Aerospace & Defense Mfg

    Validate Complex Manufacturing Processes and New Facility Design with Process Simulator PRO

    Challenges

    A diversified, global defense and information solutions company is successfully using Process Simulator Professional to model and simulate their complex, large scale manufacturing processes. This organization provides mission-critical, next generation solutions for the command, control, communications, computers, intelligence, surveillance and reconnaissance markets. It employs approximately 10,000 people and generated over $3 billion in revenue last fiscal year.

    When this organization bids a project in the areas of integrated electronic warfare, sensing and surveillance, air traffic management, information, cyber-security, or networked communications, it must leverage its years of experience, knowledge and technical expertise. Its markets demand a lot and the risks in meeting these demands must be all but eliminated. Bidding such projects requires proof and validation.

    Some of its clients include:

    US Army US Navy Homeland Security NASA
    US Airforce Marine Corp. FAA

    Objectives

    A tool that helps them answer a variety of predictive and prescriptive questions for complex projects:
    What resources will they need, how many, and by when?
    In their high-mix production processes, where are the bottlenecks?
    Can their manufacturing process meet the demands of the new program without jeopardizing current contracts?
    How long will it take to ramp up to maximum utilization?
    How do they layout a new manufacturing line or facility?

    Value Provided

    A reusable modeling and simulation tool that allows them to measure and validate manufacturing processes data prior to actual manufacturing. Process Simulator Professional is a reliable and easy to use Visio plug-in. This analysis tool allows this customer to build models of complicated interdependent manufacturing processes and new facility designs. These models are used to simulate the process before contracts are signed and projects begin. They provide the proof and validation needed to determine if and how to meet the stringent requirements of this customer’s contract obligations.

    Solution

    When a new contract is bid, this organization must know how to meet the production requirements of the contract and what it will cost. For one project they may need to model 90 different manufacturing processes or parts. Some of these processes may require new and expensive equipment. Process Simulator helps them determine what equipment is needed, how many pieces of that equipment are necessary and other data needed to meet the designated delivery dates.

    A manufacturing process might include the following steps, or any combination thereof for each of 90 different parts:

    1. Material Cutting
    2. Layup
    3. Curing
    4. Machining
    5. Inspection
    6. Painting
    7. Assembly

    Each complex process must be analyzed as a dynamic system because the production ramp-up and learning curve (cycle time) for each process is so critical to their business success. Accounting for the learning curve is essential to their profitability. Often at the beginning of production, a contract sells product below cost and over time moves from a loss to a profit. This organization must know when this will occur, how many resources will be required to meet delivery dates, and what training will be required and for how long.

    Process Simulator Professional is a modeling and simulation tool that allows them to easily build many unique process models and later connect these models to one another. It can also be used to help determine the footprint and space requirements needed to house large scale equipment and the correct and efficient placement of manufacturing tools and other resources.

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    Government and Department of Defense

    Army Depot Capacity and Labor Resource Analysis

    Capacity Planning

    Situation

    One of the critical functions for which the repair depot is responsible is the reset (refurbishing) of the water purification unit (WPU). They conduct the process from "site arrival through reset complete".

    These water purification units are used to convert polluted river or lake water to potable water for troops in the field.
    The facility can currently reset 73 units per year.

    The repair depot was anticipating increased demand due to the continued high level of troop deployment throughout the world, and needed to know what their maximum capacity was.

    Objective

    • Identify the actual maximum capacity of items the Paint Shop can reset given the current state of equipment and resources.

    • If the current throughput did not meet the demand of 200 units per month, identify the primary and secondary constraints.

    Results

    The current state model helped validate that the model is reflective of the actual process and determined that the maximum throughput under with current staffing is 75 units per year.

    They model also helped determine that the functional test equipment is the primary constraint limiting them to 75 units. If they relieved that constraint the next constraint would be the mechanics.

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    Healthcare

    Capacity Planning of an Outpatient Clinic

    Situation

    An outpatient clinic at an academic medical center offers comprehensive evaluations/consultations to patients undergoing an anesthesia-related, low to medium risk, planned surgery/procedure. Implementation of surgical process improvement initiatives across various surgical specialties led to an increase in demand for the clinic services. The administration believed insufficient consultation rooms would hinder their ability to expand services.

    Objective

    Determine the capacity requirements, both facility and personnel, needed to support the expected growth in patient volumes.

    Results

    The simulation model results indicated that there were a sufficient number of rooms at the outpatient clinic to meet the increase in demand. However, imbalance in patient scheduling across the day was causing a bottleneck in the system. By redistributing the workload more evenly across the day, the patient throughput in the clinic could be increased by 30 additional appointments.

    Implementation of the recommended patient appointment schedules and associated change in staff work schedules could accommodate the increase in demand in the existing facility with minimal addition of staff. These results were reviewed and approved for implementation by a multi-disciplinary team comprised of the clinical staff
    - providers, respiratory therapists, and administrators.

