CIOIN

N Kailasanathan

VP & CIO, TITAN

Executive summary

How would Titan know which market would place demands for which specific type of watch, which manufacturing unit should cater to which retailer? And exactly how many watches will he need? Till now the answers lay in  a gut-feel based, inaccurate and feeble  demand forecasting system.   Read how Titan used APS to get back on track.

Titan was established in 1984 as a joint venture between the Tata Group and the Tamil Nadu Industrial Development Corporation. It manufactures over nine million watches and today has a customer base of over 80 million. Its watches are sold through six different market channels including - an international market, 240 World of Titan showrooms, over a hundred Time Zone outlets and more than 12,000 retail outlets.

Titan needed a system that would - even with expansion  - produce a forecast that would be instrumental in directing the company through a minefield of capacity constraints, multiple sales geographies and a multi-tier distribution channel. 

"Co-ordinating between the supply chain and distribution has been one of the biggest challenges of Titan. What we needed was a good forecasting system, which could predict sales across the country, given the complexity of new models, promotions, and other constraints," says N. Kailasanathan, VP and CIO, Titan.

On the supply side, staff would work out a supply forecast based on current stock, and likely sales over the next two months, and then plan for production. 

"All of this was done on manual worksheets, which were sent to the factory where weekly feeds were given for production plan. There was no scientific basis for any of this." says J. Murali, group manager, supply chain and logistics at Titan. 

This paved the way for mismatched inventory. The production alignment ratio hovered around 70 percent - which meant that on an average 30 percent of Titan's inventory was not being used.

To get started Titan zeroed in on an Advance Planning System (APS) which could deliver forecasts that took into account resource constraints like plant and supply capacity. Titan decided to go with APO for functional reasons as SAP was an advantage because they already had a stabilized SAP ERP backend. 

The implementation was no less than a Herculean task. It was plagued by problems. Getting data from 200 retail stores and about 100 redistribution stockists on time was a challenge. Apart from that, they were impeded by hardware limitations.

To tame that bull, the vendor suggested that Titan move up from 32bit to 64bit. But slowly, the primary reason for deploying the APS was fading: checking forecast errors.  "APS' are not like ordinary implementations. They have fewer users - most with a stake in the business. So they themselves became anchors and started using the system. It took some persuasion to get users to accept the optimizer figures, but once we showed them how accurate they were, everyone came around. 

The project brought with it a bundle of perks for Titan. The new system provided a streamlined demand planning process. 

Importantly, the system allowed Titan to create semi-finished watches (in the first stages of production most watches look the same) and wait for a confirmed forecast to produce finished goods. This strategy of postponement improved Titan's market response time and ensured that they made fewer 'non-sellable' watches. 

The system also took advantage of the deployment optimizer, which allowed Titan to distribute finished products from its five assembly plants on an optimal basis. The system also significantly reduced production planning time from a week to three days. The number of manufacturing units has also increased from two to five, and additional complexities that have cropped up have been effectively taken care of by the new system