Better Decisions for New Item Launches Using Predictive Tracking

By Gallucci, John | The Journal of Business Forecasting, Winter 2014 | Go to article overview

Better Decisions for New Item Launches Using Predictive Tracking


Gallucci, John, The Journal of Business Forecasting


EXECUTIVE SUMMARY | The variability and high failure rates of new item launches are often drivers of service issues, unfavorable cost variances, and excess inventories. Combining Predictive Analytics with a rigorous tracking and decision-making process (Predictive Tracking) can minimize these risks and ensure the proper balance of demand and supply. This process begins with a commitment that Demand Planning must be responsible for service, cost, and inventory associated with the launch, and it requires a high-performing relationship with other functions. It is centered on the concept that analytics and communication tactics must be well organized, forcing discussions within weeks of a new item launch that are traditionally completed several months after, when it is too late to take action.

One of the most alarming discussions that I had with some peers recently is that the concept of accountability is becoming cliché. I cannot recall how a few of us arrived on the topic during the IBF Orlando Conference, but the conversation was memorable. There was a consensus that most functional areas do not want to be associated with failure, whether it is tied to new product launches, service issues, excess inventories, costs, etc. "Deflection" was a word that was used several times, and it can be defined as an antonym for accountability. This situation is likely not news to anyone, but when I heard it in the context of creating a forecast, a document that will always be incorrect by some margin, it was of particular interest to me. Accountability for the forecast is a critical facet of Planning. It is the primary driver of an efficient and lean supply chain. Under-performing results should never be deflected, in the same manner that solid predictions that drive business performance should always be rewarded.

We can all agree that new business challenges arise every day. In Planning, most of us are paid to predict these issues before they occur, and then to engage them so there is little impact to the company's results. Some experts refer to this as scenario planning and others as a component of risk management. Whatever you call it, the bottom line is that any activity where you are predicting a future event falls into the category of forecasting. In Planning, we forecast volumes all the time through the Demand Planning discipline. However, the forecasting of potential business complications requires another level of analytics and action. In my experience, the ability to do this well is what separates great planning organizations from good ones. Execution at this level comes back to one word, accountability. You may need to take a step back to see that relationship, but it is there.

If you read my article in the Fall JBF 2014 issue, titled,"Is Communication More Important Than Accuracy In Demand Planning?," then you already know that I hold Demand Planning accountable for more than just the volume forecast. A large part of that article talked about the need to provide relevant context, and clear communications of what likely scenarios may occur above or below the numbers that we are passing along to other functions.

New item forecasting is an area where this context is critical. I have successfully deployed a process that uses Predictive Analytics to help with new item postlaunch tracking. I have shortened the term to "Predictive Tracking." My definition of success for this process is:

* No service issues

* Achieving cost targets

* Minimal excessive inventories, even for unique components of a failed launch.

It sounds far-fetched to achieve these results given the variability of a new item launch. However, this is exactly why Predictive Tracking is so effective. It is focused on minimizing variability by examining and forecasting the behaviors of critical assumptions. If you are interested in achieving these results, then read on, and I will outline the process for you. However, please be cautioned that you will need a few things before you get started:

* Predictive data sets for analytics. …

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