PMC 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, PMC’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
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Outdated databases and standards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
PMC’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, PMC’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
PMC’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 large discount retailer was preparing to incorporate a demand-driven scheduling system. A key parameter required for this system was accurate workload content by task for each individual department. This is a classic Industrial Engineering function and the retailer employed PMC to propose a methodology and to execute the study. While collecting this data, it was important to use Lean principles to identify opportunities to reduce waste and suggest process improvements.
Six stores across two states were studied. Within each store, three departments were studied. The departments studied were Lawn and Garden, Stationery, and Toys. The toy department consisted of the retail floor as well as an assembly area for bicycles. There was one common set of tasks which was applicable to all retail departments and a separate list of tasks for the assembly area.
The demand-driven scheduling system is highly desirable for the retail industry because it is crucial to provide customers with the desired service level, while avoiding overstaffing. Lean principles are currently finding their way into industries outside of manufacturing and the retail industry is no different. By identifying waste within a store, processes can be streamlined and process times can be minimized; thus improving the customer’s shopping experience and minimizing the associated costs to the retailer.
PMC utilized random sampling to measure the workload within each store. The study encompassed one business cycle across six different stores. A business cycle was defined as a seven day period, all hours of operation, as well as the opening and closing activities of associates.
Random sampling data was used to develop standard times for tasks. The number of items sold was used as the workload driver for each department. This data was used to develop demand-driven schedules. Several additional analyses were performed using this data including:
Analyzing the impact of government regulations for applying price tags to items versus shelves
Investigating the results of scheduling department managers during peak hours
Comparing task proportions between department managers and retail associates
The work content developed was compared between Old and New store structure
PMC provided the data required to support a demand-driven scheduling system based on workload and performed several detailed analyses on this data. This data can be used to ensure the appropriate service level is achieved, without overstaffing. Several process improvements were suggested based on lean principles which will enable the retailer to improve productivity of staff and to improve the customer’s shopping experience.
A major optical retailer was concerned with their scheduling method, based primarily on sales dollars. They were interested in developing a more accurate, nimble, and demand-driven scheduling technique because the current method was not proven to be the most accurate. The first step toward this strategic goal was to perform a time and motion study to measure the workload. Another focus was to do some detailed analysis on the flow of traffic into the stores and to determine the amount of hours required to operate a store based on the workload.
A store was composed of three departments: retail floor, laboratory, and doctor’s office. Regardless of the department, resources were allocated by sales dollars. This methodology may be sufficient for departments that are very sales-dependent; however the laboratory is not.
Each store is equipped with an automated traffic counter at the entrance. This device counts every time the beam is broken. This method makes it difficult to form conclusions based on this data because items such as party size, associates entering the store, customers crossing the beam several times, etc. are unaccounted for.
There were two concerns with regard to the time and motion portion of the study. First, were enough hours being allocated to provide the appropriate service levels? This concern was particularly acute with regard to the retail floor. One reason for this concern was the fact that several services not generating any immediate sales were offered (i.e. adjustments, repairs, etc.). Therefore, required work may have had no hours allocated to it. The other, more strategic, concern was whether or not sales dollars was the appropriate workload driver for the three departments.
Store performance is typically measured by the sale conversion rate. A main input to the conversion rate is the traffic count. For this reason, a better understanding of how the traffic flow converts to sales was clearly of vital importance.
PMC utilized random sampling to measure the workload within each store. The study encompassed one business cycle across twelve different stores. A business cycle was defined as a seven day period, all hours of operation, plus the opening and closing activities of associates.
PMC collected traffic data semi-automatically. An analyst would manually count and categorize inbound traffic and record it in a database with a time stamp. This data can be separated by store, day of the week, time of day, etc. This data was instrumental in determining the “true” conversion rate since it eliminated noise associated with the automated traffic counter.
Random sampling data was used to determine how associates spend their time (see table on following page) and to develop standard times for tasks. The frequency of tasks was used to determine the work content for a store. This data is used to develop demand-driven schedules. The data also revealed that using sales dollars as the workload driver for laboratory activities was less accurate than using items produced because the work performed in the laboratory is not driven by sales dollars; it is driven by the number of items produced.
The traffic flow analysis was used to define and quantify all patterns of traffic flow potentially useful in the scheduling of associates. Many of the browsing/buying customers came on the weekends, implying these shifts should include more sales associates. The traffic flow was also used to determine the actual sales conversion rate based only on the number of browsing/buying customer arrivals. This information was vital for the stores managers to verify that they have the appropriate staffing levels at the appropriate days and times. This would minimize the staff’s idle time, which was significant in some stores, and improve customer service during periods of peak demand.
PMC provided the workload data required to support a demand-driven scheduling system and performed several detailed analyses on this data which determined the ideal hours required to operate a store. This data can be used to ensure the appropriate service level is achieved; without under or overstaffing. Also, using the traffic flow data, the right classification of associates are scheduled to perform the services required.
Several process improvement opportunities were identified and detailed solutions were presented. Some of these ideas came in the form of best practices from the elite stores while others were derived from the application of lean principles in order to reduce waste. The areas that benefited from process improvements were loss prevention, customer satisfaction, productivity, and sales and marketing.
A hospital administration was planning the construction of a new hospital. The building architects determined the structure of the hospital to encompass four departments – Emergency, Inpatient, Imaging and Surgery. To facilitate the most optimal utilization of these spaces, PMC was able to offer a simulation model so as to validate the capacity envisioned by the architects’ plans. The simulation used historical data to analyze arrival patterns and to evaluate the service time at every department, as well as provide a model of every area in all four departments.
