Modern Machine Shop

DEC 2016

Modern Machine Shop is focused on all aspects of metalworking technology - Providing the new product technologies; process solutions; supplier listings; business management; networking; and event information that companies need to be competitive.

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Page 89 of 164 December 2016 MMS 87 FEATURE and tooling types. They form the basis of our annual Capital Spending Survey. Now the numbers: Having peaked at $7.5 billion in 2014, machine tool consumption con- tracted 3 percent in 2015 and 7 percent in 2016. Based on the 2017 Capital Spending Sur vey, projected total machine tool consumption in 2017 will be down an additional 1 percent. However, a deeper look at the survey results shows that demand for core machine tools (the types provid- ing the most commonly applied processes) will increase in 2017 by 9 percent. In addition, GBI's new econometric model for machine tool unit orders indicates that the rate of contraction in overall machine tool demand bottomed in July 2016 and will improve through the end of 2017. New orders for durable goods have contracted since the summer of 2015, although there were signs of improvement in the summer of 2016. (Durable goods are products such as aircraf t engines, automobiles and equipment that have an extended useful life for the buyer. Both the making and the buying of these products tend to ramp up and down at a pace that mimics signif ic ant e conomic tre nds, so e conomists watch these figures closely.) When durable goods production seemed to have peaked in the summer of 2015, it did so at near record levels. The fact is, the industr y's capacity to produce durable goods has been utilized to a lesser and lesser degree. This "capacity utilization" has slowly, but steadily declined since early 2015. So, what's driving the forecasted rebound in capital equipment spending at metalworking facilities? In short, it's a need for increased pro- ductivity. Shops need to increase productivity in order to remain competitive in a global manu- facturing marketplace and to counteract the much-talked-about skills gap. More and more shops are turning to lights-out and/or unattended machining to achieve this increase in productiv- ity, but new equipment, including machine tools, workholding and automation, is needed to run lights-out. According to the 2017 survey, buying a machine th at c a n inc re a se p ro d u c ti v i t y is c l e a r l y th e No. 1 motivation for buying a new machine tool (see Figure 1). While this was the first year "increase productivity" was a possible answer, the survey has asked about motivational factors for the last 30 years. Although several of the factors that motivate machine buying have declined in impor- tance over time, one factor in par ticular—the availability of "new models"—has remained fairly steady since 2006. This observation fits with the motivation to increase productivit y, because there are basically only t wo ways to do that: improve the capability of either the capital equip- ment or the human labor. The labor option is less attractive right now because workers with the appropriate skills and habits are difficult to find and, if found, command wage/benefit packages that are costly. More important, worker productivity has a relatively modest potential for improvement, in any case. These facts compel manufacturers to achieve productivity gains by investing in capital equip- ment. What's more, new models of machine tools, and the latest offerings in workholding and automation, are significantly more productive than they were just five or 10 years ago. SCIENCE TO ENHANCE THE ART Let's step back at this point to look at the back- ground of this research and the methodology behind it. We at GBI think significant changes in our research methods make the findings of greater value than ever before. For the past decade or so, we created our own forecasts for machine tool unit orders based on several leading indicators considered critical for the metalworking industr y. While correlations between the leading indicators and machine tool orders were used to create the forecast, the process was largely an "artistic" one: that is, it was based on what we believe are keen percep- tions and a deep wisdom derived from our years of stu d y ing this m a r ketp l ac e. A l thou g h this approach has proved remarkably accurate, one relying less on interpretation promises to strengthen credibility as well as increase reliability. So, this past summer, GBI developed its first economet- ric model, using techniques that are more scien- tific in methodology. This model blends the art and the science of forecasting. To build the model, GBI started with slightly more than 2,000 economic time series. Examples of an economic time series include the level of

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