Traditionally, apparel chains work in response to the orders from distributors which are based on the forecasts. In a dynamic industry like apparel industry, it is impossible to accurately forecast the volumes and the product mix. This can result in high costs of stockout and carrying costs. Besides, forecasts in advance to the order of six months may not be able to judge exactly the customer expectations. Another important point is that the individual efficiencies in the systems don’t add up to overall efficiencies of the entire value chain. These considerations across the textile apparel industry gave rise to the concept of Quick Response system.
The adoption of QR requires major changes in the manufacturing planning and control (MPC) systems. Firstly, every player in the chain needs to have an information system. Secondly, computer based systems are to be used in an integrated manner to accelerate planning and to support manufacturing and distribution along the chain. New packages with better forecasting models, frequent re-planning, precise shop floor control and technologies like CAD and CAE, integrating design and manufacturing have to be used to build up better QR systems. The use of FMS (Flexible Manufacturing Systems) is necessary for Quick Response. Modular type production and Unitary production systems are some of the flexible production systems which can be used.
An example of such intelligent systems is a fabric measurement system that measures the genetic fingerprint (tensile, shear hysteresis, bending, thickness, compression and surface properties), tailorability prediction system modeling the interaction of fabric with machinery, intelligent sewing machines capable of making optimal adjustments depending on the fabric being stitched and self learning systems monitoring and controlling quality.
Intelligent sewing machines are an integral part of intelligent textile environment. Traditionally, mechanic uses his judgment to make sure that the thread tension and feeding pressure during stitching are optimum. Intelligent sewing machines use actuating motors driven by fuzzy-neural models. The control model based on fuzzy-neural algorithms can optimize dynamically the mechanical settings of the two most widely used sewing machines.
Levis Strauss is one of the earliest examples of application of Quick Response concepts in the apparel business. The Head Office of the organization is located in San Francisco. All major design and marketing functions are carried out from this office. The country has its sales spread across the globe with Europe contributing around 30% of the total sales. The production plants are spread across Europe and all the regional sales offices are engaged in recording the sales data. In such a widespread situation, the possibility to communicate between the various units was one of the priority requirements in the definition of the information systems. This vital need has made Levi a pioneering company in the area of Electronic Data Interface. The system helped Levi Strauss to be aware of the changes in the apparel business and incorporate changes in fabric and designs as and when required. Studies conducted by ICRIER in 1993 showed that productivity of Indian apparel industry lagged far behind that of developed nations. Analysis of the reasons affecting the productivity found that technology, workforce management, quality standards, labour relations and management involvement were prime reasons.
Quick Response system can thus be seen as an extension of JIT philosophy where the entire value chain is involved for combined efficiency and benefits rather than the benefits for individual players. QR reduces the lead time across the value chain and hence reduces the risks as the decisions can be taken much closer to the actual sales event. The information exchange makes the forecasts better and the risks are shared across the different players in the value chain.
Ronjay Chakraborty is a PGP22 student of IIML, specializing in Marketing & Operations