How IT helped create the world’s cheapest car

Two kilos. That’s how much Tata Motors shaved off the weight of the Nano by using tires without tubes.

When Tata Motors recently created history by launching its people’s car, the Nano, the media covered almost every innovation that helped make it special. Reams were written on the price difference, which is certainly astounding: the Nano costs less than half of its closest price competitor. The Nano’s engine size, where it was placed, how it was placed were all talked about. Every conceivable story angle was covered, except how IT helped bring the Nano’s price tag to about $2,554.

It isn’t a mean amount. IT helped with a 20 percent reduction in the cost of manufacturing planning process.

And IT offers the same cost efficiencies to many vehicles in the Tata Motors stable.

But that’s not all. Piggybacking on digital manufacturing, the $9.07 billion Tata Motors has been able to refine its innovative ‘design to manufacturing’ approach. Digital manufacturing not only introduced Tata Motors to ‘concurrent engineering’ but also gave its assembly lines the ability to adapt to multiple automobile variants. It permitted Tata Motors to churn out a wide spectrum of vehicles not only at lower costs but also faster too.

Here’s a rundown on Tata Motor’s version of digital manufacturing (DM) that gives its plants control and flexibility, whether they produce a heavy commercial vehicles or passenger cars like the Nano.

Enterprise in First GearTata Motors was introduced to DM about three years ago. Compared to other IT initiatives at the organization, DM’s the new kid on the block.

Tata Motors graduated from a 10-year-old bouquet of function-specific apps to a centralized ERP & CRM platform. It also worked a 15-year-old implementation of homegrown product lifecycle management (PLM) applications before it adopted a sophisticated suite of PLM solutions. IT was an old hand around Tata Motors before DM was identified as a way to make manufacturing processes more efficient.

But what really gave DM its push is how it shortened time-to-market.

“For an automotive company, a key competitive advantage is its ability to introduce new products in a very short span of time. Compressing time-to-market is a function of effective and efficient plant design and manufacturing processes. We realized that the design of our existing manufacturing process was slowing us down,” recalls Probir Mitra, senior GM-IT at Tata Motors.

Mitra is referring to practices that involved manual effort to conceptualize, create and maintain processes, plants and product designs. Manual meant that the organization faced difficulties as it figured out the right tools to be used in a new plant. Even in existing plants, manual processes resulted in longer lead times to choose the appropriate tools for an operation and create programs for assembly line robots. Creating such robot programs manually was time-consuming and error prone.

All this delay was costing business. Longer planning meant data used to plan a vehicle became useless over time. Or when Tata Motors tried to schedule the assembly of two different products on the same line, there was no clear understanding of the bottlenecks that would crop up. The absence of tools to do ‘what-if’ analysis slowed the enterprise down if it introduced a change in the product mix or it rolled out a new product on an existing line.

Take for example, a plant that rolls out 500 vehicles a day. What happens when — anticipating increased market demand — the organization decides to increase production to 750 a day? Will the plant be able to scale up?

Each facility in the plant is created with a certain throughput including specific turn-around-time and capacity, says Mitra. “When you scale up, the sum totality of increased manpower and the entire infrastructure needs to be considered,” he says.

Digitally simulating the factory with DM allows the organization to conceptualize what is needed for a plant to produce 750 vehicles a day. And it’s particularly helpful when the organization wants to plan for a 5,00,000-car facility, but only wants to invest in a 1,00,000-cars set up at the start.

“There was little clear understanding of the overall plant design, especially the interaction of different components in the assembly line for the smooth functioning of the plant,” recalls Mitra.

Planning on Cruise ControlThat’s when Mitra and his team decided to turn to Dassault System’s Digital Enterprise Lean Manufacturing Interactive Application (DELMIA) solution for digital manufacturing. The DELMIA rollout, which began in July 2005, was completed in four months. It was first deployed for Tata Motor’s sub-one ton truck called Ace. It was then graduated to a new version of the Indica, and then to the Nano.

DM automates processes in product design and production engineering planning. It not only helps plan manufacturing processes and design a plant’s layout, but also virtually simulates the repercussions of those plans.

Digital manufacturing covers many areas including product design, plant layout, time measurement, process planning, ergonomics, robotics and simulation of the whole plant. It’s output is taken for process documentation, which is considered as a manufacturing blueprint. DM integrates with SAP to provide process-timing data which is used to cost out a product.

Data flows from the DM to ERP, which costs out a product, an assembly or a sub-assembly. “Or, when you produce items in sequence, you have to book your production into the system. When you do this, the system knows how many hours of efforts will be consumed. There is a seamless integration between various systems,” says Mitra.

He adds that with DM, planners can also simulate the movements — and fatigue levels — of people working on the shop floor. Because of the repetitive nature of assembly-line work, a badly-created process can impact efficiency.

“It costs a lot of money to make a physical prototype. And, after you’ve made it, you might find that changes need to be made. In physical world, the costs of making those changes could prove quite expensive, particularly if your facilities are very large,” Mitra points out.

T.N. Umamaheshwaran, CTO (engineering automation), Tata Technologies and the person who headed the DM program at Tata Motors, agrees. “Using DM, on simulated basis, the costs of making such changes are much lower. You can actually see and walkthrough your facility even before you have laid the first foundation stone. You can simulate all the operations even before the first of machines are installed,” he says.

“DM is a means of time travel. We can’t imagine what would take place at a new plant, if we did not have DM tools. Two years before the first stone of a plant is laid, we already start working on it. We don’t even know where the site will be, but we know what it will take to make 750 cars a day. That’s the kind of simulation that gives management a comfort level,” he says.

