Cross-Functional Management manages business processes across the traditional boundaries of the functional areas minimizing suboptimization.
Customer Product Rationalization
Customer/Product Rationalization (CPR) combines Total Asset Utilization (TAU) with Activity-Based Costing (ABC) to determine the true cost of products and customers. CPR is the only tool which allows you to maximize return on asset dollars gained for resource dollars spent (profit). A dynamic CPR model allows you to devise an Account Acquisition Strategy and reward Sales and Marketing for increased profits instead of revenue or sales volume.
Methods to ensure that your process provides your customer with predictable outputs of sufficient quality into their process.
The day to day activity which is the primary purpose of the area.
The monitoring, control, and reaction activities of important processes to maintain performance levels and prevent backsliding.
The system that takes system input, prioritizes opportunities, and deploys and monitors local resources.
The ongoing communication of information to provide system feedback, focus, and alignment within and between areas.
The defining of the area's processes and the development and implementation of common operational practices.
Providing systems and support which involve employees in their own process within their span of control.
The establishment of the roles and responsibilities for the area.
When you accurately model the Total Asset Utilization of a process and use that information to improve your effective use of those assets, you have essentially found additional capacity without significant capital investment, thus finding additional capacity that was there all along.
FMEAs have been used in the aerospace and automotive industry for decades. People knowledgeable about the process examine and record the ways that it can fail to perform then rank using a 1-10 scale how frequently that failure would occur, how difficult it would be to detect, and how severe it would be. These three factors are multiplied and the resulting number is called the Risk Priority Number (RPN). This number is used to prioritize the failure modes so that corrective actions can be taken to reduce the frequency, and severity and/or improve the detectability. There are four main types of FMEAs:
Design — used when designing equipment. Failure mode is how the equipment can fail to perform its design intent assuming it is not broken. Corrective actions are to the equipment.
Process — used to analyze a process to use equipment. Failure mode is how the process can fail to meet internal and external customer needs assuming the equipment functions as designed. Corrective actions are to the standard operating procedures (SOPs) and possibly to the equipment.
Product — used when designing a new product. Failure mode is how the product can fail to meet the final customers’ needs assuming it is manufactured as designed. Corrective action is to the product design.
Equipment — used when trying to improve equipment reliability. Failure mode is the how equipment subsystems cease to function according to the design intent. Corrective action can be to equipment design or preventative maintenance practices.
Gauge control studies are used to quantify the measurement technique you are using to measure a characteristic in terms of:
Control — the gauge is predictable through time
Repeatability — the variability associated with measuring the same thing time after time
Reproducibility — the variability associated with different operators measuring the same thing
Capability — the total variability due to repeatability and reproducibility compared to of the level of discrimination needed given your measurement specification
If a gauge or measurement technique you are using is not in control, it changes through time and if you measure the same thing next week you will get a different answer. Without control, you cannot even begin to make the measurement you want. Also, if you recalibrate a gauge too frequently, you can introduce variability into the measurement system. Once it is in control, your long-term gauge study, will tell you when you need to recalibrate.
If a gauge has a high variability due to repeatability, you may want to examine your measurement technique to see if you can reduce this variability.
If a gauge has a high variability due to reproducibility, you may want to make sure that the different operators are following the same measurement technique to see if you can reduce this variability.
These studies become more complex when the gauge is a “destructive” gauge, where you can only test one specimen once after which it is altered. There are advanced techniques available for these situations as well.
A process which is able to provide exactly what is needed when it is needed. This minimizes inventory stock or idle time while maintaining perfect delivery performance.
PDCA stands for Plan- Do- Check- Act, a scientific way of approaching a problem: Plan what you are going to do by investigating the problem; Do what you plan to try to fix the problem; Check to see if what you did fixed the problem; Act to standardize what you did to fix the problem, or if the Check showed that you have not fixed the problem, start the PDCA cycle again.
The systems of Daily Management that bring about definition, control and continuous improvement of the area.
There are two types of quality: design and conformance. Design quality is how well the product or service specifications meet the needs and expectations of the customer. This type of quality can be improved using design FMEAs and Voice of the Customer matrices. Conformance quality is how well the product or service specifications meet the needs and expectations of the customer.
Reliability is the probability that a part, assembly, or system will perform its intended function in a stated condition or environment for a predetermined length of time without failure.
Growth Analysis is used to determine if a system has increasing, decreasing, or constant failure rate. It is used to determine if preventative maintenance is ineffective or effective.
Fault Tree Analysis is a top-down approach for determining the root-cause of why a particular failure occurred.
MTBF is a measure used to track the average time between failures, where a failure is an inability of the equipment to satisfy performance or meet design specifications and has the expectation of successful performance without adjustment or rework.
MTTR is a measure of the average time to repair a failure.
Statistical Process Control (SPC) is a diagnostic tool that allows you to determine “special” versus “common” causes of variation. Common causes of variation affect every process, and are why nothing is ever exactly the same. Special causes of variation occur when something happens that is not usually part of the process. SPC allows you to identify when these special causes occur so that you can eliminate them and bring predictability, or “control” to a process without overreacting to normal variability. SPC by itself does not change a process - you must have a reaction plan in place that tells you what to do when a special cause occurs.
By using a control chart you can graphically monitor a process variable and continuously perform a statistical test to determine if the mean has shifted, an indication that the process has changed. Control charts also identify an increase or decrease in process variability. The upper and lower limits of expected variability are calculated from historical data.
A common misconception about control charts is that they control a process. In reality, they are a powerful diagnostic tool to see if the process has shifted, and although they can be used to change the process when a special cause occurs, they are not the most efficient way to control a process in real time. Engineering Process Control (EPC) when used to its full capability will give you lower variability around a target. Click here to read a white paper by LWI consultant Althea D’Souza about some options to use control chart-like format to adjust your process.
If you want to learn more about SPC, you can purchase Quality With Confidence, written by LWI consultant Mike Petrovich and LWI founder Dr. Jeffrey Luftig. LWI also has the premier SPC training in the world available at your location. Click here if you are interested in attending this consistently highly rated class.
A system which uses statistical techniques to monitor the output quality of a process to assure an internal or external customer of a consistent product or service.
Statistically Designed Experimentation
Statistically Designed Experimentation economically optimizes process settings to improve your critical outputs.
Suboptimization occurs when you optimize the performance of a department or area which adversely affects other areas and results in a cost to the total organization. The optimization of every department or area will result in a suboptimal organization. An optimized organization will have some areas that operate less than optimally.
Methods to ensure that your process’s supplier provides you with predictable inputs of sufficient quality into your process.
A systematic methodology to manage a business. It is critical to avoid the pitfall of focusing exclusively on product without devoting significant effort to the administrative units, the downfall of many primitive “TQM” efforts in the early 1990’s. Total Quality, or Total Business Management, should focus on improving the financial and non-financial objectives of the business (or Key Performance Indicators [KPIs]).
Total Quality Assurance ensures that a business has predictable and sufficient quality to meet their internal and external customers’ needs. An output of three systems: Supplier Quality Assurance, Customer Quality Assurance, and Statistical Quality Control