The textile industry is famous for its very different characteristics when compared to industries in either process or discrete manufacturing. Developing production planning and scheduling software for any textile mill is a real challenge even for seasoned industry experts
The challenges can be grouped as raw material concerns, manufacturing lead time, manufacturing constraints, orders, and inventory.

Raw material concerns involve high raw material costs and seasonal raw material procurement cycles. Cotton, for example, is a seasonal commodity; therefore, the availability and price will change throughout the year. High raw material cost is another issue. Raw material costs may constitute as high as 60 to 70 percent of the total costs.

Manufacturing lead-time can also pose challenges. Manufacturing lead times can be excessive, sometimes more than two months. This is because the raw cotton production process, for example, has to go through many processes and most of them have huge lead time requirements. Looms in particular take the most lead time. A loom machine can make only 500 meters of fabric in a day whereas typical order lengths are in the range of 25,000 to 50,000 meters range.

Most of the manufacturing processes also have high setup times. Quality analysis time also runs high as the finished cloth needs to be manually inspected for defects. Extra lead times also result due to unavoidable generation of inventory in the form of extra meters than the ordered lengths.

Another challenge involving manufacturing is that different processing speeds occur at different work centers, which must also be included in the equation. Dyeing machines, warping machines, spinning machines run at speeds of 30,000 to 50,000 meters of yarn per day but looms run at 500 meters of fabric per day. Because of this, there may be only 2 to 5 dyeing machines, but there may be as many as 500 looming machines in the same plant.

Likewise there are different processing requirements on the same production line. For example, up until the dyeing process, the manufacturing process fits orders that are big and similar. But at looming, the manufacturing process fits many smaller and varied orders. This poses a real challenge, as fitting these two diametrically opposite requirements is next to impossible to do.
Another issue is the unpredictable generation of second quality textile and the fact that variations in color and shade is only known after the fabric has been woven and finished (though they are caused back at the dyeing stage). This can result in a lot of rejected material being processed unnecessarily, thus adding to manufacturing costs and processing time.

These manufacturing constraints ultimately impact customer orders. Because the production rates are very low on looms, customer orders are broken into smaller sub-orders, and the sub-orders are distributed to many looms to reduce the lead time for individual orders. However, variations in the color or shade from the order can also emerge, which, as explained earlier, are detected at the end of the entire process.

Not surprisingly, inventory is another challenge faced by the textile industry because there is a high generation of extra finished products. In addition to extra material resulting from second quality and color shade variations, extra yarn moves through the entire production cycle. Up to dyeing stage, the work-in-process (WIP) is in yarn form and the length of this yarn is fixed at the yarn making stage. It cannot be cut as per order lengths. These extra meters travel through the production cycle and end up as excess inventory, which is later adjusted in the next planning cycle.

Consequently, plant capacity is inefficiently utilized due to unavoidable generation of extra meters-more than the lengths ordered.

After going through these constraints, it is obvious that it is difficult to develop production planning and scheduling software for the textile industry. Only a veteran who has in-depth industry knowledge as well as knowledge of how to tackle these constraints in the implementation can develop a software for planning and scheduling for the textile industry.

Dyeing versus Looming
It is very important to understand the different requirements at the dyeing and looming processes so a suitable planning and scheduling software can be suggested. The dyeing and looming processes are the true bottlenecks in the entire production cycle of all textile plants. Both dyeing and looming have high setup time, high production time, and high change overtime, but looms are far slower than dyeing machines. Looming is more like a warehouse with a lot of WIP inventory called grey stock and this grey stock is on many looming machines, in small quantities. Dyeing machines, however produce long sets of warp (dyed yarn). One set of warp can be produced by one dyeing machine in one day but the warp can only be consumed by at least 50 looming machines in one day. To keep the ability to produce many kinds of fabric, the manufacturers generally install many kinds of looming machines. All of these looms are fed by only 2 to 5 dyeing machines. Due to these factors the dyeing area is always hard-pressed to feed the looms with small lengths and different types of dyed yarn for the next work orders in line.

