Lean Tech helps manufacturing companies become more competitive by reducing cost, improving quality and increasing productivity, while contributing to a sustainable development.
Lean Tech provides consulting services within the following areas:
- Reducing raw material consumption by reducing shrinkage and wrecks, optimizing production processes and improving production procedures / efficiency
- Reducing energy consumption through benchmarking consumption (consumption per machine, equipment, products, etc.) and improving energy-intensive processes
- Improving product quality by understanding process variation and implementing statistical process control (SPC) to control critical variation.
- Improving production flow and increasing productivity / machine efficiency (OEE) by performing bottleneck analysis and buffer capacity analysis.
- Emission control and establishing monitoring program
- Determining measurement error and improving measurement systems (using CoV - Components of variation, and MSA - Measurement system analysis)
- Experimental design (DOE) and analysis of results when developing new products or improving existing ones
- Defining goals and measures / performance indicators
- Quality control, structuring and analyses of data in order to determine root causes and identify areas for improvement
- Data visualization
- Cost-benefit analysis
- Root cause analysis and problem solving
- Preventive maintenance by using Statistical Process Control - SPC
- Courses and training within Lean and Six Sigma
Lean Tech provide courses and training within Lean and Six Sigma. The training focuses on finding practical examples and adjusting the training in order to fit the participants and their company's need. For more information contact Lean Tech.
Lean Tech focus on continuous improvement and reducing waste. Lean Tech create value to the customers by using tools within Lean & Six Sigma. Common tools are value stream mapping (VSM), thought maps (TM), process maps (PM), measurement system evaluation (MSE), components of variation (CoV), design of experiment (DOE), statistical process control (SPC), cause and effect analysis, cost-benefit analysis & 5S.
If you start benchmarking theoretical consumption and actual consumption, you can get an estimate of the potential savings. If these are significant, the next step is to look at how to reduce the gap.
Conduct a benchmarking per product and per process to see where to focus. Raw material consumption can be reduced by focusing on reducing shrinkage and optimizing the production process in order to ensure efficiency.
For companies who recover raw material by use of distillation processes, significant savings can be gained through optimization. For one company I managed a project where we reduced the raw material consumption with 470 000 $ annual by recovering distillate that normally were sent to the boiler house. Here are 10 efficiency tips.
Start by mapping and benchmarking
today’s consumption: how much energy is used for different purposes in production? Energy per product? Energy for different process steps? For different machines and equipment?
The more you know about your energy consumption, the easier you can improve the energy efficiency. If you do not have the necessary measurements, you can calculate the theoretical consumption, and start by improving your most energy intensive processes. I have helped a company reduce the energy costs with 200 000 $ annual by mapping the consumption and optimizing the production process. You can read about it in my 10 efficiency tips.
In order to do quality improvement it is important to consider all factors that can be significant. Start by identifying everything that can affect your product. All processes are subject to variation, which may be classified as random or chance variation / noise (common cause) or assignable cause variation (special cause). Statistical process control - SPC is a tool to separate between normal and special variation, and can be used to understand and control variation. You can watch my video about statistical process control
For some manufacturing companies, multiple independent machines cooperate to produce the finished product. Some machines produces more scrap, have more down time or run at lower speeds. This might result in long waiting time for the next machine. Obviously, capacity can be increased by increasing the efficiency of the slowest machine, but also increasing the buffer between machines can improve productivity. The machine with the lowest efficiency should have enough buffer to ensure that it never waits for the machine up-stream. If stop causes and stop duration are logged, analysis can reveal the increased capacity gained by investing in buffer capacity. Lean Tech perform buffer capacity analysis, investigate production flow and help companies defining relevant stop causes. A commonly used productivity
indicator / KPI is OEE
(link to free video).
In order to reduce your emission you need to know the current status and the emission sources. Start by mapping todays’ situation; what is the size of your emissions to water, air, landfill and recovery? This can be detected by sampling. I have identified wastage of 1.4 million $ / year for a company by mapping emissions. By gaining emission control
, you can achieve significant savings. I have also prepared an IPPC (Integrated Pollution Prevention and Control) report for a customer, where all emissions and waste are described and quantified. Part of the scope was also to compare their processes to BAT (Best Available Technology). You can read more about this IPPC
In Norway, emissions are regulated through the corporate discharge permit. The Norwegian Environment Agency requires that Norwegian companies document their emissions to soil, air and water to ensure that they are within the requirements of the permit. In 2010 came a demand from The Norwegian Environment Agency to establish a monitoring program for emissions to air and water. Lean Tech was hired by Allnex to create a Monitoring program for emission to air. The work included calculation of the various emission sources contribution to the total emission. The uncertainty related to the various emissions was also determined. You can read more about the monitoring program
for air emission.
