In a perfect world, all manufacturing costs would be known with certainty and correctly allocated to individual products based on the activity drivers. However, cost data is often distorted in the real world, leading to misallocations that can significantly impact a company’s bottom line.
As a result, each product will appear to have a higher cost than it actually does, leading to sub-optimal decision-making by management.
For various reasons we will explore, systematic distortions often lead managers to underestimate the true product cost, leading to sub-optimal pricing decisions. Ultimately, these distortions can significantly impact a company’s profitability and competitiveness.
Allocation refers to the process of assigning costs to different areas of the business. Reducing manufacturing allocations allows businesses to pinpoint where money is being spent and where improvements can be made.
In any organization, allocation is distributing resources among different individuals or departments. Regarding manufacturing, allocation refers to allocating materials, labor, and overhead costs.
The allocation goal is to spread these costs evenly across all company products or services. Poor allocation can lead to unnecessary cost overruns and inefficiencies in the manufacturing process.
Unfortunately, misallocations are a well-known pain point for organizations. Even more frustrating is how very little has been discussed, written about, or changed in manufacturing organizations since the allocation problem first appeared.
While we know there is a consistent problem, no one seems to do much to fix it- This series aims to fix that.
The first article in this series explored how misallocating manufacturing costs lead to declining profitability and decreased relevant decision-making support.
While distorted data may seem like a toothless topic to many in manufacturing who believe it can be overcome with directional intuition and big-picture thinking, this is far from the truth.
When mental models fail to tie to actual data, allocations are used to assign or spread grouped costs and close unexplainable gaps. Inaccurate or limited data leads to cost “unknowns” spreading between products, projects, or departments.
Inadequate manufacturing data collection processes suffer from a variety of ailments:
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Lack of traceability/Genealogy from production start to finish.
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Lack of metering to measure quantities input and output by batch and production order.
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Manual reconciliation processes that “plug “differences between floor and system-reported data.
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Lack of support resources that can only focus on production activities.
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Lack of internet connectivity and or poor IT infrastructure.
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Unintegrated systems. In dairy production, for example, one system measures lbs. of milk consumed, and another measures lbs. of fat, protein, and solids. The two systems are not integrated and do not reconcile at the production order level.
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Lack of process knowledge and collaboration between finance and operations.
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The standard cost process requires intensive resources and attention, so zero-based and driver-based budgeting never gets done.
Many manufacturers suffer from some combination of the above factors.
The real problem with manufacturing cost allocation is that it often relies on estimates and assumptions. For example, a company might assume that its products take the same amount of time to manufacture, but these estimates often don’t stand up to a critical assessment.
The crux of the issue is that some products take longer to manufacture while consuming more resources than others, which should be considered when allocating costs. And yet, the real consumption rates are unknown, leading companies to allocate too much or too little cost to their products.
Let’s go through a few case studies in detail. Follow along in the two case studies below to understand how these problems develop and their consequences.
Table of Contents
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Scenario #1- Allocating to Reconcile Process Gaps
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Scenario #2- Allocating w/o Activity Drivers
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Scenario #3- Allocating Budgeted Overhead over Machine Hours
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Strategic Implications of Manufacturing Allocations
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Fixing the Allocation Issue
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Conclusion- How Distorted Cost Data Leads to Allocations
Scenario #1- Allocating to Reconcile Process Gaps- Manufacturing Cost Allocation
A Day in the Life of a Data Reconciler.
After each production day, the plant line supervisors turn in timesheets to report packaging labor to indicate how many people worked on each line they oversaw.
However, the data reconciler notices there’s routinely a 10-20% disconnect between these timesheets compared to the time clock.
She knows that this problem won’t be going away due to a lack of a reliable way to track labor in the facility’s seven manufacturing lines.
To make matters worse, the plant usually brings in a handful of temps who seldom stick around until the end of the day and must shadow full-time workers to learn on the job- None of the supervisors tracks temps in their timesheets.
The same scenario plays out with reports the data reconciler receives indicating material yields where cream cheese is packaged into finished goods containers. What winds up happening is that approximately 10% of the cheese between production and FG packaging goes “missing.”
Throughout production, small material losses add up in unseen ways. For example, small amounts of cheese remain in the kettle once the production hopper empties out.
