This is a question that Dan Gilmore asks in his latest discussion of Mapping and modeling the supply chain.
The idea of a model of the entire supply chain is very attractive, but unfortunately it is unattainable due to the nature and complexity of the environment.
The main reason for this is that there are different levels of supply chain analysis – strategic, tactical and operational – they span different time frames, ask different questions, use different data at a different level of aggregation and require different modeling and analytic tools.
Examples would be:
1) Strategic: Network design which uses aggregated data on SKUs and demand locations to answer long term questions such as where to locate facilities, what should be the size of each facility, what should be the optimal distribution and sourcing strategies, etc.
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2) Tactical: Inventory optimization which uses information on demand by SKU (rather than an aggregation of SKUs), service levels, lead times and their variability to answer the question – how much inventory do we need and where do we position it? For many companies, this is part of S&OP (some refers to this as SIOP, where I stands for Inventory) which is a process that continuously matches supply with demand in a profitable way.
3) Operations: For example, production scheduling – how do I schedule tomorrow’s jobs on the line? Here, we need a model that takes into account the details of every change over cost and change over time. Similarly, in transportation decisions – how do I route my trucks or order enough space with my carriers next week?
To summarize, network design involves long term planning, requires data aggregation, is done at a high level, and the frequency of re-planning is relatively low. On the other hand, inventory positioning and management must be done at the SKU level (aggregation will kill the model) and the frequency of re-planning is high, weekly or monthly.
But is it important to model the supply chain? What are the benefits and why now?
I believe it has always been helpful to model the supply chain but even more so in the current more volatile environment for the following reasons:
- Management is more open to using analytics as part of the decision making process
- Data from the entire supply chain and beyond is more readily available
- Creates a common platform across functions for discussion of costs and supply chain drivers
- Quantify and understand the cost and service level drivers
- Calculate total landed cost which can help decision making in several areas including sourcing, complexity reduction and supply chain segmentation
- Determine supply chain risk exposure or perform a what if analysis to test out new ideas