So dyeing machines are better suited to produce big quantities of dyed yarn of the same type, (e.g. same color and same number of ends). For example, if the ordered length of fabric is 25,000 meters and the order has been broken into 10 work orders at 10 looms, then it will take 5 days to finish the WIP at looming. This is if all the work orders are done simultaneously and speed of looms are 500 meters of fabric woven per day. A single dyeing machine will produce 25,000 meters of warp in half a day.


These challenges in the textile industry can be met by conducting a profitable to promise analysis; grouping, breaking, and sequencing orders; and by routing WIPs. In the textile industry, orders are considered more like combinatorial meters rather than individual order meters, so the same type of orders can be grouped and sequenced to achieve production efficiency as well as reduce inventory creation. All WIPs can also considered the same way for the same purpose.

Profitable to Promise Analysis

Businesses in the textile industry mostly gets varied orders in terms of rate per meter, quantity, fabric type etc. Because of this, each order has to be evaluated on profitability, customer service levels and long and short-term goals of the company. Profitable to promise analysis allows the business to find out if the particular order will be profitable to make by considering the costs of raw material, process, inventory, and other factors against the price the customer is willing to pay. Thus it can be seen that some orders will be a lot more profitable than other orders. This analysis is perfectly possible if you have the right software tool, which can provide you with this kind of information.

If raw material availability, machine capacity, and production lead time are known at the time of order taking, then it is possible to give a definite delivery date to the customer. This is known as capable to promise. If we can also provide information about customer, production, inventory, stock out, material, and other overhead costs down to the item level, and then compare all incurred costs to the selling price, it will be possible to decide whether the incoming order should be taken and what priority it can be assigned, at the time the order is being taken. This functionality is very important for the textile industry.

In conjunction with above mentioned factors, a planning system that is also capable of grouping, breaking, and sequencing orders while it is doing total lead time calculations to determine a delivery date will solve many production planning problems. It will eliminate waste, reduce the generation of extra inventory, increase machine capacity utilization, increase customer service levels, eliminate stock out costs, and reduce production costs.

Grouping, Breaking, and Sequencing Orders

Grouping, breaking, and sequencing orders will also help to overcome textile production challenges. Group smaller orders at dyeing process. The same dyeing WIPs can be grouped so the generation of extra meters can be minimized. Break bigger orders into many smaller orders at dyeing, and sequence them with other orders. Loom areas typically have many kinds of loom machines, which can produce different kinds of fabric but at very slow rates. If big orders of same material are continuously coming from dyeing, they will only go to a particular loom machine which can process it; other loom machines which cannot use these warps will go idle for want of material. Another way to minimize set up time is to sequence WIP orders with the same color at the dyeing process. This will minimize the set up time to change of color. Also, breaking individual orders into many parts will create many work orders for the same order at looming process. This will minimize lead times significantly at looming.

Also, look for already existing inventory in the form of extra meters at looms and finished stock in the inventory to allocate these meters against the matched fresh orders and plan for the remaining meters.

Route WIPs

By appropriately routing WIPs at the looms, setup times will be significantly reduced. Route WIPs at the looms where the same weaving style is set for the earlier work order and where the same number of ends (number of yarn threads which go inside the fabric to form its width) is set for an earlier work order, otherwise manually knotting ends may take up to ten hours. Also route WIPS at looms where the same weft (yarn threads which go in the fabric vertical to the warp direction) is set for the earlier work order.

Optimization Engine to your rescue:

Optimization Engine from Infynita Inc. Technology is a readymade tool for the textile industry's needs of production planning, scheduling & optimization. It can not only help you achieve the goals mentioned above, it will help you to reduce your manufacturing lead times substantially. Using Optimization Engine you can do scenario analysis, profitability analysis, product mix analysis, achieve available to promise functionality and many more benefits.

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