and their interconnections which finally result in the end product(s). The performance of the chain is dependent on the weakest link in the chain. That means, the slowest machine or the process taking the most time in the chain will determine how fast the line can churn out an end product. Hence it is important to determine the primary constraint.

Step 2 – The next step is to exploit the constraint which means squeezing as much as possible from it. This implies maximizing the OEE of that constraint. Note that focusing on other machines or other equipment without addressing the OEE of the weakest link is waste of efforts and counter-productive. This would typically lead to stack-up of WIP inventory.

Step 3 – In this environment, we realize that the other machines in the chain would be producing faster than what can be consumed by the constrained machine. If not controlled, it would lead to WIP inventory as mentioned above. This can be controlled by constraining the release of raw material and reducing the operational schedules of the other machines.

Step 4 – Once the 3 steps mentioned above are followed and we have exhausted the resources available to us, the only option to increase the effectiveness of the chain is to “elevate the constraint”. This would be done by adding more capacity at this constraint by investing in more resources, adding a machine, etc. Jumping directly to step 4 without following the first 3 steps is a risky investment.

Step 5 – A important point at this juncture is the realization that the weakest link or the constraint in the chain no longer remains so after elevation. The weakest link shifts to the some other equipment in the chain. Now, we go back to step 1 and repeat the earlier steps thus beginning a cycle of continuous improvement. In each continuous improvement cycle, we can start with a specific goal and keep on targeting higher goals with each cycle. The final objective is to delight customers and exceed overall business objectives.

This is similar to the AGILE approach discussed on of our earlier article – incrementally and iteratively increasing the overall plant effectiveness. To achieve this objective, it is important to have the right systems and tools in place to monitor and measure equipment available, performance and quality. Having an analytics system in place over the data collection capabilities provides abilities for the taking the right decisions related to identifying the critical constraints, exploiting them, elevating them and detecting the shift of the constraint.

An effective manufacturing data analytics solution helps at each step of the TOC process. Note that we are assuming that the constraints are primarily internal and within our control. External constraints can be addressed by widening the scope of the solution and influencing external stakeholders.

Visually model the various chains (lines) and connection in the solution. Identify the critical chain and the primary constraint in the chain. The manufacturing analytics solution readily measures, calculates and displays the relevant parameters required for this process, viz. OEE, cycle time, etc. The output of the chain is constrained by the slowest equipment or process in the chain i.e one which has the highest cycle time. In addition, any other loss in availability, performance and quality further impacts the cycle time negatively. Thus the weakest link in the chain is the equipment with the highest effective cycle time.

This takes us to the next step which involves exploiting the constraint. Increasing availability, performance and quality of the system are actions which will assist in reducing the effective cycle time. The manufacturing data analytics solution is valuable in this situation by helping us in proactively planning maintenance activities, alerting us in case of any potential failures, detecting differences between ideal and actual cycle times, identifying operations with lower feeds and speeds, etc. We have discussed the typical losses associated with OEE in earlier blogs. This helps us get more out of the existing set-up. Various actions involved in this step could include improved tooling, experienced operators, adequate consumables and raw material, etc. Functionalities in the manufacturing analytics solution such as ticketing, knowledge base help improve the response times. Live dashboards highlight the current equipment status and help identify equipment needing urgent action.

Once we have exploited the constraint to the fullest, we can think about elevating the constraint as mentioned in the earlier section. In case, the step of exploiting the constraint changes the constraint, we have to go back to step 1 taking the new constraint into account. Having a machine monitoring and manufacturing analytics solution in place significantly reduces the time required for measurements and improves response times. Moving between steps becomes easier and an objective measurement process with a centralized dashboard increases transparency across the plant floor. Rather than compete with each other and focus on machine efficiencies, operators focus on a common goal of increasing the throughput of the complete line, operating as a team and building the team morale and confidence.

In the long run, stability of constraints is important since frequently changing constraints will prevent stability of processes and consistencies in procedures. Thus, one can plan our strategies and approach based on steady state goals with progressively planned improvement cycles.

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