Analysis of LeWin’s Kanban System for Part M670N

Introduction

In this essay, we will analyze LeWin, a French manufacturer of gaming devices, specifically focusing on their Kanban system for producing part M670N. We will address two key questions related to this Kanban system. First, we will determine the value of the policy variable, x, which represents the implied safety stock in the system. Secondly, we will calculate the required reduction in waiting time if three containers are removed from the process while keeping all other parameters constant.

Part A

Calculating the Implied Safety Stock (x) To calculate the implied safety stock (x), we need to consider the key parameters of LeWin’s Kanban system for part M670N.

Daily Demand: The daily demand for part M670N is 1,725 units.

Average Processing Time per Unit: The average processing time per unit is 0.002 days.

Waiting Time at Feeder Workstation: The average container spends 1.100 days waiting at the feeder workstation.

Container Capacity: The container for M670N can hold 395 units.

Authorized Containers: LeWin has authorized 14 containers for this part.

The formula for calculating implied safety stock (x) in a Kanban system is given by: $x=(D\ast p)-(D\ast T)$

Where:

D represents the daily demand (1,725 units).

p represents the average processing time per container (not per individual part). To calculate p, we need to divide the processing time per unit by the container capacity.

T represents the average waiting time at the feeder workstation.

Now, let’s calculate each component:

Calculate p: $p=(0.002days/unit)\ast (395units/container)=0.79days/container$

Calculate x: $x=(1,725units/day)\ast (0.79days/container)-(1,725units/day)\ast (1.100days)=1370.925units$

So, the value of the policy variable, x, that expresses the amount of implied safety stock in this system is approximately 1370.925 units.

Part B: Determining the Required Reduction in Waiting Time Now, let’s calculate the required reduction in waiting time if three containers are removed from the process while keeping all other parameters constant.

The formula to calculate the new waiting time (T_new) is: ����=�−(��������/(�∗�))Tn​ew=T−(xr​emoved/(D∗p))

Where:

T represents the original average waiting time (1.100 days).

x_removed represents the safety stock removed (3 containers * 395 units/container = 1185 units).

D represents the daily demand (1,725 units).

p represents the average processing time per container (0.79 days/container).

Now, let’s calculate T_new: ����=1.100����−(1185�����/(1,725�����/���∗0.79����/���������))Tn​ew=1.100days−(1185units/(1,725units/day∗0.79days/container)) ����≈0.983����Tn​ew≈0.983days

The new waiting time is approximately 0.983 days. To calculate the reduction in waiting time as a percentage, we can use the following formula:

�������������������≈�−�����∗100%=1.100����−0.983����1.100����∗100%≈10.64%ReductionPercentage≈TT−Tn​ew​∗100%=1.100days1.100days−0.983days​∗100%≈10.64%

Conclusion

In conclusion, we have analyzed LeWin’s Kanban system for part M670N. The calculated value of the policy variable (x) representing the implied safety stock in the system is approximately 1370.925 units. Additionally, if three containers are removed from the process while keeping all other parameters constant, the new waiting time would be approximately 0.983 days, resulting in a reduction in waiting time of approximately 10.64%. This analysis demonstrates the importance of understanding and optimizing Kanban systems to improve efficiency and reduce inventory costs in manufacturing processes.