I received a press release earlier this month from AspenTech (an ARC client) claiming that its customer Samsung Total Petrochemicals expects more than $5 million in annual savings after implementing one of its solutions. This claim caught my eye not only because of the size of the savings, but also because it’s rare for supply chain software vendors to make specific savings claims in their press releases, especially savings of this size.
(Samsung Total has deployed aspenONE® Planning and Scheduling at its Olefins and Aromatics plants in Daesan, Korea).
See if you can understand the following sentence from AspenTech’s product brochure: “aspenONE Planning & Scheduling for Olefins allows olefins producers to select optimal feedstocks based on operational conditions and demands.”
If you don’t speak “petrochemicals,” a translation is needed. Fortunately, ARC does a fair bit of business in these verticals and I asked my colleague Dick Hill to help me translate.
What this basically means is that a refiner typically has various customers demanding products of specific grades and in specific quantities. Meanwhile, a refiner’s raw materials, which are semi-refined products that come out of an upstream plant, also vary in composition. One type of feedstock might be better suited for a particular type of product, but if there is no demand for that product a refiner may choose to make a different one even though the feedstock material is not optimal for that production process. Basically, the AspenTech solution is a supply/demand matching application that helps a refiner purchase the best feedstocks based on demand, lead times, its specific production processes and operational constraints, its current feedstock inventories, and changing feedstock prices.
In short, production optimization in the heavy process industries is much more complex than it is for discrete manufacturing industries.
According to the press release, here are the areas where Samsung Total expects to achieve its savings:
- Better inventory visibility on feed stocks and final products;
- Development of ‘what if’ scenarios for various business decisions and their impending production outcomes;
- Reduced gaps between optimal planning targets and actual outcomes;
- Improved feedback loops on plan versus schedule versus actual.
I translate this to mean that Samsung Total can reduce production costs by better matching demand with supply (feedstocks) and with the strengths of its particular production process. And it looks like part of the payback will be in the form of data that allows for better continuous improvement programs.
The press release includes a supporting quote from Mr. Young-In Yoon, VP of Base Chemical Production Business Unit at Samsung Total Petrochemical, which references the $5 million in expected savings. This is probably a conservative estimate. I know that if I were making a public statement about what savings I was expecting, I’d be very conservative.
I’d love to see other supply chain planning and execution vendors make these kinds of specific savings claims backed with appropriate customer references. But I also know that getting customers to make public statements about ROI is not easy.
However, even if other SCM vendors do make these kinds of specific claims, the savings probably won’t be as large for a couple of reasons: First, the AspenTech solution is sold in a software-as-a-service model, which means the initial outlay is much lower than the traditional software license model, which allows for a quicker payback period. Second, refineries are massively expensive, often costing over a billion dollars to build, and they often have very large throughputs. Savings from better decision making in a big refinery scales in a way that is just much, much greater than what is possible in smaller types of plants.