    Healthcare - Capacity Planning

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    Manufacturing

    Steel Manufacturer Simulation Reveals True Constraint of Truck Weighing Process

    Challenges

    A 100 year old global recycler, manufacturer, fabricator, and distributor of steel and related metal products, has corporate headquarters in the USA. One of their growing facilities in the south, was uncertain that current truck weighing processes could easily handle future volume projections. They were pretty sure the infrastructure and/or business practices needed to change to meet growing demand.

    They asked ProModel Corporation to evaluate flow constraints associated with the current truck weigh-in and weigh-out infrastructure and procedures. This would help them better analyze the proposed addition of a new entry weigh-in location.

    Objectives

    A simulation model that helps them answer a variety of predictive questions around their truck weighing processes:

    • Identify the congestion/constraints of both the current-state and future-state processes
    • Verify that the proposed capital plan will adequately address key constraints and/or improve the process
    • Identify the true capacity of the current-state truck processing system

    Solution

    ProModel consultant, Dave Tucker, worked with them to construct an initial current state truck flow model.
    Scale report data from October 2015 was examined and company subject matter experts (SMEs) were interviewed to fill in data gaps. The model was populated with typical activity times and capacity constraints for scales, scrap unload, slag pick-up, finished goods loading and tarping areas. A working model utilizing all of these inputs was tested and refined during validation and verification efforts. Next, some future state scenarios with an additional inbound scale (also changed the use of current inbound/outbound scales) were developed with additional scenarios experimenting with increases to finished goods trucks.

    Some general assumptions about truck flow were made:

    • Future truck flow volumes will be equal to or greater than those of the past
    • Loading and unload points are “black boxed” using associated capacities and processing times.
    • Personnel are available as needed in process Locations
    • 90% of all finished goods trucks need tarping; however, no pre-load trucks are tarped
    • Truck lengths are approximately 60’
    • Truck speed limit on site is 10 mph
    • The plant is open 24 hour / 7 days a week.

    There are five types of truck loads:

    Value Provided

    A reusable modeling and simulation tool that allows them to measure and validate current weighing processes as well as test future scenarios. For example, 11 scenarios were tested and run for 30 unique one month replications and compared to the baseline.

    • Use new scales
    • Baseline with finished goods trucks increased by ~20%
    • New scales with finished goods trucks increased by ~20%
    • Baseline with finished goods trucks increased by ~40%
    • New scales with finished goods trucks increased by ~40%
    • Baseline with finished goods trucks increased by ~60%
    • New scales with finished goods trucks increased by ~60%
    • Baseline with finished goods trucks increased by ~80%
    • New scales with finished good trucks increased by ~80%
    • Baseline with finished good trucks increased by ~100%
    • New scales with finished good trucks increased by ~100%

    The model determined that adding another inbound scale would not reduce the typical total truck flow cycle time significantly. So the organization delayed this capital improvement indefinitely. Furthermore, the model also showed that the bottleneck area was really in finished goods loading. ProModel is now working on a 2nd project to look for ways to improve that process area and reduce truck flow cycle time more significantly.


    Capacity Increase and Capital Avoidance

    Situation

    With a rapidly escalating demand for green energy, our client was already in a customer-delivery backlog situation and was expecting things to get worse due to their capacity limitations. They were planning on building several new manufacturing facilities to meet this increasing demand for their products. They also knew that they were not maximizing the throughput capability of their existing facility, but were not sure which of several very costly process improvements would yield the best result in the shortest amount of time.

    In order to improve their current delivery dilemma and also minimize the number of new plants, they needed to optimize the throughput capability of the current facility. The management team realized that with the severe levels of risk involved both to short and long-term customer delivery, as well as the millions of dollars at stake based on which choices they made, that simulation analysis would be the best method for determining the fastest and most effective course(s) of action.

    Objectives

    The client's primary business objectives for this initiative were as follows:

    • Increase throughput in a single existing facility

    • Understand what improvements from this project could be replicated across other existing plants to minimize future capital investment in building new plants

    • Improve their ability to meet increasing demand now and in the future

    Manufacturing

    Results

    The client team working in conjunction with the ProModel team developed the appropriate simulation model to help determine that adding uniquely sized buffers, in critical areas of a line would result in an immediate throughput increase. However, with each buffer costing hundreds of thousands of dollars, every single piece of equipment was a critical decision. It was determined that a scenario involving multiple, uniquely sized buffers was the most cost effective and fastest change they could make to improve the throughput of the plant.

