Why is it So Hard to Realize the Business Benefits of Supply Chain Planning?
A Three-Part Series on the Characteristics, Pitfalls and Lessons Learned from Project Implementation
By Tom Chason, Principal SCMO2
Over the span of my career I have witnessed many supply chain planning software implementations that have not lived up to expectations or delivered the anticipated business benefits. For some of these efforts I was part of the team (in my younger days!), on some I was brought in to provide a fix and some of the failures are so epic they make the rounds at conferences and are legends in their own right. So I often ask myself, what is it about supply chain planning that makes it so hard to successfully model?
When you really think about it, you come to realize there are actually are quite a few characteristics unique to supply chain planning that contribute to the level of difficulty in realizing the advertised business benefits. Let’s face it, these packages and installations require whole teams of people for good reason.
In this post, I highlight those specific characteristics and emphasize the resources required to effectively overcome these high hurdles. In a follow up post to this one, we will review the specific pitfalls associated with supply chain planning projects that arise as a result of these unique process characteristics. And in a final post, I will share the lessons we can learn from those pitfalls and the steps we can take to avoid them in the first place.
So, why is it so hard to realize the business benefits of supply chain planning?
The first answer is actually rather obvious: because this involves complex business processes that require the technical skills of specialists. Forecasting is aggregated statistical modeling, and statistical modeling requires effective algorithms. Those algorithms require optimization and active measurement for effective network planning. And that planning needs stochastic modeling to effectively ensure a safety stock of inventory to prevent an item from running out. All these calculations are for naught without an ongoing method of collecting and inputting reliable data.
And that’s the second reason this is so hard. Because large amounts of transactional and master data are required as inputs for planning, there are multiple integration points that need to be managed across multiple systems. This geometrically increases the complexity of the implementation. Forecasting algorithms require execution systems and reporting systems to gather and communicate internally. Vendor systems and customer systems must be integrated to keep products flowing into and out of the operation. Because many of these sites are geographically diverse, and each may have a different data management system, the technical integration of all this infrastructure is hugely challenging.
Finally, planning processes can change day to day depending on the particular event du jour. These are not static systems (we most certainly do not want them to be!). It is challenging to completely document, test and train for every process variation that may come up during the course of a planner’s day; however, most training curricula are still budgeted and designed like their execution system counterparts. In a “live” situation, exceptions and anomalies appear frequently, at best causing delays, but at worst, generating false outcomes.
The takeaway: Supply chain planning systems are inherently complex, governing hyper-dynamic processes that require vast amounts of data from many various points of generation. Without a clear strategy and unified implementation effort accounting for its tendencies, it’s easy to understand how a solution can go awry.
In my next post, we will review the common pitfalls associated with these projects. It is surprisingly easy to fit them into a few definable categories.