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About the thumbnail: A simple sandwich making activity for time study was done by my group that captures the use of time study to capture standard time

OVERVIEW: My supervisor, a process engineer, at my OJT asked me how I can improve work around the production line. I proposed to him that I simulate the process for documentation purposes that may help them spot improvements in estimating yields. At the time, I was just shadowing his work and was left with nothing to do. He said he was amazed to see that I was knowledgeable about a simulation software that does that but he did not push me for to do that project since he was unfamiliar with it and its not really the norm. 😅😅

With that thought, below I present a case on that possibility and how this can be helpful for production lines in general to properly document time studies and focused improvement efforts. I showcase here the lecture and implementations that I have done in sample activities using Simio event-based scheduling simulation.

Before that, I would like to solidify this blog by divulging (big word) some key lessons that was presented to us and make my own commentary about the theoreticals and ideas floating around.

LECTURE (MFCIMSY):

The manufacturing world is full of innovations that target the essential automations and conveniences to make production easier. It is thus the job of the process engineer (i believe) to skim through these different possibilities and align it to the needs of their operations. Of course, nowawadays, it is a hot topic to go into computer integrated manufacturing (CIM) where the seamless integration of design, planning, manufacturing, quality control, and business systems is done through computers. The goal is productivity, flexibility, and speed in meeting customer demands.

TLDR: its driving the most profit, meeting customer expectations, with the least cost and reduced lead times.

Overview of CIM:

The building blocks for CIM and is present in today’s system are the following:

• CAD/CAM: Linking product design with manufacturing
• CNC & NC machines: Precision and repeatability
• Robotics & Automated Handling: Reducing human intervention
• Flexible Manufacturing Systems (FMS): Adaptability to product changes
• Quality Management & JIT: Lean, efficient systems

A key note that is always mentioned during discussions is these technologies are meant to amplify the existing system and not replace people or work. There is a bad rep for introducing these type of systems into the field since automation might lose jobs but on the contrary it just means that the workforce should be empowered given the new set of tools to attack bottlenecks and simplifying processes.

With this, it is clear that for the implementation of CIM there should be a clear picture of the following critical steps!

• Focus on the real goals (not just buying tech)
• Simplify existing processes first
• Standardize best practices
• Integrate systems before automating
• Automate incrementally — never all at once

To supplement these learnings, the following calculations must be considered by the engineer to justify the investment that will be allocated for these type of improvements.

Key Formulas in Computer Integrated Manufacturing (CIM)

Computer Integrated Manufacturing (CIM) is more than just automation — it’s the integration of design, planning, production, and business systems. To truly understand how CIM is evaluated, designed, and implemented, we need to look at the formulas and calculations that drive decision-making in manufacturing.

This was overlooked in discussion since we had to reachout to a company and make a CIM proposal but making cost calculations and quantitative analysis is the basis for the work that mfg. engr. in the plant. I have here below the following equations and formulas covered in discussion.

Financial & Investment Evaluation

The economics of automation and new technology are just as important as the engineering. Here are the standard formulas for engineering economgy as a starter for checking if the company/organization can handle its budgetary requirements for process improvement:

• Future Value (compound):

\[F = P(1+i)^n\]

• Single Payment Compound Amount Factor (SPCAF):

\[\text{SPCAF} = (1+i)^n = \frac{F}{P}\]

• Single Payment Present Worth Factor (SPPWF):

\[\text{SPPWF} = \frac{1}{(1+i)^n} = \frac{P}{F}\]

• Capital Recovery Factor (CRF):

\[\text{CRF} = \frac{i(1+i)^n}{(1+i)^n - 1} = \left(\frac{A}{P}, i, n\right)\]

• Uniform Series Present Worth Factor (P/A):

\[\frac{P}{A} = \frac{1 - (1+i)^{-n}}{i}\]

• Sinking Fund Factor (A/F):

\[\text{SFF} = \frac{i}{(1+i)^n - 1}\]

• Uniform Series Compound Amount Factor (F/A):

\[\text{USCAF} = \frac{(1+i)^n - 1}{i}\]

• Payback Period (equal annual cash flow):

\[n = \frac{IC}{\text{NACF}}\]

• Present Worth Method:

\[PW = -IC + \text{NACF} \cdot (P/A, i, n)\]

• Uniform Annual Cost Method (UAC):

\[UAC = -IC \cdot CRF + \text{NACF}\]

Cost & Break-Even in Manufacturing

When deciding between manual and automated processes, break-even analysis is essential. I have here the basic formulas for it and other basic analysis.

