Poster
Client Context
Micromeritics Instrument Corporation is a leading provider of analytical instrumentation for particle and materials characterization. They are headquartered in Norcross, Georgia, where over 90% of instrument production takes place, but they also have manufacturing facilities in Spain and the United Kingdom. Micromeritics instruments are the instruments-of-choice in more than 10,000 laboratories of companies, governments, and academic institutions around the world. They make 64 unique types of instruments, amounting to an annual revenue of over $X million USD.
Project Objective
Micromeritics currently has a multi-month order backlog due to supply chain issues and staffing shortages from the COVID-19 pandemic. The company is trying to reduce their backlog of orders while simultaneously increasing their sales revenue by 20% over the previous fiscal year. To help achieve the client’s goals, the team identified four main opportunities in the current system:
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Alleviate inefficiencies in the stockroom to increase picking productivity and decrease overall picking time
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Improve assembler efficiency by organizing rolling carts more effectively
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Increase instrument batch sizes to reduce the number of stockroom visits each month
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Reduce wasted assembly time by more easily identifying missing parts
Design Strategy
The team created a Warehouse Slotting Algorithm to determine the new layout for parts in the stockroom, taking into consideration the demand and accessibility of parts in each location. The algorithm ensures that high-demand parts are placed on shelves at or below eye level (called “Golden Zones”) to improve accessibility and minimize picking time. The team also utilized the S-route heuristic to effectively route pickers through the stockroom.
As parts are picked in the stockroom, they are placed on rolling carts and then sent to the assembly area. Currently, parts are placed randomly on the shelves of the carts in any way they fit, which causes assemblers to have to sift through the entire cart to find the part they need. Assemblers can spend up to 50% of their time searching for parts on the rolling carts. To streamline the assembly process, the team created an algorithm for a modified bin-packing problem to organize parts on rolling carts by assigning each part into a specific cardboard bin on the shelves. By ordering parts numerically by part number, the team was able to help minimize the time it takes assemblers to search for specific parts during the assembly process.
To measure the impact on the stockroom, the team built a simulation model in Python using data collected from time studies to compute a theoretical picking time for one batch of each instrument type. After validating the simulation with a chi-squared goodness of fit test, the team was able to fail to reject the null hypothesis that simulated picking time is the same as actual picking time.
The team also provided recommendations on the optimal batch size for each instrument to maximize the number of rolling carts that fit in the assembly area to therefore reduce the total number picks for each instrument per month.
Deliverables
The team had two main deliverables: (1) Graphical User Interface (GUI) for the Warehouse Slotting Algorithm and (2) Excel output from Rolling Carts Organization Algorithm. The GUI for the stockroom takes in a complete list of stockroom locations and the bill of materials for each instrument and then outputs 3 documents for the client: new location assignment for each part in the stockroom, the optimal picking route to minimize stockroom traversal, and incremental instructions for the client to implement the solution over time. For the rolling carts, the algorithm considers the instrument type, the batch size, part sizes, and the part number to output an Excel file with a list of the bins needed and what parts go in each bin.
Value and Impact
Instrument production at Micromeritics has 5 main steps: Inventory replenishment, picking, assembly, testing, and shipping. On average picking and assembly make up 15% and 55% of total production time respectively for all instrument families. The project and recommendations reduced overall picking time by 20% and the average assembly time for an instrument by 11%.
The cost of the stockroom organization is the primary cost involved in reducing picking time. The complete reorganization is expected to take around 285 hours which equates to $10,000 worth of labor hours. Micromeritics will spend at least XXX hours on picking this year. Approximately 773 hours will be saved if the recommendations mentioned are implemented. The annual savings will be $Y based on the production plan for 2023.
The number of additional instruments multiplied by the value of the specific instrument summed up is used to approximate the value of this increase in production to be worth $2.6 million. The client believes that the cost of scaling up production by 11% will be approximately $Z M for the year and should be immediately affordable for the company. Therefore, the potential additional profits generated by the project will be approximately $N annually.
Project Information
Student Team
Nikita Jakkam, Roshen Jegajeevan, Mihir Kandarpa, Lokranjan Lakshmikanthan, Harish Murali, Devarsh Pandya, Sunny Patel, Hannah Tracy
Faculty Advisor
Faculty Evaluator
