SW: How does one decide the combination of markers for best fabric consumption, quickest spreading and cutting time, or overall cutting room cost? It is our understanding that Optiplan does not decide the combination; once manually decided, the software only calculates the cost for that option.
Harvey : The Optiplan is capable of planning and giving the best possible combinations automatically. It takes into account the manufacturing order, all constraints coming from the model (marker), the material and the manufacturing environment, and uses its sophisticated mathematical algorithms to calculate the optimal size assortment for the individual markers. This is the unique feature of the software, which makes it unique in comparison to the other solutions available in the market. Marker planning is basically equivalent to calculating the best balance between fabric consumption and manufacturing efforts, taking into account all given rules. In the course of finding a marker solution for a manufacturing order, Optiplan performs thousands of calculations and methods to come to the optimal solution in the end.
SW : Optiplan can suggest best roll allocation for spreading of different marker length. However, how has this been made user-friendly when faced with problems such as:
[a] Fabric rolls with defects, which need to be cut-spliced during spreading, resulting in reduced effective roll length. Does the mathematical calculation of the software go wrong?
Harvey : There is a difference between roll planning and roll allocation. Optiplan performs a roll allocation, which means that the roll allocation algorithms are able to compare the actual material requirements for a production order against the rolls in the inventory. Planning rolls with taking into account of the defects has to be an online-process in the cutting room. However, Optiplan does not offer such a feature. Based on the selected allocation strategy, Optiplan proposes to extract rolls from the inventory that would be best for the production order. One strategy would be to follow the ‘best fit’ approach. The software will propose to extract rolls that minimise the leftover: Another strategy would be ‘short rolls first’, which would avoid the situation where the number of short rolls in the warehouse increases over the season. The actual roll assignment to individual spreads still has to be done in the cutting room due to the reasons stated.
[b]Mathematical algorithm for roll optimisation generally works for minimising end-bit length (left out fabric), but while working with defective fabric one requires to maintain specified end bits to take care of re-cutting of defective parts; how does Optiplan take care of this?
Harvey : Optiplan takes care of this during planning of the order, provided the user wants to have this behaviour and makes sure that there are some short markers in the plan for the fabric remnants. The story here is two-fold and goes back to the planning of the order. Very often, there is a mismatch between a theoretical optimal solution of an order and the reality. As stated in the question, we always have short pieces of fabric left on the rolls, and we need at least some short markers to be able to use these pieces. Although this might seem theoretically not to be the best solution (if you just look at the numbers), in real life this is what you need. This, in combination with the fact just explained above that the actual roll assignment is done in the cutting room, takes care of this problem.
SW : It is our understanding that Optiplan can optimize the number of lays and number of fabric layers in individual lays based on size/colour matrix of an order. But this feature effectively has no commercial implication as :
[a] Spreading time and cost roughly remains constant irrespective of layer length variation.
[b] Cutting cost saving does not impact overall cost savings as cutting capacity is always underutilised.
Harvey : Very often, spreading or cutting is a big bottleneck in the production process. The beauty of Optiplan is that it does not follow a strict dogma to save material or to increase productivity, but that it is easily possible to define the ‘optimal’ solution case-by-case. If spreading or cutting capacity is underutilsed, then probably, a more aggressive planning strategy would lead to further material savings. If spreading or cutting faces a bottleneck, then one needs to replan to ensure that material savings do not sacrifice production lead times.