This blog post is the fourth in a series of posts on Integrated Business Planning. This post also builds on the concepts defined in a previous post done by Carlos Centurion on “Moving Along the Analytics Maturity Curve."
The main point highlighted in Carlos’s blog post was that companies have adopted analytics through a highly IT driven approach, implementing layers of technologies that were then applied to business problems via siloed analytic applications. This is partially the fault of software vendors that were constrained by the limitations of OLAP. But even in the era of Big Data and R, companies are finding it difficult to connect the dots and apply analytics in a way that truly represents the unique interdependencies within their business.
Sales and Operations Planning (S&OP) is a classic example of companies adopting a layer-by-layer approach. It’s one that might result in meeting siloed KPIs in the short-term, but the sequential nature of decision-making ultimately falls short on maximizing shareholder value.
The Glaring Issues with Sequential Planning
Some of the glaring challenges associated with sequential planning are as follows:
- Inability to arrive at an optimal solution due to approximations done at each step of the process.
- Inability to do rapid re-planning due to time taken for each sequential steps.
- Financial considerations are an afterthought or not even a consideration.
- Local functional level optimization trumps corporate strategy and goals.
Why Companies Are Still Stuck Doing It
Sequential planning is not really how the typical business minded supply chain executive thinks. Time and again, we have collectively heard from supply-chain executives that they make decisions based on a global picture, taking into account all of the constraints of their business: people, demand, supply, financials, strategy, regulation, etc. Unfortunately, the way that systems are deployed today does not reflect this global planning approach. There are two main reasons this is still the case.
Never-ending Data
There is no end to the pursuit of complete and clean data. But many IT departments try to come up with the perfect data warehouse before enabling global analytics. This is a waterfall approach and very difficult to realize in practice. The result has been a proliferation of data marts and cubes meant to serve specific business siloes, ultimately fueling sequential planning.
Legacy Technology Limitations
In addition, software vendors have grown out of a sequential planning mindset either due to acquisitions that have happened over a long time or due to and initial niche focus on either supply, demand or finance.
Global Planning has had it’s challenges, too
It has to be accepted, though, that one of the reasons why global planning has not taken off is that global planning solutions have always been developed as tool kits rather than packaged applications, in order to reflect the unique complexity and competitive advantages of every business, even within the same industry.
Companies, or more likely consultants, had to build the data integration and ultimately replicate the business model as part of one off implementations instead of being part of a framework or application. This has been the biggest challenge for lack of traction of global planning tools.
Enter “S&OP Systems of Differentiation,” a Step Towards IBP
If there is a global planning solution that is closer to being a packaged application than a toolkit, then there is significant opportunity for companies to unlock value through global consideration of constraints. Getting 20% of additional data (in addition to what they already have from their S&OP systems of record) could give them 80% or more of value through improved profit margins and revenue.
Gartner has begun to validate this concept through their recent publication of the Magic Quadrant for S&OP Systems of Differentiation. In the next post, we’ll discuss the additional requirements of a System of Differentiation and how it can be leveraged to enable global planning.
