PMI was retained by a major automotive OEM to perform analyses of material flow within an integrated stamping and sub-assembly plant. The OEM not only wanted recommendations on proposed bar-coding systems and reallocation of production personnel, they also required a reliable tool with which to evaluate future proposed changes to the system. Throughout the project, PMI’s team utilized a variety of industrial engineering techniques. Recommendations were offered, and a custom fit tool was created. Through use of these instruments, the client’s requirements were met.
- Inefficient material flow
- Excess labor
- Inefficiencies in storage areas and storage requirements
- Outdated databases and standards
- Insufficient reporting system for maintenance scheduling and bar code scanning
The plant studied was one of the largest in the automotive industry, containing 23 press lines and occupying 2.5 million square feet. Key system details included:
- Stamping lines’ output passed through the sub-assembly area before being shipped out of the plant
- Material flow was generally ‘linear’ – entrances and exits occurring at opposite sides of the plant
- Stored materials were housed in containers or racks
- Forklifts and dolly trains were the main form of material transport
The plant was suffering in several areas relating to inefficient material flow:
- More forklift operators than necessary
- Inadequate storage areas
- Ineffective bar code system
- Inadequate system for reporting equipment utilization and maintenance scheduling
PMI’s plan was to thoroughly study, analyze, and evaluate infrastructure requirements for better tracking and management of the material handling equipment fleet in the plant facility. This was achieved by utilizing several methods including: continuous and elemental time studies, static simulation modeling using Flow Path Calculator; and dynamic simulation modeling using Witness software.
Upon project completion, PMI’s team delivered:
- Headcount reallocations: The plans exceeded the initial goal of 22 operators reallocated
- Simulation models: The analytical tool allowed for quick analysis of material handling resources required by changing production conditions in the plant from both short-term and long-term changes to the production schedule
- Bar Code and ID System Analysis: Full alternative, decoupled solutions that could be pursued in sequence or in parallel
PMI’s solution offered tremendous savings to the automotive OEM:
- Headcount reductions resulted in an annual savings of $4.3 Million.
- Bar Code and ID systems recommendations totaled $1.3 Million in potential savings.
A simulation model was built to evaluate the performance of an automatic warehousing system under different values of design parameters and operational policies. The automatic warehousing system considered was composed of two main segments: the AS/RS and marshaling system. The AS/RS included the stacker cranes, storage racks (bins), input buffers, and output buffers. The marshaling system included the output dock, input dock, pick loop, and the input/output conveyor. The two systems interfaced at the input and output buffers of AS/RS. The performance of the warehousing system was observed under a large number of design parameters and operational policies including the number of aisles in AS/RS, the number of horizontal and vertical bins in each aisle, aisle assignment policies, bin assignment rules, and conveyor and stacker crane speeds.
The incoming parts are first loaded to (input) pallets and the contents of each input pallet are communicated to the central computer. The computer assigns an aisle number and a bin (rack) location to each input pallet. The input pallets queue at the input dock. Whenever an empty place is detected on the conveyor, the input pallet is transferred to the conveyor. When the input pallet reaches its designated aisle, the computer checks for an empty place in the input buffer of the aisle. If an empty place in detected, the input pallet is automatically transferred to the buffer. Otherwise, the input pallet makes one complete cycle and tries again to enter to its designated aisle. The automatic stacker crane of the aisle finally picks the pallet from the input buffer and stores it to its assigned bin. When an item is requested from the system, the computer selects the aisle and the rack location of the (output) pallet. The stacker crane picks the output pallet from its bin and transfers it to the output buffer. Whenever an empty place is found on the conveyor, the output pallet is moved to the conveyor and transported to the output dock. The output pallet may then enter a pick loop or it may be emptied and returned to the incoming stock location for reuse. The pallets that enter the pick loop are sent back to the AS/RS after their contents are altered.
The client, a major automotive manufacturer, wanted to verify the throughput of the system under different conditions. The performance of the warehousing system as demand (store and retrieve requests) doubled and tripled was to be observed. The number of aisles (stacker cranes), the capacity of each aisle, and the rules for selection of the store and retrieve requests for processing by the system had to be decided.
The overall objective was to design an automatic warehousing system that was efficient and flexible. The best values of a number of system variables and operational policies had to be decided which included:
- Number of aisles
- Vertical positions of input and output buffers of each aisle
- Capacity of input and output buffers
- Number of vertical and horizontal bins at each aisle
- Policy for storing different part types (based on part turnover frequency) at the aisles
- Policy for storing different part types (based on part bin size requirements) at the aisles
- Horizontal and vertical maximum/minimum stacker crane speeds
- Conveyor speed
- Store and retrieve request profiles for each part type
- Bin assignment rules for store and retrieve requests (closest – open location rule, first-stored, first retrieved rule, closest – full location rule, frequency class-based closest – open location rule)
- Position of input and output docks.
The results of the simulation determined that:
- Crane speeds can affect the throughput rate of the system by 30 percent.
- Closest – full location bin assignment rule increases throughput by 10 percent when compared to first-stored, first-retrieved bin assignment rule for the retrieve requests.
- A 3 – aisle system degrades the throughput of the system by about 10 percent when compared to a 4 – aisle system.
- The proposed 4 – aisle system can perform under acceptable conditions even if the demand for
the system increases by three hundred percent.
A multi-billion dollar automotive supplier was outgrowing its 1.6 million square foot warehouse. With time running out and customer demand increasing, they called upon PMI to analyze the material flow within their facility. More than 10,000 vehicle instrument panels are assembled and shipped daily from this location.
Both in-house plastic injection-molded and purchased parts are stored in designated sections throughout the plant. These raw materials are brought to assembly lines via fork trucks or vehicle trains. WIP parts are placed in temporary holding areas and finished goods are stored or shipped out by truck to automotive assembly plants.
High vehicle congestion areas were scattered throughout the facility. Once controlled part routings were becoming difficult to manage with increased complexity caused by ILVS (In Line Vehicle Sequencing) strategies recently deployed by automotive manufacturers. With over 1200 named parts to move (including color and style complexities), plant engineers realized something had to be done to improve the situation.
The goals of the study were to improve the pre and post-assembly material flows within the facility. By establishing a data-driven baseline scenario, alternatives could be tested for increased efficiencies regarding material flow to and from assembly lines. First steps were to collect, assemble, and format material flow data. Material handling labor costs and resource utilization were analyzed, as well as verifying a path for every part within the operation. Secondary efforts included static and dynamic modeling to further test options for operational modes
A project team was established to conduct a detailed study of material flows connected to warehouse areas. A phased approach began with a review and overhaul of existing data, complete with all current part numbers. Part routings were verified on the plant floor, and mapped in a material flow computer model. Specific routes for high volume parts were modified to increase overall material handling efficiencies.
Over 100,000 square feet of floor space was isolated for a central marketplace where often-handled parts were managed through a WMS. A well-maintained database tracked part numbers and routings to assist in inventory planning and management.