The solution is also easing retrofit pains at the assembly-line level. “The new Indica project is seeing a lot of benefits from DM. Earlier, only after we had made prototypes did we realize that we had overlooked some practical problems. Maybe it was an operator who could not reach or see a part comfortably while assembling. But it was too late to do anything then. Now, we are able to figure out such problems at the desktop and take corrective measures,” says Nitin Rajurkar, GM (technology & production services) at the passenger car business unit of Tata Motors.

Umamaheshwaran adds that simulation movies are filmed for review by different teams. Each team appends their comments according to their area of expertise. “To test a new product we need to make a large number of prototypes. That’s like a mini-factory. The number of tests is huge, as we need to go to multiple target markets and need to comply with several regulations,” he points out.

But Tata Motors didn’t invest Rs 6 crore in DELMIA just to create prototypes.

More Maneuverability DM can do more. It can also optimize an existing factory layout by adopting new manufacturing techniques and technologies. And it can simulate a change of product on an existing line.

“Take the Ace product for example. On the same Ace line, we want to release a new one-ton Ace, which means that the line has to adapt to a new product. Or take the Indica’s plant. On the same assembly line, the weld lines are different for different variants of the Indica because we bring out an old Indica and the new Indica on the same line,” says Umamaheshwaran. “Within the Indica platform, we have the Indica hatchback, the Indigo sedan and the Indigo Marina. Variety management is important,” he adds.

DM has a solution to this. It allows ‘what-if’ analysis and can create different scenarios. And it offers more. Umamaheshwaran underscores the importance of DM for a tighter supply chain and logistics.

Say a car’s dashboard is outsourced, he points out. DM simulation can help Tata Motors understand how modular the entire instrumentation panel is. With the help of DM’s simulation of a complex process, process planners and decision-makers realized how much the organization could save in inventory space and reduced turn-around-times by outsourcing the panel.

“In the old days, people planned for manufacturing facilities with very long time horizons. It was something that was created and then forgotten about it except for minor changes — until you got a new platform,” says Mitra. “Today, change is constant. Frequent changes are in much higher demand now with so many new products and variants being brought out. With DM, the focus is not on building a one-time infrastructure. The focus is on the flexibility; to be able to constantly change with new requirements, especially if changes to process can cut costs.”

And DM is helping do exactly that. By automating process planning and engineering, Tata Motors has taken a sickle to costs and time-to-market. The ability to simulate facilities and processes has reduced the cost of physical rework, which translates to lower TCO. There has been a 30 percent reduction in manufacturing and facilities planning timelines, with a 20 percent reduction in cost of the manufacturing planning process.

“In fact, the time to design an entire process end-to-end has — for certain functions — reduced by over 50 percent. In terms of costs, certain functions have managed to cost less by 50 percent,” says Mitra.

Other advantages associated with DM reflect the non-stop nature of manufacturing. “You can’t afford to shut down for five days, make your changes and come back in. You need to have information so that you can simulate, work out the smallest window of downtime and execute,” asserts Mitra.

“Earlier, we used to have a separate cell to take care of robot programming,” says Umamaheshwaran. “Programming was done while the robots were online and this caused downtime. So, we were forced to make changes during the Diwali holidays or during a planned shutdown. Now, we don’t have to wait. A lot of robot programming to adapt it to new products happens offline,” he says.

The result? There has been a reduction in time-to-market for passenger cars by at least six months. DM has helped quickly identify areas of ‘work overload’ and constraints while balancing lines to adapt to different vehicle variants.

And the business can’t get enough. “At the Pune plant, our facilities were designed around the Indica. But today if I want to introduce a new product into the current facility, the designers have to work around the facility and not the product,” says Rajurkar. “Here’s where the difference lies. They have to design with constraints. DM helps them a lot here. We are now able to predict bottlenecks, biomechanical problems and other constraints much more in advance — avoiding a lot of rework. This helps keep the car’s cost down, because reworking problems late in the day is expensive.”

Riding Top DownRajurkar says that DM doubles up as an excellent tool to help justify investments in capital-intensive equipment and technologies to senior management. “Earlier we used to implement something without understanding the whole picture. Because of this we’d run up against un-anticipated problems. Now I can make five different proposals based on simulated scenarios and select the best one,” he says.

“Honestly, our process planning department was more of a process documentation department because it used to document what happened on the shop-floor. Now, the documentation is system generated, and when a planning person is free he uses his time to plan processes,” says Umamaheshwaran.

Going forward, Umamaheshwaran would like to create videos from the simulations, which can be used for operator training. “It’ll give us the ability to make everyone understand what they are supposed to do at every station,” he says.

It will also help get around a language problem at the pan-Indian company. “We do everything in Hindi and when go elsewhere, we need to translate [training manuals] into the local language. Simulation movies are much easier than making a plethora of translations,” says Umamaheshwaran.

“I can’t say that we are using DM to its full potential,” adds Rajurkar. “I don’t know how DM will perform when multi-model complex operations are to be done at the same facility. We haven’t yet validated that. We want to know how the system will cope with complete line balancing to take care of multiple variants,” says Rajurkar.

“Digital manufacturing helps the teams working on product and process design to work on a common, seamlessly integrated platform. DM has given us tremendous benefits in terms of simulation, concurrent work, product design work and envisaging smaller facilities as a part of the larger footprint we want to have tomorrow,” says Mitra.

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