In most of our business turnaround efforts, we see a reoccurring theme as a major problem contributor – inaccurate product costing. In any turnaround project, one of our first areas of review is product and service margins as well as the legitimacy of the data in generating the cost information. On a consistent basis, the inaccuracy of product costing, and the poor decision-making based on this flawed data, puts many businesses at risk, and as this article will show, leading some of these into insolvency. This is the first in a multiple-article series and this one shows two real-life scenarios with the resulting fallout from poor product costing.

Business Case One:

Several years ago, we performed a product costing audit for an $80M business that built an excellent line of large, mobile products for the telecommunications and utility industry. In their plant, they were very vertically integrated, having full steel fabrication, machining, assembly and equipment installation capabilities. In our initial review of the business, what triggered our initial concern was that they were using a fully-burdened shop rate of $75.00 per hour for all their processes, whether this work was performed in the highly automated and capital-intensive fabrication and machine shop or their manual assembly operations. Therefore, our initial position was that they were undercosting their fabrication and machining labor operations and overcosting all of their assembly operations, both by probably a multiple of two. We stated that this costing inaccuracy was driving them to make incredibly wrong decisions with respect to their manufacturing operations and that further study would prove this.

For some background, because of the costing inaccuracies, they had justified and heavily invested in capital equipment for fabrication and machining and were continuing to bring more of this work back inside from outside suppliers. On the flipside, the assembly product costs appeared to be too high, which had put them on a track to outsource more of their assembly and installation work each year, the greatest portion of the direct labor work content. Unbeknownst to them, this last scenario was clouding the costing even further by heavily diluting the hourly base for allocating overhead costs and driving their costs per hour even higher. After only two weeks of work, we presented our findings and the management team was stunned.

In the study, we performed an activity-based analysis on every production work center in the business. Activity-based costing ties all associated costs to the driving factor, and with respect to this business, we tied the heavy depreciation costs, tooling development, Manufacturing Engineering and floor technician support to the sophisticated fabrication and machining centers. With respect to the assembly labor, this overhead was much lower, and given the high numbers of hours in the group, we expected that the rates would be much lower than what they were currently using. The actual results of the study showed that the hourly rates of the fabrication and machining workcenters should be much higher than currently stated, from $130 to $150 per hour depending on the workcenter, while the assembly labor rates should be much lower at about $45 per hour.

Because of their inaccurate costing, they had mortgaged their future by spending too much cash on capital equipment that was never financially justified and added far more cost than they financially account for in producing the components that used these capital assets. Now they could also see that their outsourcing of the assembly labor was actually at a premium and costing them substantially more than their in-house costs. This loss of production hours also reduced the absorption of the burden, only worsening their cost position. The net result of these problems was that costs were going up in spite of their actions and they were now both in a competitive disadvantage and cash poor, due to the high level of capital investment. We strongly recommended an immediate change in direction to fix the issues.

The unfortunate result of this project was that the President of the company refused to step up and take action. His only action was to replace the CFO but made no changes in the product costing. Within only 18 months, cash strapped, they were forced into liquidation, forcing the layoffs of over 300 employees.

Business Case Two

This scenario was much more typical of what we see in that the audit of a year-end physical triggered the write-off of inventory, in this case $1.2M in inventory for a $60M business. We were called in to find out what the problem had been caused by and fix it.

Based on our experience, our immediate sense was to look at product costing. We quickly surmised that the two product lines were substantially different in cost structure. One product was in a highly competitive market that was very price sensitive, especially in the last two years and this also signaled a potential top line revenue problem. Up until this point, the business had always been very profitable and had never seen the need to develop detailed product cost data. Therefore, we worked with the management team and developed costs for the majority of both product lines in about a month.

In our research we found that the business sold two families of products and for years had success with tracking the COS at 67% for both lines. The new costing information we found showed that one family actually had a COS of 52%, which was a pleasant surprise, while the other was about 87%, and every sale of these products was a loss for the company. The poor margin of the second product was attributed to several factors. The first was that they were offering a premium product with a complete list of standard, high cost options, which obviously added greatly to the base cost. The second factor compounded the first in that in the last two years, the pricing had been reduced to drive sales to this high-feature, price point product and away from the custom products. In our review of the sales history and financial statements, we found that the problem had been in existence since the launch of this price point line, but only when the sales reached a high level the second year was the impact large enough to be visible. As for finding the cause of the write-off, we determined that the cost of the new product was greatly understated when using the 67% COS and the inventory was not being relieved enough to reflect true costs.

In response, we provided a list of actions that included substantially increasing prices on the price point line and pushing sales back to the custom versions of the products. Given the new cost visibility of the other product line which had a much lower COS, they were able to now use pricing to penetrate these markets and increase sales of these models. Both groups of actions dramatically increased profitability.  The total revenue of the troubled product dropped off but the balance, typically custom units, were now profitable. On the flipside, the market share growth of the lower cost line more than made up for the lost revenues and total gross margins increased because of all the changes. Finally, to insure this problem did not reoccur, we worked with the Finance department and created a segmented financial statement – this uses Activity-Based-Costing to build cost data for each product line – that showed each product family as a separate business, allowing each to be managed and optimized on their own.

Conclusion

These two business cases show that inaccurate product costing can certainly create margin and overall business profitability issues. But of greater danger, this lack of visibility can lead to irreversible business damage and if left unchecked even failure, as shown in the first business case. The lesson here is that without accurate and timely product and process cost data, businesses cannot effectively manage their margins or product lines and will be at a strategic disadvantage in their market. In following days, I will cover specific areas to look at with respect to costing materials, labor and overhead and how to accurately build product cost data.