I don’t think it’s about demand volatility, so much as it is about demand granularity. Demand has always been volatile, it’s just that retailers had only so many channels (stores) to serve as aggregation points of demand. But now, more granular demand — not aggregated into stores, but at the individual customer level, expressed in many more channels than they had before — is much more highly visible.

The margin of the future is “in the margins” (ha ha) — if you’re forecasting to the aggregate, you’re leaving a lot on the table, and there is no tolerance in margins for the inventory and the mistakes it makes to operate at that summary level. The future is not analytics or AI — those are the tools. The future is understanding individual demands as closely as possible. Granular.

One cause of volatility is the growth of “ship from store” fulfillment of e-commerce orders (rather than shipping from a dedicated distribution center). This makes it harder to track sales and inventories at the individual location level, and makes replenishment more unpredictable. If e-commerce order fulfillment is totally randomized from one brick-and-mortar location to another (depending on who has the goods in stock and the costs of shipping), the customer looking for something on an actual store visit is more likely to be disappointed.

It’s not just infusing predictive analytics into the inventory management process, but embedding machine learning into the forecasting and replenishment models applied by retailers.

Supply chain is an area of fast changing processes and introduction of new technologies. The rate of change in this area is high and historically, the core competencies here have been on optimization of existing processes and not on introduction of new processes. That makes it more challenging, but not materially different from merchandising or marketing in this age of the intelligent enterprise.

The tools do exist and have early adopters. They’ll become even more critical in creating new paths to success that protect the top line and deliver on bottom line efficiencies.

As long as retailers plan their assortments based on where demand was fulfilled rather than where it was generated, the best forecasting systems in the world will not help them.

Many trends are intersecting to make it more difficult to predict and respond to demand. Consumers are more fragmented, shopping patterns are more irregular, there is more competition including from niche players, and the number of distribution channels has exploded.

This leads to two main issues. First, having the right products is more challenging. Second, ensuring those products are in the right place is challenging.

The second difficulty can be remedied by systems that allow a single view of stock and that treat most locations as places that can act as fulfilment points. The first challenge is definitely helped by greater use of data and analytics; however, it also arguably benefits from more human creativity and innovation when buying, branding and promoting products.

Traditional assumptions about forecasting and consumption are holding back supply chains. Volatility is the order of the day because of changing consumer demands, fragmented consumer demands, new products, online or mobile shopping, and various delivery options. If forecasting algorithms do not factor in these new assumptions, they will be wrong. If inventory movement models are not quick and agile, they will not have product where it needs to be. If inventory cannot use replenishment models rather than over-ordering, they will be wrong. While better analytics will help, it is important to remember that analytics are only as good as the assumptions used to build algorithms. Assumptions needs to be updated first.

Ecommerce has just added a new wrinkle to an old problem. Volatility has always been a significant hurdle because markets don’t perform in totally rational — and therefore predictable — ways and because it’s all but impossible to accurately forecast the kinds of critical disruptions that can adversely impact supply chain performance.

Analytics clearly held — up to a point — but their benefits are often offset by the fact that they themselves are the products of bias and assumption, starting with the belief that historical forecasting is the best predictor of future demand. It seems to me that the foundational supply chain problems have stayed the same. What’s changed is the tsunami of data that is currently available and the compression of time to analyze it. Add an expanding number of demand or distribution nodes that require servicing and a less than predictable consumer mix into that stew, and the result is the problem set we face today.

So yes ecommerce is making things more difficult, but the difficulty is an exacerbation of existing problems.

Data analytics is the key to improving performance and more accurately forecasting demand, which is the main driver for a smart and efficient supply chain. The use of Big Data getting as close as possible to the customer is the best way to understand what the impacts are on a local level. The supply chain today is too complex to look at warehouse demand in its simple aggregated form and forecast from there. By understanding the data at the lowest level, breaking out the impacts of weather, specified demand channels and promotions you start to understand the drivers and enable more accurate forecasting that leads to better supply chain optimization. Moving on to the next level of mastering the supply chain, we bring in the store operations, the store layout, displays and inventory on shelf. Now we really begin to understand the physical and online channels we can start to build a better picture of the demand and future forecast. To say that availability and optimization are less important or are taken as given is… Read more »

Looks like we should add “holding more inventory” to the list of extra costs that come from ecommerce. It’s important to KNOW the costs and avoid undue enthusiasm from the hype.

Retails traditional forecasting tools were always difficult — because demand is not predictable via algorithm. As a result, there’s no way that adding more data will find a perfect solution.

Could better data analysis help? Some. But the best inventory management I’ve seen has been done by humans with smart data at their fingertips. Only humans can make the judgement calls effectively — but they’ll do that best when they have better data in hand.

What’s damaging in implication is the idea that inventory management should be “turned over” to big data systems. Retailers should be careful not to take the bait – keep people involved.

Demand volatility is likely overwhelming traditional forecasting tools. Better forecasts can be generated by real-time data collected with shelf-based RFID solutions. These use small batches of data delivered on a daily basis as opposed to trying to forecast based on historical Big Data with the demand volatility difficult to analyze. Small data is able to see the demand volatility more easily. Fusing inventory data is a bigger hurdle than other data since it hard to collect from the store and POS data is not close to being 100% accurate. Data must be collected from the store shelves on at least a daily basis to be accurate enough to meet demand volatility.