Enterprise Demand Sensing in the Automotive Industry

By Truss, Lynn T.; Wu, Peiling et al. | The Journal of Business Forecasting, Fall 2006 | Go to article overview

Enterprise Demand Sensing in the Automotive Industry


Truss, Lynn T., Wu, Peiling, Saroop, Atul, Sehgal, Satish Kumar, The Journal of Business Forecasting


GM's research project investigates ways to quickly sense customer demand and ways to respond to it ... new analytical methodologies, IT framework, and collaborative decision-making processes are being developed to better match demand with supply ... the CPFR (collaborative planning, forecasting and replenishment) program is being adopted by the auto industry.

General Motors Corporation (GM) is the world's largest automaker and has been the global industry sales leader since 1931. Founded in 1908, GM employs about 317,000 people around the world. We have manufacturing operations in 32 countries, and our vehicles are sold in 200 countries. In 2004, GM sold nearly 9 million cars and trucks globally, up 4 percent from the previous year, which is the second-highest total in the company's history. The General Motors extended enterprise, which includes suppliers, dealers, and logistics providers, is a large, complex network. The automotive industry in general has unique and significant challenges in sensing and responding to customer demand. Customer preferences change rapidly and products are complex with respect to numerous option configurations and long lead-times. Customers have many touch points (Internet, dealerships, etc.), and the associated databases are large. As a part of GM's central R&D organization (the world's first automotive research organization), we have developed an Enterprise Demand Sensing Research Program to investigate and innovate analytical methodologies, as well as drive a collaborative decision-making framework, integrated with our business processes. We focus on demand sensing for vehicles plus accessories and service parts, with many application areas and numerous challenges. Our research program supplements our internal research staff with external research colleagues and partners from universities and companies who collaborate with us to develop new methodologies and prototype solutions that we can test and evaluate in our business. This collaboration model provides exciting opportunities and great benefits to us and our partners.

DEMAND SENSING BUSINESS OBJECTIVES

As the central R&D organization for General Motors, our research program must develop and model innovative systems/solutions for our internal business customers. We begin by identifying the key challenges for our business analysts and ensuring that our deliverables address those challenges. At a high level, Enterprise Demand Sensing must help our organization to have the Right Product at the Right Place at the Right Time. We focus on sensing demand for our vehicles as well as the demand for specific options and accessories that go on those vehicles. With proper demand sensing, we'll have the right parts at the assembly plants and the right vehicles at the dealerships, ready and waiting for our customers. Of course, timing is everything. We cannot afford to miss trends or to have the wrong colors and option configurations on the dealer's lot. The customer shouldn't have to "settle" for something or compromise on their favorite color, for example. With improved sensing technologies, we can improve the mix of vehicle configurations that we build and distribute to our dealers. Bottom line: our business objectives are to increase customer satisfaction and customer enthusiasm, improve our market share, and increase our sales and profitability.

SUPPLY CHAIN CHALLENGES

In order to impact the above business objectives, we need to find ways to address significant supply chain challenges on both the demand and the supply sides of the spectrum. On the demand side, there has been a tremendous increase in competition, product life cycles are getting shorter and shorter, and customer's taste is constantly changing; all of this leads to high volatility in demand. It is difficult to predict customer preferences and trends, and it is even more challenging to identify and acquire relevant demand signals that are both accurate and timely. …

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