• Total cost function:

\[C(Q) = C_f + c_v \cdot Q\]

Where:

• $C_f$ = fixed cost

• $c_v$ = variable cost per unit

• $Q$ = quantity produced

• Total revenue:

\[R(Q) = p \cdot Q\]

Where $p$ = unit selling price.

• Break-even condition:

\[R(Q) = C(Q)\]

Solve for $Q$ to find the break-even output.

Mold & Tooling Cost Models

One of the featured items in the industry are molds for plastic injections and fixtures. It is salient that we cover the needed empirical cost and time it takes to produce such items before we determine a decision to pursue it actual fabrication. Below are some of the formulas that cover these considerations.

• Mold base cost:

\[C_b = 1000 + 0.45 \, A_c \, h_p^{0.4}\]

Where $A_c$ = cavity plate area, $h_p$ = combined plate thickness.

• Ejector pin estimation:

\[N_e = A_p^{0.5}\]

• Ejector system manufacturing hours:

\[M_e = 2.5 \cdot A_p^{0.5}\]

• Geometrical complexity indices:

\[X_i = 0.01N_{sp} + 0.04N_{hd}\] \[X_o = 0.01N_{sp} + 0.04N_{hd}\]

• Part manufacturing hours (simple geometry):

\[M_{po} = 5 + 0.085 \cdot A_p\]

• Additional hours for non-flat parting surfaces:

\[M_s = f_p \cdot A_p^{1/2}\]

Mold Resetting & Cycle Time

Another factor to consider is how cycle time influence productivity directly. Approximations are used in clamp and reset time calculations which is basically the time it takes for the mold to be reused again.

• Mold closing time:

\[t_{close} \approx 0.5 \, t_d \cdot \frac{2D+5}{L_s^{1/2}}\]

• Mold opening time:

\[t_{open} = 1 + 2.5 \cdot t_{close}\]

• Resetting time:

\[t_r = 1 + 1.75 \, t_d \cdot \frac{2D+5}{L_s^{1/2}}\]

Where $t_d$ = dry cycle time, $D$ = stroke adjustment, $L_s$ = max stroke.

Work Center Costing

What is often overlooked is how repetitive work can be given to machine instead of manual labor. I have here the following the labor and machine costs formulas that one process engineers should be familiar with to justifying savings in labor and automation.

• Labor cost:

\[C_L = w \cdot (1 + OH_L)\]

• Machine cost (annualized):

\[C_M = \frac{UAC_{machine}}{\text{Annual machine hours}} \cdot (1 + OH_M)\]

• Total work center hourly rate:

\[C_{total} = C_L + C_M\]

Break-Even Example (Manual vs Automated)

I have here an example of the break-even analysis regarding the difference between doing manual and automated work given in discussion!!

Manual machine:

• Fixed cost ≈ $22,931
• Variable cost per unit = $0.65

\[C_{manual}(Q) = 22,931 + 0.65Q\]

Automated machine:

• Fixed cost ≈ $53,820
• Variable cost per unit = $0.104

\[C_{auto}(Q) = 53,820 + 0.104Q\]

Break-even comparison:

Solve for $Q$ where:

\[C_{manual}(Q) = C_{auto}(Q)\]

This yields approximately:

\[Q \approx 56,575 \; \text{units/year}\]

To get an overview more about the formulas and discussions above, the reader may check the consolidated document below of our CIM course :> (not for the faint of heart~)

IMPLEMENTATIONS (LBYMF4B):

One of the key notes for CIM trend is the use of simulations to test these potential investments in the field without disruption of real production. This is in line with the discussion above about FMS, material handling, flow lines, and automated factories where in the simulation helps answer “what if?” questions, evaluate investment options, and control strats.

