What are the key factors that affect the accuracy of your manufacturing cost estimates?

Several key factors affect the accuracy of manufacturing cost estimates. These include fluctuations in material and labor costs, proper allocation of overhead costs, production volume, process efficiency, supplier costs, technology and equipment, regulatory compliance, market conditions, currency exchange rates, time constraints, waste and scrap, quality control, shipping and logistics, and environmental factors. Consideration of these factors and the use of appropriate estimation methods can improve the accuracy of cost estimates.

One creative strategy to help estimate your cost more accurately is to move labor from the variable to the fixed cost category, and evaluate your price based on what the *customer* is getting, not what *you* are paying for. Try it as an exercise first to see how the math works out. What you'll find is that if you implement standard pricing based on relative similarity from SKU to SKU, and treat labor as a fixed cost, you'll quickly see opportunities to increase net profit: - addressing other major fixed costs - categorizing SKUs and building value lines - leveraging capacity to increase volume of high-price SKUs Estimating and "cost-plus" are complex and difficult to be accurate. A simpler approach is to base everything on Net Profit.

I like Paul's recommendation however I would add a subtle but important change to it by splitting direct labor and management labor. I would consider the direct manufacturing labor a variable cost input and consider management labor a fixed cost. After doing that I would use the direct Labor Efficiency Ration (dLER) and management Labor Efficiency Ratio (mLER) popularized by Gregg Crabtree as managerial accounting KPIs/drivers.

Over the years I've see overhead allocated in various ways: flat rate, percentage based, and then some really ambitious (and hard to maintain) computational models that use a variety of inputs and methods like Time-Driven Activity Based Costing. My recommendation would be to keep it as simple as you possibly can to achieve your desired end state and then to have a regular cadence for updates to the rate. Stale rates can be sneaky killers of your margins.

At one point in my career, I served as the CFO for the DoD's Diagnostic Reference Laboratory. We tested millions of tissue and fluid specimens annually. We were assessing whether to consolidate 3 labs into 1 and in order to get the fidelity on operational costs at each for our evaluation we created a time-driven activity-based cost model. The model was extremely accurate (to within the penny!) but it took more than 6 months to build the model and collect the data. Not only did it allow us to see total costs, but also the cost of every step in the process which helped identify additional savings that we originally hadn't set out to find. The project ultimately led to a savings of over $10M/year so it was a worthwhile pursuit.

Best way to attack this in my opinion is just with a simple control chart with uCL and lCL. Find a reasonable range via StandardDev or a percentage and just track your data. After a quarter of measuring you'll have a great idea of where your variance lies and also how to improve your initial estimates to begin with!