Up Time Magazine- August/September 2008

Buying a Competitor When buying a competitor, a due diligence review must be performed which includes the usual management, market and financial review of the competitor, subject to the appropriate state and federal laws and proprietary protocols. The management team acknowledges their thinking has been challenged about the Turkey Creek plant’s performance and they agree they must review whether or not the competition’s assets are performing at a comparable level to theirs. It is suspected that the competitors’ assets may be performing even worse, given their current financial difficulty. So, they elected to ask the review team for the Turkey Creek plant to be part of the due diligence team to assess the performance of the competitors assets. The results are shown in Figure 2. ment of 110 Tons/Day vs. 200 Tons/day design capacity to better reflect true capability of the current plant limited operations. This decision also helped the plant meet their capacity and availability performance metrics. The existing Capacity Utilizations for the comparisons are also shown in Figure 2. After discussing the production profiles with the personnel from Turkey Creek, they reported that they could not do any better because the highest volume product at this plant runs at a slower output vs. rated design capacity. The customers have convinced the marketing team that this slow running product is much better for their process operations. The marketing team recognizes this value to the customer but has been unsuccessful in negotiating a higher price for this product. The data in Figure 2 was presented to the “ABC” Company Asset Productivity Champion during an executive leadership meeting. The champion was puzzled with this big disparity between the competitor and Turkey Creek, so now the challenge was to figure out WHY. performance of an operating asset. Typically, most companies look at traditional time-series data to analyze performance. Personnel spend countless hours reviewing the performance in an attempt to quantify the potential for improvements. It is very time-consuming to quantify efforts to improve performance simply from looking at time series data and they typically result in requests to spend major capital dollars to address improvement efforts. After importing the daily production output data into the statistical software, many important characteristics to improve operational performance tend to stand-out more readily. At this point, the data is plotted in rank order on log scale for the “x-axis” vs. a log-of-log scale for the “y-axis”. This data has been disconnected from time-series effects and show distinctive patterns of performance. When the data is presented in this fashion, straight line segments and definitive cusps will appear making it easier to more accurately characterize the overall “hidden plant”. These plots show data plotted in rank order rather than in time series using a method called “Bernard’s Median Rank”. They provide the user with a quick, visual, assessment of the true process performance and potential. Steep slopes, commonly called “Beta or Production Slope” are desirable and display small variability in process output from small common cause variation. Flatter slopes display large variability from larger common cause variation which is built into the process or from special causes. The plots will also establish a single point estimate of typical daily production. This single point estimate always occur at the CDF = 63.2% or the R = 36.8 on the plots and will be the best characterization of the typical production output. This characterization is commonly called “ETA” and the magnitude is determined by the size of the physical facility and show how it is managed and operated relative to rated design capacity. Many plant cultures typically point to equipment failures or asset optimization issues when capacity losses occur. It is a daily no-win battle to genuinely understand the true causes because no formalized, proven plan exists to determine the origin of ALL the loss. All of this compounds the uncertainty on how best to deliver asset and process optimization and keeps a business in constant flux when trying to target the hidden capacity of their operation, the “hidden factory” that lies beneath. Over the past several years, a Leading Service Provider (LSP) has refined this method, to understand the potential, reduce both asset and process optimization losses and improve overall august/september 2008 Production Analysis Time-series 400.0 350.0 300.0 250.0 Tons 200.0 150.0 100.0 50.0 0.0 1 15 29 43 57 71 85 99 113 127 141 155 169 183 197 211 225 239 253 267 281 295 309 323 337 351 365 The “ABC” Manufacturing Director and the champion had recently been exposed to some Figure 2 - Production Analysis newly developed statistical approachThe results of this review were surprising to eves to better characterize production output pereryone. The competition’s plant is reporting acformance by a Leading Service Provider (LSP). tual performance better than Turkey Creek, but These methods are referred to as “Process Relithey were typically only operating at about 50% ability Plots”. These managers began to have of true facility design capacity. The competition dialog to see if this approach could help them had consistently cut fixed costs over the past 5 figure out why. years and also lost many of their resources due to retirement and attrition. Process Reliability Plot methods were developed in the mid-1990’s and are now just gaining It was painfully obvious, that this plant would significant acceptance throughout the manufachave a very hard time pushing to meet the origituring community. Utilizing the abundant data nal rated design. The plant had identified the found in a plant’s assets and operating enviDryer System as deficient with a preliminary esronment, these non-traditional statistical plots timate of $25MM in capital for a new Dryer syscan be used to translate the data into an easy tem. This company had already reported being to understand language of improvement opporstrapped for cash, so they have been unsuccesstunity. Most production output data is riddled ful in selling this Dryer Project to the Engineerwith special causes leading the data to behave ing Director to secure the capital investment in a non-linear fashion and common causes that approval. are not very well understood. Turkey Creek - 2007 - Reported Capacity Utilization = 56% Competitor - 2006 - Reported Capacity Utilization = 89% Competitor - 2007 - Reported Capacity Utilization = 92% As a result, a business decision was made several years ago by the business team to reduce the rated capacity to more of a contractual arrange- Daily production output data is always available because it defines the overall production 10

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Up Time Magazine- August/September 2008 Contents Upfront Upclose Information Technology Lubrication Infrared Maintenance Management Motor Testing Precision Maintenance Reliability Ultrasound Vibration Upgrade

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