As the cheese heads to the packaging lines, a portion also remains in the pipes, of which a portion could be collected as “white rinse” at the end of the day to be reused in production the following day.
Then, as the packaging lines slowly fire up, several cartfuls of under/overweight containers are filled and pushed into the cooler to be salvaged or trashed.
Toward the end of the production shift, some cheese remains in the kettles that workers scoop out, box up, and then place this salvaged material into the storage cooler without adequately recording the inventory movement in the system.
Due to the abovementioned factors, the data reconciler must account for and reconcile the differences, which she does by allocating the difference based on the total pounds packaged. For the labor scenario described above, the same allocation process occurs to close the gap.
Figure 1
In this scenario, multiple process disconnects cause a manual allocation to reconcile the cheese sent to be packed to the amount of cheese packed.
This effectively creates the exact yield for all lines, even though they vary significantly in line efficiency and effectiveness. These variances are substantial when discussing a product where material costs are 80% of COGs.
Worse, the “missing” 100 lbs. or 10% of missing cheese from the packaging lines will, at some point, be added back to the beginning of the manufacturing process without being transacted through the system. This will inevitably distort yields in this facility’s production’s starting and ending points.
The same goes for labor. Since there is a difference between the production supervisor’s estimates of labor per line and what the time clock says, the difference will be allocated. In both cases, any future root cause analysis to investigate and resolve improved manufacturing processes is dead on arrival as the end data is now inaccurate.
Scenario #2- Allocating w/o Activity Drivers- Manufacturing Cost Allocation
For the second case study, we will review how overhead is typically allocated, adapted from the HBR article “Measure Costs Right: Make the Right Decisions by Robin Cooper and Robert S. Kaplan.
In this example, consider two hypothetical plants which produce a simple product, pencils. Both factories have the same manufacturing floor capacity, processing equipment, and similarly skilled forces; however, one factory is focused on producing only one product while the second factory produces multiple variations.
Each year, Plant A produces 1,000,000 #2 yellow pencils.
Plant B also produces #2 yellow pencils, but only 590,000 per year. To fill the plant, keep the workforce busy, and absorb fixed costs, Plant B also produces a variety of similar products:
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300,000 blue pencils,
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100,000 red pencils,
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10,000 green pencils
Plant B’s aggregate annual output totals 1,000,000 units, just like Plant A’s. During the budgeting process, the operations team directs accounting to set identical rates for standard direct labor efficiency, machine hour efficiency, and direct material consumption (yield) rates for both plants.
Despite the similarities in product and total output, there are critical differences between the efficiency and profitability of each operation.
Plant B requires a more extensive production support staff to:
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schedule machines,
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perform setups,
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inspect items after setup,
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receive and inspect incoming materials and parts,
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move inventory,
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assemble and ship orders,
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expedite orders,
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rework defective items,
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design and implement engineering change orders,
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negotiate with vendors,
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schedule materials and parts receipts,
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and update and program the much larger computer-based information system.
However, these costs are not captured or assigned appropriately based on resource usage. Instead, most companies allocate factory support costs in a two-step process.
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They first collect the costs into categories corresponding to responsibility centers (production control, quality assurance, receiving) and assign them to operating departments.
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But the second step—tracing costs from the operating departments to specific products—is done simplistically.
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Many companies use direct labor or machine hours as an allocation base in highly automated environments. Some organizations with a declining role of direct labor will use two additional allocation bases.
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Materials-related expenses (costs to purchase, receive, inspect, and store materials) are allocated directly to products as a percentage markup over direct materials costs.
Due to the complexity, Plant B will be burdened with considerably higher idle time, overtime, inventory, rework, and scrap than Plant A.
Plant B’s extensive factory support resources and production inefficiencies will generate expectation-to-reality cost distortions. Even worse, Plant B likely set up sales agreements in anticipation of achieving a much lower cost than is possible for each line.
Suppose the standard output per unit of direct labor hours, machine hours, and materials quantities are the same for #2 pencils as for green pencils.
In that case, the two types of pencils will have identical forecasted overhead rates—even though green pencils, which are ordered, fabricated, packaged, and shipped in much lower volumes, consume far more overhead per unit.
However, once production begins, Plant B’s cost system would report production costs for the high-volume product (#2 pencils) that greatly exceed the costs for the same product built-in Plant A.
