Optimization has proven much more tricky to implement that originally thought. Optimization where the company must agree internally in order to set costs, is far more difficult to implement than where the optimization is essentially a black box.
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
Linear vs. Discrete Optimization
Optimization 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. For instance, the 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.
The Decline of Optimization
However, while optimization drove development in SCM at one time, it no longer does. The evidence for this is that 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. A related story to this is that optimization did not meet its originally envisioned potential.
What is Actually Running the Solution?
There is a story at 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 Not Optimization?
If optimization did not “change the world“ of supply chain, the question naturally becomes “Why not?“ There are probably a number of reasons, however, in our view one of the most prominent came down to implementation and maintenance difficulty combined with company knowledge. Implementing and maintaining optimization methods requires a great deal of effort and long term investment. Secondly, optimization requires a great deal of discipline and knowledge on the part of the implementing company. Many companies want the benefit of advanced planning, but are not culturally, financially or prepared from a skills perspective to make the sacrifices required in order to obtain the outcomes they desire. More often that not, companies want simple solutions that delivery value over a short time horizon. That is not what optimization delivers. On the other hand, software deserves some of the blame as much advanced planning software has been designed to be more complex than is necessary.
Conclusion
Optimization is always the most complex and difficult of implementation, but also brings significant benefits if implemented correctly. However, for most that mountain is too high to climb. I sometimes get email from technical people who really like optimization and wonder why I am “negative” on it. This has nothing to do with whether I personally like optimization or not. I am reflecting the experience of companies, most of which are unsuited to use it. A lot of money has been spent or wasted on optimization that could have been used to solve much more simple problems in the business. Solving these simpler problems has a higher payoff, and thus should be addressed first. In addition to lower investment, the returns from optimization have not been encouraging, and thus its incumbent upon me as a consultant to bring these failures to light for clients, and the direct them towards solutions that have a higher probability of success.
