The Value of Scrap, Rework, and Yield at Steel Foundries

R. Stevenson, D. Coyle and G. Richard

Steel foundries must consider both internal and external opportunities and threats when planning for the short and long terms. While external threats, such as sharp decreases in steel casting demand, are outside the control of steel foundry management, opportunities are present within all steel foundries to reduce costs, improve on-time delivery performance and shorten lead times. Inconsistent as-cast quality has an impact on scrap and rework costs, making it difficult for steel foundries to remain responsive while trying to win new work and/or meet customer delivery requirements. 

The relationship between internal and external opportunities can be seen in recent reshoring examples, where some companies are moving away from global supply chains to more stable and predictable local suppliers. The results of a recent survey highlight that quality, cost and delivery were the most common cited motivations for companies that have initiated reshoring efforts (Table 1). Manufacturers that are able to focus on internal opportunities to improve quality, cost and delivery will be able to capitalize on more external opportunities such as winning new work from companies that are looking to reshore.

Variability in As-Cast Quality

Variability, or the lack of uniformity, in manufacturing processes and products greatly affects a manufacturer’s ability to meet customer demands and specifications. The destructive nature of variability on manufacturing systems and the resulting scrapped and reworked products, high amounts of work-in-process, long lead times, and ultimately, the overall cost to manufacture a product have all been well documented (Fig. 1). 

For those involved in managing steel foundry operations, the impact of variability on queue time can be reflected in the challenge of efficiently managing the flow of jobs through the cleaning room. While molding and melting departments can be reliably managed and scheduled based on molds-per-day or heats-per-day targets, the cleaning and finishing operations have traditionally been much less predictable and manageable in steel foundries. In the case of the cleaning room, the amount of time spent on welding and grinding can be inconsistent—often due to the highly variable as-cast quality levels of the castings entering the cleaning room. This variability in processing times creates variability in the arrival times to operations further along in the production process and greatly increases the amount of time castings spend sitting in queues, waiting to be processed in the cleaning room.
While the difficulties in managing cleaning operations at steel foundries is generally known among steel foundry managers, the extent of this problem is quite often not quantified. A study by Beyersdorf, Peters, and VanVoorhis highlighted the findings of a project that investigated material handling, scheduling procedures, rework, layouts and product flow in 22 different steel foundries. The study highlighted excessive rework and excessive material handling as two main problems. It concluded that the magnitude of the rework problem was largely unknown because of the inability of most foundries to track castings and document the total amount of required finishing time. 

Additional studies have focused on quantifying the variability of cleaning room operations within steel foundries, revealing the processing times for repair welding and hand grinding operations are highly variable. One case study showed that 91% of the grinding time on an order for 50 1,000-lb. steel castings was spent on grinding casting defects. If an operator were to grind castings with the same quality level (i.e., 91% of grinding time spent grinding on defects) for the entire year, this would cost the foundry $45,500 annually. While all castings ground in a year, particularly in a job shop environment, are not likely to have the same amount of defects, the example does help illustrate the potential magnitude of grinding costs at steel foundries with excessive casting defects. 

The authors of the study went on to add that “additional costs to solve these quality issues are incurred by the welding operations, inspectors, heat treatment, and material handling.” The same paper showed two additional case studies. In one case, 78% of the grinding time was on defects and in the other case 65% of grinding time was spent grinding on defects. Another study that tracked the amount of welding done on castings at a stainless steel and high alloy foundry showed one part number had repair weld times that ranged from 1-16 hours. This wide range not only drives up costs for labor and materials, but also increases the amount of time that parts spend in queues waiting to be processed, which makes scheduling and shipping on-time to customers difficult. 

The Cost of Poor Yield

A survey conducted by Beckermann and Hardin in 1997 quantified typical yields and risering practices in steel foundries and summarized the negative consequences of lower casting yield to include: additional costs in remelting scrapped steel (estimated to account for 7% of the total casting cost); the need for increased capacities for melt furnaces and melt handling; and increased costs associated with additional labor, molding and sand use. The survey results showed the average casting yield for the 93 participating steel foundries was 53.3%, and the average best- and worst-case casting yields were 72.7% and 33.2%. 

