Management of Bottlenecks in High-Mix, Low-Volume Production Systems

Bottlenecks keep shifting in many high-mix, low-volume production units. Most of those units are high-variety, make-to-order (MTO) production units driven by order due dates. Bottleneck formations occur due to rapid changes in workload on shop floor caused by a stream of diverse orders (with varying process requirements and stipulated lead times). Bottlenecks cause disruptions in the flow of orders on shop floor and consequently, increase WIP and production lead times of orders and reduce throughput and on-time delivery. We can reduce bottlenecks and their negative effects by predicting bottleneck formations, performing proactive capacity planning and setting right focus on right resources at right times (for improving the productivity of bottleneck resources).

Theory of Constraints Approach to Bottleneck Management

The theory of constraints (TOC) is one of the approaches to bottleneck management in production systems. It usually works with a model of production system in which there is a single constraint with adequate subordination of all other resources to it. In this model, the issues with shifting bottleneck do not arise because its effect will be indirectly addressed by real-time buffer management. Production control and management becomes simple and easy with this model and therefore, TOC is considered as a simple solution for production management. However, over the last 35 years, a vast majority of job shops engaged in HMLV production did not transform their production into this simple and convenient model for implementing the simple TOC solution although they constantly look for simple solutions. This is because ensuring subordination is not easy and economical for small and mid-sized job shops in general. Buffer management in TOC implementation becomes less effective when shifting bottlenecks occur due to high known variation in process requirements of orders.

For any given resource capacities, Schedlyzer generates optimal production schedule and predicts bottleneck formations and displays them on Gantt chart. To improve WIP, lead times, throughput and on-time delivery, capacity planning based on the prediction of bottleneck formations will be very useful. It helps set right focus on right resources at right times.

Lean Manufacturing Approach to Bottleneck Management

Takt time calculations are ineffective for production control when orders are very diverse with large known variation in process requirements and they must be delivered by stipulated due dates.

When an industry keeps receiving a stream of diverse orders with large known variation in process requirements and specific due dates, bottlenecks are quite likely to shift on shop floor over time. It is not easy to convert customer demand into Takt time for high-variety production driven by specific order due dates. In many HMLV systems, it is not easy to smoothen the demand for product families for the purpose of production leveling due to stringent due dates of individual diverse orders. It may not be possible to enhance capacities of resources (or work centers) as much as required and whenever needed for managing shifting bottlenecks. Pull system does not help with prediction of bottleneck formations and proactive capacity planning to reduce bottlenecks.

Other methods like POLCA of Quick Response Manufacturing and CONWIP do not help with identification and proactive management of shifting bottlenecks. Prof. Christoph Roser did some research on identification of bottlenecks in production systems. References to his work are given at the end of this document.

Finite Capacity Scheduling (FCS) Approach to Bottleneck Management

Forward finite capacity scheduling (FCS) identifies shifting bottlenecks in production systems described above. Forward FCS can be described as perfect push system for production control. Although forward FCS is not appropriate for actual production scheduling in general, it can be implemented with the help of FCS software exclusively for the purpose of bottleneck identification.

Schedlyzer is a powerful scheduling software tool that implements forward FCS for bottleneck identification and implements optimal FCS for actual resource-constrained production scheduling. A summary Gantt chart in Schedlyzer clearly reveals shifting bottlenecks in production when Schedlyzer implements forward FCS logic. The following diagram contains a summary Gantt chart of 100 jobs which have to pass through a sequence of 11 operations on a production line. Operations are shown in distinct colors. Any yellow segment on a horizontal line represents waiting time of a job for an operation.

Shifting Bottlenecks in Gantt Chart

Whenever the resource for an operation becomes bottleneck, jobs have longer waiting times at that operation as shown in yellow color. The above chart is produced by Schedlyzer after scheduling 100 jobs.

A resource utilization chart shown below displays daily resource utilization levels in green color. Each column corresponds to a day. Yellow color for any resource on any day represents the amount of resource idle time on that day. Gray color represents unavailable time of the resource on a day. This chart also shows permanent and shifting bottlenecks.

Shifting Bottlenecks in Resource Chart

Schedlyzer shows through what-if analysis how capacity enhancements of certain resources reduce bottleneck formations and improve throughput, WIP, production lead times of orders and on-time delivery. The summary Gantt chart and the resource utilization chart reflect the changes in bottleneck formations. It may be noted that capacity cannot be increased for any resource as much as required.

For any given resource capacities, Schedlyzer also helps determine minimal achievable lead time for a new order based on process requirements of the order and the existing workload. It also finds an optimal material release time for each order to control production lead time and meet order due date. For any new job with the least priority, Schedlyzer will also show how much time the job will have to wait at each operation if material is released right away.

To generate an optimal schedule and perform what-if analysis reliably and efficiently, Schedlyzer basically needs just one assumption that there is no chaotic level of uncontrollable variation in the system due to assignable causes. Unlike lean manufacturing and TOC, it does not demand any system simplification for its functioning. It is very helpful in finding the best opportunities for improving overall production performance of a plant.

Other Approaches to Bottleneck Identification

Summary

For any given resource capacities, Schedlyzer generates optimal production schedule and predicts bottleneck formations and displays them on Gantt chart. To improve WIP, lead times, throughput and on-time delivery, capacity planning based on the prediction of bottleneck formations will be very useful. It helps set right focus on right resources at right times.

In summary, WIP, lead times, throughput and on-time delivery can be improved by predicting bottleneck formations, performing proactive capacity planning and setting right focus on right resources at right times. Schedlyzer is very helpful to high-mix, low-volume production units in this regard.

For more information about scheduling in HMLV systems, refer to our web page, Intelligent Scheduling For A Tough Class of High-Mix, Low-Volume Production Systems.