The model encompassed interdepartmental movements of patients: outpatients entering the hospital, registration, triage, inpatient movements, the appropriate operating / treatment room and also the pre operation / preparation process and the post operation / recovery process. Along with patient movements and key performance metrics such as utilization of different areas, patient lead time in every department was improved.
• Required effective master plan
• Uncertain equipment requirements
• Potential bottlenecks needing identification
• Patient wait times needing improvement
Emergency patients would arrive into the system each weekday and then be classified as either emergency or non-emergency. All patients were routed to triage and patients requiring specialized treatment such as isolation or decontamination were sent to specialty treatment rooms; if these rooms were unavailable, any normal treatment room would be converted into a specialty room.
Patients requiring normal treatment would be sent directly to the unaltered treatment rooms, where they would be attended by a nurse and MD assessment. Incoming patient treatment rooms could serve patients requiring any of 29 different types of specialty treatment such as PCU, ICU, neurological care, labor care, and pediatrics. Patients could also be moved from one type of treatment room to another should the situation require.
Imaging was offered for arrivals of outpatients, inpatients and those from emergency. Of the 13 different imaging services available each of them operated in a specific schedule in a day. Imaging services would seal access to an inactive imager, and imaging patients would complete a preparation and recovery process before and after the procedure.
Surgery patients flowed through the pre-op unit, the operating room, and the post-op unit. RIO and PCU were the two units serving pre-op and post-op patients and patients would go through either of these units or both. The number of operating rooms available at different hours of the day was different on every weekday.
The goal was to determine the right number of beds required in Imaging, Surgery and Inpatient areas. Validating the existing capacity of surgery and estimating the number of working hours required was necessary to cater to the needs of the forecasted number of patients. As well, testing different floor plan adjacency options (in cases where preparation and recovery would happen at the same location) would enable the master plan for the hospital. Equipping for higher levels than necessary would increase capital outlay, lower resource utilization, and increase the medical center’s operating costs.
The overall objective was to determine the minimum number of inpatient, surgery and imaging treatment rooms to be equipped while reducing patient waiting time in all departments. This was determined satisfy projected patient demand for a year at the hospital.
Outcome measures reported by the model that would aid in the decision-making process included: daily patient throughput, patient lead time, value added time spent by the patient in the system, and utilization of every treatment area and waiting room.
The recommendations of the study included:
• Reallocate the number of inpatient treatment rooms to different types (ICU, PCU, pediatrics etc.) considering high volume types
• Reduce the number of operating room capacity in surgery up to 60% considering low utilization as a result of high number of patients whose surgery is cancelled after pre-op.
• Reduce capacity at the admission, waiting room and trauma room can to 1 each in the Emergency department considering the volume of patients.
Based on these recommendations and implemented changes, the facility was well equipped to conduct the forecasted 102,000 patient interactions per year.
A major producer of baby-food products desired additional information about their existing bottling system and recommendations to improve production efficiency. To meet the client’s goals, PMC first simulated the existing design and then modeled several different scenarios to optimize system throughput.
The bottling system consisted of the following: Glass Depalletizer, Optical Scanner, Accumulation Table, Filler, Capper, Coder, Tray Packer, Case Palletizer, Labelers and a system of conveyors.
A new bottling system’s design called for the linking of the best equipment and technology that the company had available. However, this linkage did not exist or might have been inefficient and being run over capacity.
The main objective of the study was to understand the behavior of existing bottling systems and to assist in designing new and efficient ones. This was achieved by:
• Identifying bottlenecks and determining the level of resources necessary to maintain production targets.
• Providing accurate, objective, quantitative information to refine the process and increase productivity.
• Developing a control strategy for the system by understanding its logical operation.
First, a base model operating under original specifications and parameters was developed for evaluation. Then, alternative scenarios and suggested system improvements were modeled and evaluated to determine the line configuration that would optimize system throughput.
The process simulation allowed engineers to test the system and identify inefficiencies. This study led to the most effective system configuration by quantifying the effect of changes to the system.
This simulation model was developed to improve the existing production facilities for one of the world’s leading producers of ready-to-eat cereals. Plant management had formulated some process modifications and wanted to objectively evaluate the effects of each one. By using a simulation model, plant engineers were able to see the effects of the aforementioned changes in their model before they were introduced into the actual plant system.
The packaging line consisted of the following operations:
• 4 baggers
• 1 cartoner
• 1 accumulator
• 2 case packers
• 2 case lifts
In the current system, product was fed into bagging machines where it was discharged into plastic bag-type containers. The bags were then transported to a cartoner that inserted the bags into individual cartons. Once boxed, the product was fed to an accumulator that grouped the cartons and fed them to case packers. The case packers inserted the group of individual cartons into a case for shipping. The filled case was sealed and removed from the system by a case lifter.
The main objective of the study was to understand the behavior of the existing packaging system and to assist in designing a new and more efficient one. First, a base model, operating under original specifications and parameters, was developed for evaluation. Then, alternative scenarios and suggested system modifications were modeled and evaluated to determine the optimal line configuration.
The simulation objective was to determine if overall throughput requirements could be met or improved. The next objective was to determine under what conditions, and the proper line configuration that would allow throughput goals to be achieved.
Therefore, the model needed to:
• Identify bottlenecks and determine the level of resources necessary to achieve production targets.
• Provide accurate, objective, quantitative information to improve the process and increase productivity.
• Help plant engineers gain insight into developing a control strategy for the system by understanding its logical operation.
Several scenarios in the packaging system were evaluated to determine a configuration that would optimize system throughput. One system modification suggested that the same production efficiency could be achieved by removing a bagging machine from the packaging lines. However, when only one case packer was used in the same system, throughput decreased. Current system difficulties could be resolved by modifying the system configuration, or by increasing the speed of the conveyors.