Cost and delivery performance are key to a good plan but in reality, many more objectives must be met at the same time. Examples are: carbon reduction, resource utilization, import/export taxes, commodity price changes, preferred Suppliers and regions, increase in market share at reduced prices and many more.
Traditionally, the main goal for supply chain planning has been to optimize use of resources, deliver on time in full (OTIF) and reduce cost. However, there are other objectives such as increasing market share at reduced margin or even loss, adherence to regional regulations and compliance, improving carbon emission or landfill, preferred use of certain suppliers or higher priority for some customers amongst many others. Many of these objectives are in conflict with each other. Consider a simple case of reducing cycle times, improving customer service and maximizing use of resources. As WIP is increased cycle times go up and utilization increases but delivery performance drops. The solution to this specific problem can be found in this Adexa whitepaper.
In a more general sense, there is a need to express any kind of objective with some level of emphasis indicating its importance. The task of the system is to balance such objectives and find a solution that is optimal to the end users’ desires. Different people have different objectives and need to “negotiate” their priorities so that the system can arrive at a decision that converges to, at least, the minimum requirements (tolerance level) of each stated objective.
It should be noted that, it is not possible to always find a solution if everyone insists on hundred percentage of their objectives. We cannot always have minimum cycle times, 100% delivery performance and 100% equipment utilization all at the same time.
The methodology that we have implemented in our algorithms is based on a smart heuristic algorithm that enables the users to state their objectives and expressing the tolerance level and how important each objective is relative to the others. Then the system goes through analyzing thousands of scenarios arriving at top solutions, given to the end users for their selection. Needless to say that, depending on changes in the business as well as the economy, competitive factors, Tariffs and many other factors, business objectives can change. Hence new objectives maybe stated and their importance may be changed. For example, if the price of a commodity goes up, then one objective is to avoid increase in cost. The system would automatically examine all the alternatives giving perhaps lower priority to making products that use too much of that commodity or are dependent on a region with high tariffs. It is critical that the system is flexible enough to understand any stated objectives by the users. With Adexa, this is accomplished through the use of Attribute Based Planning (ABP) technology. With ABP, any object in the supply chain can be attached to as many attributes as the users may desire. The value or range of values of these attributes are expressed as tolerance levels for the system to search for a solution. the attributes form intelligent constraints for the search space in expressed in the form of Boolean expressions using AND/OR Logic with “=”, “<”, and “>”.
It should be noted that, the way the users are communicating with the system is to express their objectives not try to change tens or hundreds of input variables such as increasing demand or taking out a supplier just to see what happens. Although that is also possible but not needed. This approach provides the users with an environment to state their objective and let the system do the heavy lifting of examining different scenarios, perhaps thousands, in order to arrive at a solution that is acceptable to all. It is also flexible enough to change the nature of the objectives as well as their relative importance as the world around the business changes. For more on this topic and other uses of automatic scenario analysis click Here.