Reducing Cost of Goods Sold

Manufacturing products produces waste that ranges from overproduction, waiting time, and transportation costs to overprocessing, excess inventory, unnecessary motion and scrap. By eliminating these wastes, production time and cost of goods sold (COGS) are reduced, and quality is improved. COGS reduction is one of the fundamental drivers of a lean manufacturing initiative. Used to measure the ongoing success of lean manufacturing, it fundamentally captures material, labor, overhead and tooling costs. However, COGS reduction shouldn’t be thought of as a phase in a lean manufacturing process initiative.

If lean manufacturing initiatives are reducing COGS, manufacturers must be able to accurately measure and manage costs in real time. Only real-time, predictive cost estimates can reliably be used to validate lean initiative decisions and guide corrective actions throughout all the processes of engineering, planning and production, sourcing, quality control, program management and production delivery.

According to Frank Azzolino, President and CEO of aPriori, a producer of cost management software, “It is essential to improve the economics of discrete manufacturing companies throughout the entire product development, production and delivery process by quantifiably and measurably reducing COGS.” Innovative cost management software platforms provide accurate, predictive, real-time cost estimates for all individuals in the process who directly influence or manage product costs such as engineering, planning, manufacturing, procurement and program management.

COGS as continuous process improvement
One of the main drivers for continuous process improvement is COGS reduction. The real-time cost information predicted by the cost management platform provides visibility to the cost effects of the many decisions made during a product’s production lifecycle. Unfortunately, for most manufacturers without the cost management platform, product-cost information isn’t available in real time to all involved—only historical costs are available. Using historical costs information generates inaccurate, out-of-date and irrelevant product costs.

Due to time and scheduling constraints, it’s impractical for most companies to use cost information effectively as a real-time guide for cost management decisions. In most cases, cost estimates are created separately and independently of the people making the design, manufacturing and sourcing decisions. This separation results in many decisions being made in a cost-knowledge vacuum. The effect of these decisions is typically not known for at least a full financial period after production is well underway.

The ability to reduce COGS by providing real-time predictive product-cost information to help the individuals involved in the manufacturing processes make their cost-related decisions has been overlooked and underserved.

A hindsight look at COGS
There are a number of reasons why discrete manufacturers have been forced to take a hindsight look at COGS:

• Cost information in most organizations is fragmented. Critical pieces of cost information are spread across independent departments within an organization and within different functions such as engineering, planning, manufacturing, sourcing and finance. This situation typically results in estimates that don’t include all relevant information required to make accurate and predictive product-cost assessments.
• In most organizations, product-cost estimates are developed by specialized cost-engineering companies or by the value analysis/value engineering departments.
• Product-cost estimates (especially the early ones) are often based on historical information or very general heuristics, are too inaccurate, and lack statistical confidence for effective decision making.
• Most cost estimating falls on a small group of specialized people who spend hours manually producing each estimate. Because the demand for costing feedback can’t always be met, the opportunity to experiment with the cost effects of design, manufacturing, planning, sourcing, etc. alternatives is limited and can’t be readily optimized.
• Most cost estimates are static and aren’t continually updated when new design, manufacturing, planning or sourcing information becomes available as the product progresses through its design-to-production-to-delivery lifecycle. Out-of-date cost information can’t be relied upon for downstream decision making.
• Costing practices aren’t always standardized across the enterprise. As more information is available, different costing practices and methods are used to re-evaluate items. This makes it difficult to leverage previous estimating work and build traceability in product-cost accrual.
• Typically, cost estimates aren’t managed during a product’s development-through-production lifecycle. Multiple cost estimates from different sources are created at different times during the process, making it unclear which product-cost estimate is current or valid.
• Today’s cost-accounting methodologies begin with the financial statement for the prior closed period. The costs in that period are then allocated across various product lines and processes, which are then further allocated for each individual product. These are by definition hindsight product costs.

Azzolino suggests that a cost-management platform alleviates these problems in a number of different ways:

Using design information driven off of mechanical computer-aided design (MCAD) geometry, along with the ability to model production facilities (including machine capabilities, raw materials and facility cost structure) and the specific cost-accounting methodologies, accurate, predictive, forward -ooking, real-time cost estimates are created.

This cost information leverages existing information and data systems in a single-cost platform, captures company-specific costing practices, and makes information available to any user in the organization.

Cost management platforms such as the ones from Apriori have an out-of-the-box support for industry sample factories, as well as for customer- and vendor-specific production facilities. These can all be used for cost assessment and “should cost” analysis. It also includes targeted functionality for design, manufacturing, procurement and program management.

With cost management platforms, predictive, forward-looking cost estimates are available throughout the development-through-production delivery process, improving accuracy and continually converging on the true economic cost of the product.

On the income statement of most discrete manufacturing firms, COGS is typically in the range of 70 to 95 percent of revenue. For many manufacturers, net income is typically three to eight percent of revenue. Looking at a representative company where COGS represents 80 percent of revenue and net income is five percent of revenue, a reduction of COGS by one percent (to 79.2 percent of revenue), generates a savings directly to the bottom line, increasing net income to 5.8 percent of revenue; thus increasing net income by 16 percent.

The best way for manufacturing companies to understand whether their process improvement initiatives are reducing COGS is to measure and manage their costs accurately and in real time.

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