Plant B’s cost system would show that the #2 pencils, representing 59% of output, will have about 59% of the factory costs allocated to them. Similarly, green pencils, representing 1% of Plant B’s output, will have about 1% of the factory’s costs allocated to them.
However, the 1% of cost allocated to green pencils would be far short of what it actually costs to acquire the materials, produce, assess, and store them. The smaller runs increase costs through multiple activities that volume cannot absorb, so The #2 pencils and the rest would absorb these costs.
Figure 2
Figure 3
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Figure 3 shows the planned, actual, and difference in costs in a scenario where the costs of producing each type of pencil where the costs for each product could be directly traced. The actual costs for all 4 product variations were $658,790 vs. a budget of $590,000, resulting in a $68,790 unfavorable variance.
However, as we know in this scenario, we do not have visibility to trace costs to each product; all we know are the totals. So, we allocate the unfavourability across each product using the totals and the same allocation structure in place.
What results are fascinating for Plant B:
Yellow pencils: 590k units produced
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The variance per unit of actuals vs plan = ($.03/unit). However, the allocation scheme determines the loss is ($.07/unit), increasing ($.04/unit costs).
Blue pencils: 300k units produced
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The variance per unit of actuals vs. plan = ($.09/unit.) However, the allocation scheme determines the loss is ($.46/unit), increasing ($.37/unit costs).
Red pencils: 100k units produced
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The variance per unit of actuals vs plan = ($.20/unit). However, the allocation scheme determines the gain is $.13/unit, with a decrease in $.33/unit costs.
Green pencils: 10k units produced
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The variance per unit of actuals vs plan = ($.37/unit). However, the allocation scheme determines the loss is ($.07/unit), with a decrease in $.30/unit costs.
In this scenario, low-volume products that require the most resources are subsidized by high-volume and efficient products. Using this information for costing decisions for all products, but Red pencils especially, where the allocation flips the cost from a loss to a profit, would be disastrous.
Scenario #3- Allocating Budgeted Overhead over Machine Hours- Manufacturing Cost Allocation
In the third and final scenario, a budget is established for overhead costs that are classified as indirect and either fixed or variable. First, Salaries/Wages are set, and then Indirect Costs & Consumables are set as fixed budgets, whereas the costs reflect the previous year’s actual with an upward adjustment for inflation
Figure 5
The accounting team calculates the Indirect Fixed and Variable Overhead rates by dividing the total dollars by the number of machine hours for both the Production and Packaging activities.
Figure 6
Although Production and Packaging operations use very different machines with different energy consumption rates, this is typically not considered. Total machine hours for all machines at the facility is the denominator used.
Figure 7
Next, the costs are rolled up through the BOM to account for direct materials, labor costs, and overheads. The overhead hours per batch multiplied by the calculated OH rates are used for these calculations.
As you can see, in this exercise, the focus of getting to a per-unit cost and margin takes precedence over analyzing the fixed costs and establishing cause-and-effect driver-based relationships.
Savvy operations managers looking to purchase additional equipment or hire new staff while avoiding scrutiny and bureaucratic red tape will almost always prefer to place those costs in the Cost of Goods Manufactured rather than SG&A, where they are more visible.
The total costs could differ if a new budget was prepared to tie in the costs of using each machine and all the support staff for each production phase.
You will also notice the $1.3M in overhead costs only adds $.14/unit to the total product cost, which will attract very little scrutiny, despite the organization having the most opportunity to reduce costs here. Generally, the market dictates commodity and labor prices, and an organization has limited options.
Strategic Implications of Manufacturing Allocations- Manufacturing Cost Allocation
The strategic implications of each of the three allocation scenarios should have your mind racing. Yes, your organization very likely uses one or more of the same allocation methodologies in your products. The product profitability information you’ve been relying on is near useless.
In scenario 1, poorly designed processes created fake data to reconcile yield loss and labor hours.
In scenario 2, allocations over multiple production lines with very different cost structures led to the most profitable items appearing less profitable and money-losing products appearing profitable.
In scenario 3, overhead allocations over a large production base distorted the importance of controlling overhead costs and instead focused on what an organization can do little to change.
Obviously, there are serious Strategic implications when products are costed by misallocating manufacturing costs. The most obvious is the effect on the overall cost of the product.
If manufacturing costs are not allocated properly, the product will be too expensive or not profitable. In addition, the allocation of manufacturing costs can impact the perception of the product and the role it will play in the long-term strategic plan.
