Who oversees the true value and cost of availability?
Availability is the result of demand planning, production scheduling and batching, sourcing policies, production and distribution lead times, and the size as well as location of inventories. Moreover, the closer to 100% availability one gets, the higher the incremental cost of every additional 1/100.
Now, it is important to properly understand the cost and value of availability. In general, costs are fairly tangible and easy to capture at a product group level. Valuing availability, however, is a far bigger challenge. After all, capturing the lost sale of an out-of-store or out-of-stock product is difficult. And if you manage to do so, what is the opportunity gain of your buyers selecting – and potentially trying – an alternative?
Furthermore, targeting availability is generally a tradeoff that is biased towards the most profitable and/or fastest-growing channels and customers. The question is: What does this mean in terms of cause and effect? It is paramount to realize that the loss of poor availability in thin channels may outweigh the gains of high availability in profitable ones – even to the point where you are actively destroying shrinking-yet-valuable channels. A topical example includes the trend within retail to favor online options over traditional retail channels. Aren’t retailers actively contributing to the shift towards a unilateral online retail landscape by doing so, while diversity is, in fact, very welcome in view of long-term, sustainable returns?
Focusing analytics: higher forecasting accuracy or more supply chain responsiveness?
A fair share of investments usually goes to tooling and skills to increase forecasting accuracy. Understandable, as high accuracy is perfect if you want to achieve high availability at minimal cost. In general, reaching a certain level of accuracy per market or product group is rather simple. Usually, it is strongly related to sales policies that are geared towards delivering the annual plan. Machine-learning algorithms can add value by properly capturing external factors – such as weather conditions, price elasticity, and consumer confidence levels – if these play a major role with regard to volumes, which is the case in certain markets.
Yet, sourcing and production need to make decisions at a single-item rather than product group level. Accurately forecasting single items turns out to be inherently difficult in many markets. Therefore, a different approach tends to be more fruitful: you accept a certain level of inaccuracy at the single-item level and deal with it. The latter means you must drive structural improvements across the chain that make your chain more responsive. This implies focusing analytics on business cases of lead time reduction, distribution tactics, batch size reduction and the likes.
Balancing availability requires capable, cross-chain supply chain analysts
Having a firm grasp of these tradeoffs between the cost and value of availability is crucial to drive sound planning decisions and push drivers of availability – such as inventory, batch size, and lead times – in the right direction.
In an earlier blog, we already discussed the importance of understanding data. We tend to see a huge return when companies actively strengthen the analytical capabilities of their supply chain function. In fact, companies should recognize the fact that they are creating valuable roles involving deep knowledge of the entire chain – from sourcing all the way to the customer.
This top-line-over-bottom-line approach creates an attractive setting for talented analysts who develop an integrated perspective on your value chain. A perspective that is highly valuable for your organization when they move into the more senior roles!