Factors impacting the reported yields included tons poured per pattern, product type and the resulting quality requirements, risering rules used, alloy, and geometric complexity such as minimum and average section thickness and the number of isolated hot spots. The most common defects limiting yield improvement were shrinkage voids, microporosity, cracks and segregation. The most common methods of improving yield were the use of computer simulation, using insulating riser sleeves and improving casting design. From this survey, it was concluded the development of less conservative risering rules would be beneficial in helping steel, stainless steel and high-alloy foundries to improve yield. 

Quantifying Scrap, Rework & Yield

The amount of published data on how much rework and yield impact steel foundry operations and costs is limited. Furthermore, many of the costs associated with repair welding and grinding operations vary from one foundry to another, and the amount of repair welding and grinding will also vary based on work mix and the complexity of the product mix being produced at a given foundry. A tool for estimating costs for rework, scrap and yield improvements would be beneficial for steel foundry personnel responsible for understanding and prioritizing opportunities within their operations. 
The cost estimator tool developed as part of this work has three different methods for calculating costs using different inputs. The inputs and outputs used for each method are shown in Table 2. These methods include a foundry-wide estimate using a minimal number of inputs (referred to as “Level 1” estimation), a categorized method that requires more inputs (referred to as “Level 2” estimation) and a per-casting estimate that requires the largest number of inputs (referred to as a “Level 3” estimation). 

Estimating Foundry-wide Costs

An analysis using the foundry-wide cost estimating method was performed for four different sized steel foundries. Figure 2 shows the estimated potential savings for steel job shops with the same estimated average yield and same percentage of cleaning or rework being done as a baseline. Estimated savings for a 1% improvement in scrap, 1% improvement in cleaning and 1% improvement in yield were all calculated for foundries with four different annual revenues and annual tons shipped. 
From this analysis, significant opportunities were identified. Additionally, although the 1% cleaning improvement is worth less than a 1% scrap reduction, most steel foundries will experience more of an opportunity to reduce cleaning costs since many may have roughly 30% of their costs going to cleaning and repair operations while maintaining relatively low scrap rates. Often, the ability to repair weld steel casting defects results in low scrap rates but high costs for welding and grinding repair operations. These estimates are considered conservative and do not place a dollar value on the extra capacity that improvements in these areas would provide—or to the improvement in on-time delivery that would result from improvements in these areas.

Estimating Costs Per Casting

While estimating foundry-wide cost savings may be helpful in performing high-level planning and prioritization of continuous improvement initiatives or capital expenditures, foundries  can also benefit from savings estimates on a per-casting basis. For this case study, a 5-lb. nickel aluminum bronze casting was originally produced using tooling that had a 13.8% scrap rate for reoxidation inclusions. Due to the high scrap rate on this casting, it was targeted for gating improvements using a modified gating system that lowered the scrap rate to 2.7%. The foundry had an in-house cost estimator model for quoting jobs before production but did not have a set method for estimating the dollar value of such a scrap reduction. Using the estimator our team has developed, the foundry provided the inputs listed in Table 2 for the Level 3 cost estimator. In this case, the casting cost, when estimated with a 0% scrap rate, aligned well with what the foundry had estimated prior to entering production. The estimator was then used to estimate the cost of producing the casting at the original 13.8% scrap rate and at the improved 2.7% scrap rate. The estimated savings of $24,420, as shown in Table 3, would not have been quantified directly without the use of this estimator.  

In addition to this case study, Figure 3 shows the savings for six other case studies from different steel and high-alloy foundries. Each case study involved tooling changes made to one part number at each foundry. These cases include a large weight range (5–30,000 lbs.), different materials (plain carbon steel, Ni-Al bronze, stainless and high alloys), a wide range of casting volumes (lot size ranging from 15 castings to 6,000) and different casting processes (two investment casting foundries and four nobake sand foundries).

The defects targeted in these case studies included reoxidation inclusions, macro- and micro-porosity and hot tearing. The foundry that shared Case Study No. 2 reported a $200,000 savings on four castings that were targeted for reducing inclusions and eliminating repair work on microporosity indications. Case Study No. 3 was part of a foundry-wide scrap reduction initiative that targeted 28 patterns and led to a 2.65% scrap reduction that was estimated to save the foundry $514,000 that year.

As these case studies show, steel foundries have significant opportunities to reduce costs and improve operations by targeting scrap, rework and yield with the help of a cost estimator tool. Projects that target reducing as-cast quality variability and improved yield should be prioritized highly by steel foundry management because of the potential cost savings and the improved ability to predict and schedule cleaning room operations.    

Click here to view the article and graphs in the digital edition of the September 2021 Modern Casting.