Comments on: What happened to CPFR?

By: student

student — Tue, 21 Feb 2012 08:23:36 +0000

currently I start to write my undergraduate thesis. my topic is related to applying CPFR in FMCG company. those reasons in this article are make sense to judge CPFR cannot work perfectly. so which strategy will make sense?

By: Logistics Guy

Logistics Guy — Tue, 20 Sep 2011 04:40:51 +0000

I think if you work in non-retail industry, you may not need software to help with CPFR. Because, the benefit of joint business planning already is already good for CPFR implementation.

By: Duncan Klett

Duncan Klett — Fri, 01 Apr 2011 17:16:42 +0000

When I did a deep look at CPFR several years ago, it included a number of pre-defined, rigid, processes. These reminded me of Rosetta Net – defined by a committee to cover all possible cases. The result is a system requiring so much data, and so complex, that it becomes extremely difficult to implement, confusing to users, and ultimately falls out of favor.

On the other hand, I know of several major electronics companies who have implemented the CPFR principles: namely capture forecast (their own to suppliers or from their major customers), review changes from the previous cycle, firm up on a common view of the demand.

Then, if getting demand from customers, they test/simulate the capability of their supply chain to satisfy that demand, resulting in a committment back to their customers (which could result in an adjustment to the demand the customer plans to place on them).

When communicating with suppliers, the objective is to obtain a committed supply chain back from the supplier, which is then used to feed committments back up the chain.

The point is, not only CAN it be done, but that it IS being done with EXCELLENT results. However, the process has been simplified from formal CPFR into something that can be implemented and used.

By: supply chain

supply chain — Thu, 27 Jan 2011 05:14:19 +0000

I strongly agreed with this article. It seems to be that CPFR requires good software system otherwise it’s difficult to manage too many SKU involved. Relationship between manufacturer and retailer is also another issue.

By: Charles Chase

Charles Chase — Wed, 08 Dec 2010 20:12:58 +0000

I agree completely with your comments regarding CPFR.
Like most initiatives, and/or new thinking (concepts) people take them too literal. They are too black/white in their thinking and implementation approach. They attempt to apply the process across all their products, and if it does work, then they abandon the process completely.
Instead of segmenting their product portfolio and applying different processes, methods, and skills to each segment they apply one process and methodology across all products. Companies need to focus on products that have high growth and profit potential (usually 20% or less of their product portfolio). Utilize data, domain knowledge (collaboration), skills, and more sophisticated technology to facilitate and improve their ability to predict and replenish those growth products more efficiently. Usually, those same growth products have the most data and information, but also have highly volatile demand (due to competitive activities and market dynamics) requiring the most collaboration and analytics.
Most technology today can automatically forecast demand for the remainder of a company’s product portfolio due to the stability of demand. Those same products are normally harvest brands that are mature and have very little sales/marketing focus. However, they do have strong trends and seasonality. With the advancement in technology harvest brands can be forecasted rather easily, and can be automated on a large scale. The majority of most companies product portfolios consist of harvest brands. Those brands are the anchors that sustain the company’s revenue base that fund and support the growth brands.
I agree that collaboration is a key success factor in any demand forecasting process whether it be CPFR, or S&OP. However, you need two key ingredients to enable collaboration, 1) trust among the participants, and 2) trust in the analytics that support collaboration. There are two levels of trust. First there needs to be trust between the CPG Manufacturer and the Retail, but there also needs to be trust among the internal participants in the demand forecasting and consensus processes. The best way to gain that trust is by aligning initiatives and performance rewards. Second, everyone in the process must trust the analytics and supporting technology because without the analytics you will make the wrong decisions, and without the technology you cannot support the process across an entire product portfolio. Most companies focus far too much on the process, and shy away from the analytics blaming the technology for the demise of the process. As a result, with the absence of analytics and technology many companies fall back to Excel and human judgment both of which are not scalable.
The good news, the data, analytics and technology have all caught-up with the process.