A big focus of this blog is, of course, Strategic Sourcing Decision Optimization (SSDO), one of the few advanced sourcing methodologies guaranteed to save your organization, on average, 12% if correctly applied (as demonstrated in two back-to-back studies by Aberdeen) and the doctor‘s speciality. But it’s not the only place you can apply optimization in Supply Management to save money. Another area, as covered a number of times on SI, is Supply Chain Network Optimization (SCNO). And, of course, some companies just focus on the intersection and do Logistics optimization. But this is not everything that can be done, or should be done, especially in an age where many industries now see The End of Competitive Advantage and don’t actually own physical assets, leasing them as need be to create the products and services desired by their prospective customers.
In this situation, what matters is Asset Optimization, where you optimize a one-time dynamic network to minimize sourcing, network, and logistics costs to minimize the total supply chain costs associated with the product you wish to produce. This is easier said than done. In sourcing, you are mainly considering bids, lanes, and associated costs to compute the optimal TCO (Total Cost of Ownership), and if lifetime costs and metrics are available, or TVG (Total Value Generated) with respect to a fixed situation. In network optimization, you are optimizing the location of owned factories, supplier production centers, warehouses, and retailers to optimize the distribution costs. But in asset network optimization, you have to simultaneously consider the network and associated distribution costs, the sourcing requirements and associated production costs, and the costs of using, or not using, the resources you already have available and contracts you have already negotiated. In addition, you have to consider the risks associated with each potential supplier and location, the sensitivity of the overall asset network to each supplier and location (and is there a single point of failure), and the ability to dynamically alter the network should a failure occur or customer demands change.
Plus you have all of the difficulties associated with each type of optimization. With respect to the network, there will be many alternatives for production site, each site will have multiple, and different, asset lines, and each asset will be qualified for a certain operation with respect to a certain product. In addition, some assets will be more efficient and cost effective, and unqualified assets will have a qualification/certification step, which will require limited manpower – a variable that does not need to be modelled in traditional sourcing or SCNO models. It’s a very difficult problem that requires modelling of multiple types of variables and constraints at multiple levels at multiple times. And this last requirement makes the model even more complex. In a traditional sourcing model, you don’t really need to consider “time”, as it doesn’t matter how often the trucks deliver your product, just how many trucks are needed to deliver your product as you are billed FTL or LTL by the delivery. And it doesn’t matter what production schedule the supplier(s) use(s) as long as your products are ready on time, so only the total volume need be considered. But when you are dealing with production models, especially when trying to dynamically construct and optimize an asset network, production schedules are significant. If a certain location only has 30% of capacity left available and can only schedule it during a given timeframe, that has to be taken into account. If some of the products have to be delivered before they can complete the first production run, then there has to be a location that is able to do so. And if a continual supply is needed over nine months, the production cycles should more or less line up with minimal overlap as, otherwise, inventory costs would soar.
It’s a complicated problem, but one that is becoming more and more important in fast moving industries such as fashion and consumer electronics — and one that most SSDO providers can’t address. But I’m happy to report that there are a few optimization vendors in the space who can. One is Algorhythm, in India, that has been doing SCNO for many years, and who has built up a lot of this capability over time while working for it’s global multinational clients such as Unilever. Another, newer entrant, is Trade Extensions, that has been doing SSDO for many years and, at the request of its major multi-national clients, including P&G and Coca-Cola, built up the capability in their solution with innovative new platform enhancements since SI last reviewed their solution in 2011 that make it very easy to define the models, run the scenarios, compare and navigate the results. A few of these enhancements will be described in a future post. Stay tuned!