    This improvement will be replicated throughout the other similar lines in this plant and is expected to reduce cycle time between 10 to 25 percent. After incorporating the recommended changes to this facility with the resulting increase in capacity, the client will be able to more confidently improve other existing facilities and also determine how many additional facilities need to be built, and when, in order to meet the increased demand projections while balancing it with sound capital investment timing decisions. If implemented, ROI to the client from this project will be in the thousands of percent, and savings will be in the millions of dollars.

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    Pharmaceutical

    New Vaccine Production Process Capacity and Resource Utilization Optimization

    Situation

    A large pharmaceutical organization with which ProModel has a long standing and successful relationship, was developing a drug purification and manufacturing process for a crucial new vaccine. The organization was ramping up for multimillion dollar per month production levels of the vaccine which needed to be manufactured in at least four different ways to fight various strains of a similar disease. Many of the pieces of equipment that would be required to meet the production levels were very expensive. Therefore, it was important for them to determine if they could meet the predicted capacity with their current resources. They also wanted a method for optimizing the process as production requirements increased over time.

    Pharmaceutical

    According to global planning groups, who provided operations and estimated annual dosage volume, they had produced 12 lots during startup and 62 lots during the first year. They needed to increase the number of lots per year to 110. For this type of product this is a huge jump in production. Manufacturing Engineering had an urgent need to determine how best to meet this goal. Because of the complexity of the process, the number of expensive resources required and the huge rise in production requirements, they knew simulation was the only possible way to accurately predict whether they could meet this quota with current resources and if they could not, what resources would need to be added, at what cost.

    Objectives

    • Determine if they could successfully meet manufacturing quota of 110 lots per year of crucial vaccine with current resources

    • Identify what capacity limitations are for the current resources and accurately analyze future capacity and resource requirements

    Results

    Using the Process Simulator model the customer was able to determine that they could successfully make their 110 lots per year quota without purchasing any additional resources. They also identified exactly what the capacity limitations were with their current resources and what resources they needed to purchase to exceed that capacity. They developed a prioritized plan for making the changes they required to optimize the production process, including automation of certain production line steps, and an added buffer tank which is an expensive change.

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    Services

    Retail Bank Branch - Staffing Capacity Cost and Customer Service Analysis

    Situation

    Customer service requirements at the client's typical retail bank branches vary over the course of a day and week for its products and services. For example, mornings might be 'teller services heavy' whereas after 2 PM might be 'banker services heavy'.

    A representative branch had the following characteristics:

    • High variability in transaction types (deposit, withdrawal, ATM, loans, etc.)

    • High transaction volume varied by type and time of day

    • 8 teller windows, 6 customer service desks, 3 drive thru lanes, and 1 ATM

    It appeared that branches were staffed using a "Just-In-Case" approach in order to maintain an acceptable service level. The current staffing method could not adequately address these fluctuations in customer demand other than by over scheduling its employees.

    Services

    Management saw an opportunity to increase profitability while maintaining or improving service by developing more efficient branch staff assignments. Could labor costs be reduced, without negatively affecting service, by doing a better job of matching skills to both 'what is needed' and 'when it is needed'? Applying this concept of "Skill-to-Demand" staffing would increase profitability and possibly increase service levels.

    The challenge was how to generate a staffing schedule according to this new approach? If they could predict which skills and in what quantity were required by time and day, costs and service would improve. With thousands of branches, the savings multiplied across their network of retail locations could be very significant.

    Objective

    The client's business objective for this initiative was to determine if changes to staffing policies at its retail banking branches could reduce labor cost and increase profitability while simultaneously improving customer satisfaction.

    Results

    The analysis showed how $120,000 of savings annually per branch came from matching the specific assignments of staff to "fuzzy" data about when a client would need them. Wait times at walk-up and drive-thru windows also decreased providing an improved customer experience.

    The project's key to success was the software's ability to replicate demand by when it occurred during the week. This enabled the branch managers to match staff schedules closely to "Customer Demand" as opposed to the more costly "Just-In-Case" approach.

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    Custom Solution / Custom Development

    Defense Manufacturer Needed to Minimize Late Orders Throughout the Supply Chain

    An armored vehicle component production facility supplies parts for other company sites where vehicle final assembly takes place. This component facility was experiencing a significant amount of late orders due to demand increases from several government programs over an 18 to 24 month period. In addition, moving bottlenecks were making it extremely difficult to plan production.

    Company executives needed to be able to give their customers reliable delivery dates and assure them that these dates would be met. In order to accomplish this, it was decided that managers at the component facility needed a more accurate short-term production planning tool. Decision makers wanted to better understand the true capacity of the component facility operations, as well as a way to test and evaluate new ideas for improving process throughput.

    Because of past successes with our COTS (Commercial Off The Shelf) simulation technology, they decided to work with us to develop a custom predictive analytic solution that would aid in their short term planning to improve on time delivery and become an effective tool for long-range production planning alternatives.

    After proving it out at this initial facility, the plan is to implement it at each facility on the supply chain.

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