Design, Development and Implementation of a Constraint Based Production Planning and Scheduling System at a major auto component manufacturer

The company, is a leading manufacturer, exporter, supplier and service provider of Engine Valves in the country. Their manufacturing facilities are located at the Peenya Industrial Area in Bangalore and the Heerehalli Industrial Area in Tumkur (near Bangalore )
 
Problem Identification

The company was facing a difficulty in the scheduling of several jobs through a set of machines in large batches with different cycle times and sequence of operations. The main issues faced were:
Inability to visualize the constraint resource.

Lack of visibility of the throughput times and hence difficulty in committing delivery dates.

Inaccuracy in scheduling the start dates for each job.

Loss of capacity on the critical resource.
Higher than desirable WIP inventory.
 
Objectives

Based on a study done by Kaul Associates (KA), it was decided jointly with the company's management to develop a Production Planning and Scheduling application at their Bangalore plant with the objective to:
Reduce inventory levels:
 

Work in progress
 

Finished goods

Improve due date performance

Improve (constraint) resource utilization
Reduce throughput time
   

The PPC Application
KA developed a scheduling algorithm based on the Theory of Constraints, which enabled the application to visualize the flow of products through multiple machines and work centers and predict more accurately their throughput times and thus control both delivery dates as well as the work in progress. The PPC Application was developed indigenously with the help of the company's IT & Production team.
The PPC application is a highly flexible and customer focused system. It consists of 2 modules:
1 Rough-cut capacity Planning module : This module is based on a sales forecast and allows the Planner to:
Identify load on resources and ensure that there is adequate capacity to meet the plan
Take decisions to relieve the excess load (if possible) on any resource by:
 
Generating extra in-house capacity (overtime, alternate resource, extra operators, etc.)
 

Outsourcing extra capacity

Identify the constraint as the critical machine with the highest utilization , which is to be treated as the control point for all further Production Scheduling and Control activities.
Arrive at a feasible Master Production Schedule

Identify other critical resources that need to be monitored.
2
Shop Scheduling module : This module is based on actual customer orders and is used to:
Identify the feasible delivery date for an order.

Level the load on the constraint over the planning period.

Generate half-weekly schedules for loading of the constraint & other critical resources
Provide delivery schedules to vendors
   
Implementation:
A cross-functional team was then formed to enable the implementation of the new system. This was carried out in the following manner:
All necessary master data like Product Master, Machine Master, etc. were updated and verified in the system
A material accounting and feedback system was put in place to monitor the daily material issues to shop and production output.
Certain ground rules were established jointly with Marketing regarding the discipline of updating of new orders/ forecasted orders in the system, revision of delivery schedules and the cancellation of orders based on market demand.
The Planner/ Master Scheduler was authorised as the single person responsible for the running of the PPC system and trained in the use of the software.
Weekly schedules were provided to the shop floor for loading at the constraint, first operation and monitoring the flow of parts at other critical operations.

Shop personnel were educated on the working of the system and advised to follow the schedule.
Causes for non-conformance to schedule were then identified and attacked based on a Pareto and Root Cause analysis.
Benefits
One Planner was easily able to handle the entire task of production planning and scheduling.

Delivery commitments could be predicted more accurately resulting in improved delivery.
Bottleneck could be identified.
What-if analysis could be conducted to fully exploit the capacity of the constraint machine and maximize throughput

Scheduling of raw material issues could be done precisely resulting in reduced WIP.

Visibility of the schedule to the operating personnel and guidance in where extra capacity is to be utilised was improved.