Cost based optimization has proven much more tricky to implement that originally thought.
Optimization: A Solution That Drove and Industry
In its early years, APO was sold on its ability to perform optimization. This is primarily because it was an industry wide practice to market advanced planning software in this way. In fact, APO or Advanced Planner and Optimizer – had the term directly in its name. The term optimization has two meanings as generally used. One is more of a business use, which basically means to produce the best outcomes. However, in the area of operations research, from where supply chain optimization originates, it has a more specific meaning. For those that did not do work in operations research or advanced mathematics, (which is a sizable portion of the business community and the executives who evaluate SCM) its more technical definition is unknown. For this reason we wanted to explicitly define it here.
“In mathematics, linear programming (LP) is a technique for optimization of a linear objective function… Linear programming is a considerable field of optimization for several reasons. Many practical problems in operations research can be expressed as linear programming problems. Certain special cases of linear programming, such as network flow problems and multi commodity problems. Although the modern management issues are ever-changing, most companies would like to maximize profits or minimize costs with limited resources. Therefore, many issues can boil down to linear programming problems.“ – Wikipedia
However, within optimization itself there are any number of types. The optimizer in most APS applications is a cost based optimizer. Costs for being out of stock, for transportation, storage and so on are entered into the system, and the system attempts to meet all demands at the lowest possible cost. However, inventory optimization (deriving stocking position from service level), duration / time optimization and space optimization for truck loading are all different types of optimization in the supply chain. This article deals exclusively with the issues surrounding cost based optimization.
Linear vs. Discrete Optimization
With cost based optimization, the approach works best in situations that are perfectly “linear,“ that is inputs can be increased or decreased in a continuous fashion. An example of a linear input would be an order quantity. Perfect linear optimization would mean that any order quantity from zero to infinity could be placed and fulfilled. In reality, supply chains are not perfectly linear problems. There is most often a lot size is a discrete value which limits the flexibility of the order quantity. One item may be ordered in units of 50. If 135 units are desired and the current inventory is less than 35, then 150 must be ordered to meet this demand. SCM has a number of techniques, such as lot size, than alter the problem being solved from perfectly linear, to discrete, or what is known as a step function. This is very important to making the resulting recommendation realistic. If discrete optimization is not used, then some type of post processing must be performed to ensure that the order quantities meet with the policies of the company and the limitations of the supply chain.
The Decline of Cost Based Optimization
However, while cost based optimization drove development in SCM at one time, it no longer does. The evidence for this is that cost based optimization is an option in three of the older modules (SNP, PP/DS and TP/VS), but is not an option in any of the newer modules (EWM, SNC, EM, SPP and F&R) Furthermore, the core optimization functionality in SCM has been stabilized for some time. Unfortunately SAP offers no other type of optimization other than cost based optimization, and this is a problem because cost based optimization can no longer be considered either a best practice or leading edge. The following reasons explain why.
- Optimization which is more customized for each supply chain area now exists in the products of the best of breed vendors.
- Cost based optimization does not have a sufficient number of success stories to be considered by any but the most risk tolerant companies.
SAP has invested a significant amount of effort behind an optimization method which is now an older and higher risk approach to optimizing the various supply chain areas. This makes the suite very vulnerable to the best of breed vendors currently, but this vulnerability is likely to increase as these distinctions between different optimization types become better known.
What is Actually Running the Solution?
There is a story from the client of an advanced planning vendor that the optimizer engine that was running in the background was not operational for a number of days, and no one noticed. This was because the planning was primarily being performed by heuristics that had been custom coded with scripts and the solution was not using the optimizer at all. Whether the client knew the optimizer was not being used is unknown. This is more common than a reading of press releases and industry periodicals in the area would think. While optimization is what sold a lot of supply chain software, but it was not necessarily what the customers of these solutions went live with. As is evident from our example above, this experience is wider than simply SAP SCM.
Why Cost Based Optimization Did Not Fulfill its Promise
If optimization did not “change the world“ of supply chain, the question naturally becomes “Why not?“ While there are a number of reasons, ranging from implementation complexity to vendor approach, however, what is most likely the dominant reason is that cost based optimization was essentially a first attempt by proponents of optimization to apply the technology to business settings. Cost is only one form of optimization, and in this case, the approach was simply overused. There is a saying that when all you have is a hammer, every problem begins to look like a nail.
Conclusion
Cost based optimization is always the most complex and difficult of implementation, and has demonstrated inferior implementability over other forms of optimization. What the past 10 years have demonstrated is that its important to have different types of optimization for different areas of the supply chain.