Of major importance when doing improvements is to define the right goals and decide how to measure (KPI - key performance indicators) in order to follow them up. Companies spend a lot of resources on data acquisition, data warehouse, reporting and visualization. But what information should you use to monitor your process and evaluate your goals? How will you break down overall business objectives into operational targets? The key performance indicators, KPI, for individuals must be something the person can influence, in order to create motivation. It is also important that management chooses balanced targets. By focusing on productivity
and delivery this can result in high quality cost or high wastage if not balanced.
Today there is access to infinite amounts of data; but what data is important and is data quality adequate? When it comes to data collection I recommend to use resources ensuring the quality of the data, and to only collect the data you need. Do not practice "nice to have". I have witnessed the implementation of a comprehensive data acquisition process that resulted in production downtime due to failure with the data collection. The rule "Make it simple" applies here as well. Lean Tech can help you if you want assistence with quality assurance of data or help with defining what data to collect.
Through data visualization using statistical programs like JMP, it is easier to see connections and trends between different data. Multivariable analysis shows the correlation between the various factors. Do the different factors affect each other? In what way? If you have data set you would like to analyse for trends and correlation you can contact Lean Tech.
All measurements involves uncertainty. Suppliers of measuring equipment usually specify the measurement uncertainty as a symmetrical interval around the measurement result. Within Six Sigma, measurement system evaluations (MSE) is used to determine the real uncertainty of measures. This can be done by measuring the same item repeatedly, and levels like analyst, instrument, batch can be included to see how much variation each level contribute to the overall variation. Based on the result of the repeated measurements, the real uncertainty of the measurement can be determined. I made a video about measurement error
where I use CoV(components of variation) to decide how much the time of the day, the day of the week and week, contributed to variation in my weight.
With experimental design you can design your experiments in such a way that you get the information you need with minimal effort. It can be used both when developing new products and when improving existing ones. My experience is that significant resources can be saved by doing smarter design of experiments. As an example, I analysed the results of 1100 trials within an experiment. The same information could have been achieved by running only 150 trials. Lean Tech can assist you with DOE and help you analyse the result.
When the levels of a factor are random, such as operators, days, batches, it can be better to perform a CoV - Components of variation. While the levels in a DOE is controlled, a CoV estimate how much various factors contribute to the overall variation. In a CoV the components varies naturally, while in a DOE you control the levels. CoV can help you understand variation: How much do the different process steps contribute to the overall variation? What factors are critical for product quality? What about measurement error? How much do the time of the day, the day of the week and week, contribute to variation in my weight measurement? Check out this CoV
(components of variation) example.
In order to decide between alternatives, cost-benefit analysis are valuable. A regularly occuring challenge is to quantify the effect of the various alternatives. This requires the ability to see the full picture and to assess the overall effect of each option. It also requires measurements to quantify the effect, and that you are able to obtain the facts. Six Sigma focus on evaluating several options, identifying pros and cons and calculate the cost-benefit effect of the various alternatives. It does not need to be 100% correct, 90% is usually more than enough to make the right choice.
The aim of root cause analysis is to identify the factor that resulted in one ore more past events. Often the event is unwanted and by identifying the root cause it is possible to prevent the event from happening again. There are several tools that can be used for identifing root causes. I often start with process mapping (PM) in combination with preparing a Thought Map (TM) or A3 in order to structure the problem solving. Depending on the problem I use various tools like Fishbone diagram / Cause and effect diagrams, Factor analysis and Pareto analysis.
Statistical process control
(SPC) is a tool for distinguishing between normal and special variation. It has many applications, one of them is preventive / predictive maintenance. Equipment wear will gradually increase variation. By identifying the normal variation of a process, you can react to special variation caused by need for maintenance, and thereby working more preventive rather than firefighting. Alarms related to the variation of your measurement (Range or stdev) can alert you when parts need to be replaced.