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The Limitations of Traditional Supply Chain Planning Systems

By Toby Brzoznowski  February 2, 2017

Every large global company has implemented enterprise planning systems to help keep their supply chains running, and research shows that billions of dollars are spent each year on upgrades and new implementations of these planning systems.

So why does nearly every company still do much of their actual “planning” using offline spreadsheets or rely on small groups of data scientists to provide critical insight and business analysis?  Shouldn’t this be addressed by the expensive enterprise planning systems?

The unfortunate reality is that traditional planning systems all fail to properly address three key requirements of today’s business, which can be categorized as Adaptability, Agility, and Advanced Analytics.


Adaptability is the requirement to accurately represent unique business processes that many companies have developed and rely on to achieve their competitive advantage. This could be in the form of a special way to manufacture goods, or a strategy for smartly sourcing materials, or perhaps a unique method for servicing customer orders. These business processes are often a company’s “secret sauce”.

The problem is that large enterprise planning systems are designed to support the masses and often that means that the key custom requirements cannot be accurately configured, forcing difficult work-arounds or solutions completely outside the core planning system (aka – spreadsheets).


Responsiveness is the requirement to quickly react to changing market conditions or changes to the core business model. Volatility and change are everywhere and force supply chain managers to try and hit a continuously moving target. Change can include external factors like new taxes, regulations, shifts in demand, or evolving customer service expectations. Change can also include internal shifts such as mergers or acquisitions, new product introductions, or entrance into the e-commerce game.

The problem is that large enterprise planning systems are overwhelmingly complex to set up, often taking years to configure and fully deploy throughout the company. These systems cannot be easily or quickly re-configured to keep up with every change happening in the business, which again leads to the reliance on offline spreadsheets and more flexible third-party tools.

Advanced Analytics

Advanced analytics is the requirement to leverage available data to identify opportunities for improvement and optimization using complex algorithms and cutting edge solver technologies. Most enterprise planning systems just do not incorporate these algorithmic technologies, which means that companies have been forced to employ teams of data scientists using special tools to attain that next level of process optimization.

The problem is that good data scientists are in short supply and high demand, and the tools they use are not designed to be accessed and deployed to business users throughout the company. This makes it difficult to scale the solution throughout the enterprise, and even more difficult to sustain as a long-term business solution.

Now the good news —- Many innovative companies are taking action to turn these system limitations into strengths and competitive differentiators through a process we call Planning by Design. Planning by Design incorporates and integrates design-based models (which are inherently adaptable and agile and include a library of advanced analytics algorithms) into a planning process, filling in the gaps left vacant by the enterprise planning applications.

We’ll explore this more in future blogs, but to learn more, register for our February 9 webcast, “An Emerging Imperative – The Supply Chain Analytics & Planning Platform.” In this webcast, Gartner’s Vice President and Distinguished Analyst Noha Tohamy (along with some commentary from me) explains how leaders are leveraging a new class of technology known as “Analytics Platforms” which are emerging as the bridge between enterprise planning systems and advance analytics tools.