Of course, we got to play with it around in the lab using the software Simio :> At the time, I did not appreciate this software since I did not fully grasp its power but now wishing that I utilized it more in my downtime (lol).

The Simio and its packages can simulate discrete-events or events that can be models in a input-output basis without processes continuously changing or tweaked. This involves definitive structure that can be summarized and related to CIM systems below:

• Entities — parts, pallets, jobs, AGV loads.
• Resources — machines, operators, AGVs (entities seize & release resources).
• Queues — waiting places for entities (FIFO, priority, finite-capacity).
• Events — arrival, start service, end service, machine breakdown, transfer complete.
• Attributes — per-entity data (part type, arrival time, due date).
• Global variables/statistics — counters and accumulators used to compute outputs (throughput, utilization, WIP).

Why would someone in the industry would want to simulate before deploying CIM systems?

It boils down to these four reasons:

• rectify complex interactions: parts, machines, buffers, AGVs, robots, and human operators interact in ways that are hard to analyze analytically.
• Risk reduction: experiment with layout, staffing, takt, or buffer sizes in a safe virtual environment.
• Decision support: quantify tradeoffs (inventory vs. throughput, automation cost vs. utilization).
• Integration testing: validate control logic before deploying to PLCs or shop-floor controllers.

With this in mind, below are some of the activities that we made to simulate discrete-event and find the variables within the Simio software. Ngl, the documentation on my end is subpar since what was needed for only for the activity to be workiing but I was able to do the following in each corresponding exercise.

-I was able to maintain and define the future event list within a given queue and execute logic between states via scheduling of new arrivals and exit paths/flow for departure of material within entitities.

• I was able to capture branching operations and obtain KPIs within the field like that of throughput time, cycle time, standard time from established averages of wait, resource utilizations and service factor.

Overall a simulation workflow should look like this:

• Define objective & scope — e.g., reduce WIP by 30% while keeping throughput constant.
• Collect data — interarrival/service distributions, breakdown/repair rates, transit times (MES/PLC logs or time studies).
• Select modeling level — high-level throughput vs. detailed part-by-part DES.
• Build model — implement entities, queues, resources, failure/repair, and material handling logic.
• Verify — code checks, single-entity traces.
• Validate — compare to historic KPIs; consult floor staff.
• Design experiments — baseline vs alternatives; choose replications and warm-up. Use CRN for fair comparisons.
• Run & analyze — collect CIs, test alternatives statistically if needed.
• Sensitivity & robustness — test demand spikes, breakdown frequency, or supply variability.

do check the consolidated document below that is the essence of simulating these kinds of events and how to implement this using Simio~!

Sandwich Making Activity Time Study:

Lastly, as a semi-culmination of the learnings for the lab, we were tasked to make a simple time study for a sandwich making. It so simple but the attention to detail and the processes matter. I have here our take on this below :>

REFLECTIONS: There is alot of potential to use this in the field but is not in demand in the local scene. I do admit that learning this is tempting but knowing the basics is enough this may be presented as a work/project to do in the job. Circling back to what happened to my interaction with the process engineer @ my OJT.

It was really antiquated that the time study being made and any other process improvements I offer as my project were being rejected due to the unfamiliarity of the engineer to the technology. They want to have me to a legacy project (meaning a project that they can use) but at the end was a roadblock on how I can implement it given that I had no supervision or tasks they are ready to provide for me to do.

That was when I realized that the burden of doing the proposal and validating it falls under me. That despite my fresh eyes, it is important to rather pace oneself and set expectations already from the get-go on any type of work that needs to be done. Doing CIM proposals and implementing it may be unfruitful if it is not partnered with equal communication of its importance!

Knowing this, It is really better to have a partner in these projects to validate the calculations or tech proposed but I was also alone on the feature so at the end I got to just shadow at best what is being done by the process and plant engineer!