This page is given for the summary of practical implemented projects I made, mainly involving into the Lean Six Sigma fields and manufacturing operations. The contents, names, figures and statistics used for those examples are changed from the original versions. There are more projects to come, but they may not be updated here yet..
Operational KPIs Dashboard Show : Hide
In order to maintain the morale and loyalty of direct production operators, the company decided to build the performance measurement systems which evaluate the operators’ productivity and attributes toward operational activities. I were in charge of production matters; therefore, I were responsible for making up that measurement system. Based on my expertise in VBA (visual basic for application) in Excel, I programmed and converted the operator-performance evaluation system into an interactive Dashboard which is dynamically clickable to change data as the following illustration:
The snapshot of KPI dashboard shows the way to evaluate graphically and interactively the performance of each operator, which in turn enables the company to manage incentive policies in particular and production efficiency and effectiveness in general.
Productivity Dashboard Show : Hide
In practice, the huge amount of data in terms of daily productivity, defects is collected and stored in the company’s ERP system. The main issue is that how the data can be used to explore the pattern of production performance, and thereby the potential problems can be detected and then the actions can be made. I took my expertise in data visualization to visualize the massive chronological data in interactive modes as below illustration:
As a shot picture of the dashboard, the pattern of productivity and defects for each machine or work center can be emerged, which results the enhancement of understanding the work center’s current level of efficiency and effectiveness
ANOVA Analysis Show : Hide
The quality of adhesive bonding applied in shoes production is heavily affected by the heating temperature. Not too hot, not too cool but the right degree of Celsius that spreads equally the surface of bonding area will guarantee bonding is effective. However, the problem is how we can know every point in the bonding areas (including toe, mid and heel of a sole) has exposed the same temperature. I accompanied with the team to measure the temperature in the three bonding areas after the sole goes through the heating machine. The data can be accessed by clicking Temperature data. After having the data, I conducted ANOVA (Analysis of Variance) to check whether or not the temperature is different in different bonding areas (toe, mid, and heel).
The data Temperature data is stored in a file named “anova.data” which is used as an input of R statistical software to execute ANOVA analysis. The structure of data is presented as below:
'data.frame': 132 obs. of 2 variables:
$ Position : Factor w/ 3 levels "heel","mid","toe": 3 3 3 3 3 3 3 3 3 3 ...
$ Temperature: num 44.5 44.5 44.1 45.5 45 44.6 46.3 44.1 44.6 44.6 ...
Graphically speaking, the boxplot diagram shows the difference in temperature across the three main bonding area of the sole. Specifically, the temperature of toe is higher than that of mid and heel part.
Technically speaking, ANOVA with TukeyHSD method is used to make the conclusion whether or not the different in temperature is statistically significant
TukeyHSD(aov(ResponseB ~ FactorA), ordered = TRUE)
Tukey multiple comparisons of means
95% family-wise confidence level
factor levels have been ordered
Fit: aov(formula = Temperature ~ Position)
diff lwr upr p adj
mid-heel 0.07272727 -0.4403096 0.5857642 0.9396555
toe-heel 1.49318182 0.9801449 2.0062187 0.0000000
toe-mid 1.42045455 0.9074176 1.9334915 0.0000000
As clearly can be seen that, the temperature between toe and heel, toe and mid is significantly different (p adj < 0.05-confident level -> Null hypothesis is rejected) whereas that between mid and heel is consistent. That leads to a need of investigation on inconsistent temperature causing the bonding gap at the end of assembly line.
Reengineering Show : Hide
Before any improvements were made, the byproducts in form of dust produced by the process of grinding the surface of EVA sheets were released to the environment partially through the incomplete dust processing system. The emission of EVA particles were really detrimental to not only environment but health and safety, and unconsciously quality of silk printing products due to dirty. The solution was to reengineer the dust processing system as illustrated as below:
Apparently, the EVA dust particles are never emitted from the closed-loop refined processing system, which protects environment, stops harming quality and healthy problems.
Benefit & Cost Estimation Show : Hide
Estimation of benefits and costs of any improvement projects is a very essential success factor. The estimation is very informative to help you reconsider whether or not the project is worth implementing as well as a pervasive evidence to convince the master to execute the project if it is feasible and promising. Continued to the previous example “Reengineering”, the analysis of benefits and costs of the project is presented as below:
It can be clearly seen that the one-by-one comparison of the before and after refinement of the EVA dust processing system in terms of benefits and costs are visualized to enhance the pervasiveness of the project potential
VBA for Automatic MRP Show : Hide
Repetitive tasks are really daunting. Unfortunately, MRP standing for Material Requirement Planning is categorized in the repetitive work because of the fact that MRP is to order required materials in a circular just-in-time manner based on the same calculating mechanism. Boring forced him to change the way I normally calculated order quantity for each material in manual to automation by programming the MRP calculation with Visual Basic for Application (VBA) in Excel as below snapshot:
With the built-in MRP programming in Excel, my job was only to wait for the right time of releasing the materials orders to according suppliers by pressing few clicks on the computer, and then walked around the factory, drunk milk coffee, and then go home.
Inventory Planning Model Show : Hide
The complexity in managing the inventory in a real business is very prominent, which raises a need of model to logically calculate stock levels (such as safety stock, cycle stock, and over stock..). These stock levels were calculated by a dozen of formulas. To bring the formulas into the sun, I carefully connected one by one formula in the file, which eventually results in the inventory planning model as illustrated:
With the map, I enabled you to break the secret of the inventory planning model that was used in the FMCG industry.