Capconn
Lua error in package.lua at line 80: module 'strict' not found. CAPCONN[1] stands for Capacity Constricted Conveyance and is a diagnostics model for analyzing certain supply chain issues, especially with respect to material handling.
The model graphically illustrates efficiencies in a supply chain. Mathematically, the concept works similar to a series of conveyor belts, each with a different width and rolling at a different speed. If a product, like sand, had to be passed through the series of conveyor belts, then normally one belt would constrict the amount of sand that could ultimately be transferred. However, by not just looking at the 'constrictor' component of the chain, but also the differences in widths and speeds, a supply chain analyst could potentially derive more diagnostic information about the system.[2]
Procedures
The following steps are typically followed:
Step 1: Decide on a discrete time step size, say W. A separate CAPCONN graph needs to be drawn for each time step. Typically, a good time step would coincide with other reporting protocols in a supply chain. For example, if a factory generates a weekly report, then a weekly CAPCONN time step will be appropriate. The choice of time step size depends on the issues that are under investigation. E.g. strategic infrastructure issues will not need a daily time step model, nor will it be appropriate to have a monthly time step if day to day inventory levels are being investigated.
Step 2: Assume j is one material handling component in the supply chain (e.g. produce off-loading). assume yj is the rate of work per individual working component (e.g. the number of boxes that can be off-loaded per hour per forklift). Assume nj is the number of forklifts and xj is the total time of work during time step W, i.e. yj ≤ W.
Step 3 It is now easy to calculate the total work Pj that can be performed by component j as follows:
Pj = (njyj)×(xj)
Pj can also be graphically presented as the surface of a rectangle with a vertical Y-axis being the value nj×yj and a horizontal X-axis being the value xj.
Step 4: A rectangle can be graphically represented for each component j (e.g. P1, P2, P3, ..., PN ) of the supply chain and these rectangles can be placed in sequence next to each other.
Step 5: We now have PN rectangles or containers standing next to each other, each representing the material handling capacity of a supply chain component within a predefined time step W. Now, assume we pour water into the first container and P1 reflects the amount of water that Container 1 can hold. Now pour Container 1 into Container 2. If P1 > P2, then container number 2 will overflow and some water will be lost. Pursue by pouring the water in Container 2 over into Container 3 and carry on passing the water from container to container until the last container (Container N) is reached.
Step 6 Assume the amount of water that reached Container N is P’ where P’ ≤ PN. Mathematically, P’ = min(P1, P2, P3, ..., PN).
Step 7 Insert the same amount of water that reached Container N (P’) into all the other containers (Containers 1 to N-1). This will produce N number of rectangles next to each other. If P’ is colored in as a surface within each rectangle, then each rectangle will be partly or completely filled. Note that since some rectangles could be wider than others (xj > xj+1), it may not appear as if all the colored areas in the different containers are of equal surface, but theoretically they will be.
How to interpret a CAPCONN graph
Typically no supply chain will end up with 100% utilization, i.e. a CAPCONN graph where all the containers are full (P’ = P1 = P2 = ... = PN). This can be attributed to many reasons, such as:
1. Bouncing bottle necks:- Different components of the supply chain can become full under different conditions. In other words, the conditions assumed for time step Wi may be different compared to time step Wi+1, which may imply that another component of the supply chain could become the constrictor.
2. Built in agility:- For some reason, such as supply chain security, additional material handling capacity has been inserted during the design process.
3. Incompatible capacities:- Certain work units, such as ships or trucks, have discrete handling capacities and can be incompatible with the units used the other components of the chain. For example, 10.75 trucks will fill up one ship. We will buy 11 trucks and hence end up with unused capacity.
References
- ↑ Stutterheim, P., Bezuidenhout, C.N., Lyne, P.W.L, 2008. A framework to simulate the sugarcane supply chain, from harvest to raw sugar. Sugar Cane International, 26(1):7-11.
- ↑ Stutterheim, P. 2007. An integrated sugarcane supply chain model: Development and demonstration. Unpublished MScEng Seminar, School of Bioresources Engineering and Environmental Hydrology, University of KwaZulu-Natal, Pietermaritzburg, RSA.