In addition, the allocation of manufacturing costs can also affect the competitive landscape. If one company allocates a significant portion of its manufacturing costs to a specific product, it may have a competitive disadvantage over other companies that do not allocate as much. As such, manufacturing costs are a crucial strategic consideration for every manufacturing organization.
Organizations using cost information that includes substantial allocations will face frustration as total costs rise and profitability goals remain elusive.
Once executives are armed with more reliable cost information, they can ponder a range of strategic options. Dropping unprofitable products is one. So is raising prices, perhaps drastically, as many low-volume products have low-price elasticities.
The allocation of costs hasn’t always been a strategic decision for manufacturing firms, but it absolutely should be. The traditional allocation methods often distort to favor one product line over another by artificially lowering or raising product lines’ costs and making them appear more or less profitable than assumed.
As a result, firms continuing to make decisions based on these distorted cost allocations will lead to sub-optimal results.
In recent years, there has been a shift toward more accurate cost allocation methods, such as activity-based costing. Knowing and leveraging the driver-based and economically sound cause-and-effect relationships into product costing is especially important in today’s environment, where firms are operating in highly competitive markets and need to make decisions based on accurate information.
Fixing the Allocation Issue- Manufacturing Cost Allocation
Now that we understand how product costs are distorted through allocations, let’s cover some of the leading causes of cost allocations. If we can understand and recognize these issues as they occur in our organizations, we can act to fix the problem once and for all.
Poor Processes lead to manufacturing allocations
One of the most significant drivers of cost allocations is a lack of good data caused by poor processes. Inaccurate, missing, or outdated information can cause decision-makers to misjudge the actual cost of production. Without good, clean data, those who set the allocations use their best guess to make critical decisions alone- never a good thing to have.
The challenge is that most process data come from noisy, dirty, and ill-structured environments. Systems meant to improve data collection are often misconfigured, incomplete, or contain process breakdowns that require users to work outside and around the systems.
Instead of improving data and reducing work, they often create more headaches. Too many systems are little changed from their original implementation and initial designs.
Whatever the cause, a flawed process can be identified by its failure to properly track and record information in a structured way others can access and understand. Pair that with the continued struggles manufacturing faces from an aging workforce with quasi-tribal knowledge and difficulty finding new workers with the right technical chops- and you have one heck of a problem.
Properly allocating overhead costs can become difficult if material costs are inaccurately tracked. As a result, businesses that fail to invest in efficient data management systems often find themselves at a competitive disadvantage.
To avoid these problems, businesses must take the time to understand the allocation process and ensure that they have an accurate and up-to-date system in place.
One of the best methods to reduce allocations is to empower teams to collaborate using software to map out the steps of each manufacturing process, recommend improvements, and then act on them. Process mapping identifies where allocations occur, and production bottlenecks occur.
Once the processes are mapped and understood, businesses can improve their existing software or bring in new systems to increase manufacturing efficiency, decrease costs, and produce robust data sets to identify reduction opportunities.
Organizations should also perform engineering studies on each line and improve their manufacturing processes to understand actual resource consumption rates and share this with stakeholders who require the information. Creating and improving processes aims to understand and use the cost drivers that reflect the laws of nature.
Lack of metering leads to manufacturing allocations
Closely following poor processes, a lack of metering in manufacturing plants is a consistent pain point that protects cost allocations.
Inefficient data management can quickly eat into profits for any manufacturing business. When data is inaccurate or incomplete, it leads to costly mistakes in the production process. For example, if the wrong materials are used or the incorrect quantities are ordered, production delays and extra expenses can be delayed. In addition, poor data can also complicate cost allocations.
Utility rates are typically high costs in manufacturing, and very few organizations truly understand the usage breakdown between keeping the lights on at the facility versus how much energy is consumed in each process batch.
As demonstrated in Scenarios #1, #2 & #3, when multiple lines run simultaneously in a facility, it can be challenging (or near impossible) to track the consumed materials, output, and labor that supports each line.
It is critical to understand what is consumed in any manufacturing process accurately. This information is typically gathered through metering, which measures the quantity of material used. Without metering, it is challenging to track material usage accurately, leading to inefficient allocation of resources.
