New Product Introduction-Feast or Famine?

By Burnette, Ron | The Journal of Business Forecasting, Winter 2009 | Go to article overview

New Product Introduction-Feast or Famine?


Burnette, Ron, The Journal of Business Forecasting


With new product introductions (NPIs), consumers rejoice and marketing generates the buzz, but supply chain executives shudder. In today's ever-changing economy, one thing is constant - the ever increasing pace of new products hitting the retailer's shelves. As companies struggle to grow market share or, at a minimum, maintain existing share, the introduction of the newest products is a proven method of achieving it.

For consumers, NPIs are exciting, but for manufacturers and retailers it is all about competing for business, and at what cost? In a recent study by AMR Research, which specializes in supply chain practices, 32% of the participants believed that products reaching the market late is the key reason for new product launch failure. Other reasons include product pricing, quality, and missing customer needs.

So how can supply chain executives improve customer satisfaction? In the last few years, a number of innovative techniques have been introduced by leading industry analysts and supply chain solution providers to help manufacturers and retailers improve the process and increase the success of launching new products into the marketplace.

THE CHALLENGING PROCESS FOR LAUNCHING A NEW PRODUCT

There are many challenges that come with launching a new product into the marketplace. Forecasting for NPIs is just one of the big challenges the supply chain team needs to consider. Before addressing how to improve forecast accuracy for NPIs, it is important to look at the overall process of the launch as a whole. Research shows that the launch of new product lines is the #2 enterprise-level event affecting supply chain management. How can you mitigate a NPIs' effect on the supply chain?

It is imperative to have a solid overall process in place to be successful at improving the launch of NPIs. One such process to consider is Design for Six Sigma (DFSS). There are several approaches to DFSS; the most popular is called DMADV, which stands for "Define, Measure, Analyze, Design, and Validate." (See Figure 1)

Phase 1: Define. Define the business case and the opportunity for a new product.

Phase 2: Measure. The needs of the key stakeholders, such as sales, finance, and manufacturing are identified, measured, and developed.

Phase 3: Analyze. This phase consists of system design, and the generation and selection of the best possible concepts. For example, if you are a manufacturer, you need to decide where to manufacture the product and what would be the required for tooling, sourcing of purchase products, etc.

Phase 4: Design. A detailed design for the product is developed and optimized. The robustness of the design ensures minimization of variations in the performance of the product.

Phase 5: Validate. The generated designs are verified and validated using simulations and other approaches.

While these five phases are crucial to the process, let me add a sixth phase that is equally important - General Availability. This is where the supply chain team can verify and validate the forecasts in Phase 1 against consumer demand. As you will see, this is a critical step in ensuring the success of an NPI.

TRADITIONAL APPROACHES TO FORECASTING

When launching NPIs, make sure you have the most accurate forecast. Having this will improve manufacturing efficiencies and ultimately delivery to the end customer. Above all, it will help to prevent disruptions in the supply chain.

Before reviewing the newest techniques for forecasting NPIs, let's take a walk down memory lane. How have companies traditionally created forecasts for NPIs? Basically, they often turned to one of two approaches. The first approach is what I call "tribal knowledge." This method relies on individuals in the demand planning or supply chain organization to identify a "like" product - that is, a product that has similar characteristics to the new product - and then use its demand history as a basis for a new product forecast. …

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