Additionally, it is easy to form inaccurate assumptions about the manufacturing process without metering. These mental models can lead to suboptimal decision-making and lower-quality products. Therefore, metering is essential to any manufacturing process, and its absence can lead to serious problems.
With today’s technology and the rise of the Internet of Things, metering a production facility has become much less expensive and more critical to ensuring a business model is based on facts.
The accelerated deployment of low-cost sensors and their connection to the internet has created a lot of hype about the future of manufacturing. The Internet of Things (IoT) and its application of big data and analytics have led to the creation of the next generation of manufacturing.
Big data can help improve manufacturing cost information accuracy and efficiency. By collecting and analyzing large data sets, businesses can identify patterns and trends that can help to optimize the manufacturing process and reduce costs. As a result, big data can improve decision-making in manufacturing and help businesses reduce costs and increase profits.
Big Data techniques and technology can connect cost and operational manufacturing information to improve internal decisions. Big Data is not a solution without addressing three key points:
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First, the organization must understand what information is needed for internal decisions;
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Second, the data must be analyzed correctly; and
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Third, appropriate cost and operational data must be collected. In short, a design is needed before data is collected.
While it is often assumed that the correct information will be readily available for cost data, this assumption is incorrect. The current cost information focus is generally accepted accounting principles and financial reporting.
Collecting, analyzing, and using cost data to support manufacturing operations effectively requires a different paradigm focused on internal management decision-making, not external financial reporting.
Important decisions that impact the manufacturing process should always be based on facts, not guesses, wishes, theories, or opinions. Today’s emerging technology helps by enabling both people and the equipment to collect and process the facts they need to achieve better results.
Attitudes of Old School Plant Managers lead to cost allocations
One of the most critical roles in reducing manufacturing cost allocations is the plant manager. These individuals understand the production facility’s daily ins and outs and must make split-second decisions that could mean life or death for a product.
The best plant managers “get it.” They understand the processes, the people, and the systems, and they know how to bring all three pieces together in the modern age.
Great plant managers support zero-based budgets, understand OEE (Overall Equipment Efficiency), and support and lead cross-function root cause analysis investigations to identify and resolve issues for good. They can back up their opinions with facts, data, and experience.
On the contrary, there is the old-school plant manager. Unfortunately, many plant and operations managers come from an old school of thought where they believe that allocation is not their responsibility. A lack of detailed insights, a lack of appreciation for technology, and a my-way-or-the-highway approach lead to all sorts of issues.
These managers are less likely to invest in new technologies or processes that could improve efficiency and reduce costs. As a result, old-school attitudes toward cost allocation can cost companies both wasted resources and missed opportunities for improvement.
As such, old-school managers are more than ok with allocating the cost of production across all units produced, regardless of whether those units are sold or not.
In addition, old-school plant managers contribute more to waste and overproduction, as production managers try to produce as many units as possible to keep costs down, leading to higher inventory levels and overall costs.
This leads to poor decision-making and, ultimately, to unthought-out allocations that have disastrous consequences for a manufacturing company. To avoid these allocation problems, plant and operations managers must have a positive attitude toward determining the true production costs and take their responsibility seriously.
Plant managers are in a unique position to help accomplish this goal. They directly control many plant expenses, such as labor costs, materials, and utilities. Plant managers can develop strategies to reduce these costs by working with other departments within the organization without compromising quality or productivity.
In addition, they can motivate team members to be more efficient and effective in their work. When plant managers successfully reduce cost allocations, it benefits the organization as a whole and can lead to increased profits. As a result, organizations should incentivize plant managers to reduce cost allocations successfully.
Conclusion- How Distorted Cost Data Leads to Allocations
Cost allocations have kept management accounting professionals and executives awake at night for far too long. Not knowing whether your work provides real value to the organization or the decision you will bet the company on is based on facts is a terrifying proposition.
The good news is that we can set ourselves up for much-needed change by understanding allocations’ causes and common uses. By focusing on our facilities and investing not just in new machinery but in great processes, metering, and the right talent to run the factories, we can go a long way in genuinely understanding production costs.
How Distorted Cost Data Leads to Manufacturing Cost Allocations- Recommended Reading
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Manufacturing Excellence by Uncomplicating Your Organization
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The 7 Principles of Manufacturing Excellence & Cost Management
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Manufacturing Excellence & Complexity- 15 Troublesome Symptoms
Updated: 4/16/2023
