For retail companies new to the strategy, omnichannel merchandising can cause a lot of headaches. This particular practice obligates merchants to deliver seamless online and in-store experiences.
In turn, the strategy has caused numerous logistical changes. Two decades ago, warehouses were primarily responsible for handling store replenishment needs or wholesale shipments. Now, distributors are required to manage bulk deliveries and e-commerce orders under one warehouse.
A data-intensive situation
Organizing and implementing such a complex operation model requires industrial assets (Internet of Things) to track shipments, measure stock requirements and find the best delivery routes. Between radio-frequency identification and GPS technology, retailers have a lot of information to work with.
The question is: How do they use it to optimize logistics? Doing so requires a two-pronged approach:
- Unify all assets onto a single platform that supports real-time data collection and processing.
- Funnel historical and predictive analytics to forecast consumer demand for certain products, anticipate logistical risks and identify new expansion opportunities.
According to Business 2 Community contributor Dale Skeen, the name of the game is visibility. How can enterprise leaders expect to make pertinent decisions based on week-old information? Consumers operate at a rapid pace, to the point where an event that occurred four days ago may be considered "old news."
This mindset requires companies to conduct themselves in the same manner. Consider the following example:
- A construction supply company has a real-time view of its concrete distribution system in Wisconsin, including local traffic reports from stations throughout the state.
- Before a truck destined for stores along Route 39 leaves a distribution center, a major accident occurs on the highway.
- The analysis allows the hardware provider to notify all affected stores and coordinate with other warehouses to satisfy pertinent orders.
Even if the business catered to contractors who submitted online orders, the same process could still be executed. The only adjustment would be that the supplier would notify its customers directly and then consider the locations in which builders are working.
Predicting the future
What if said construction supply business wanted to predict the amount of concrete it would sell over the course of 2015? EBN spoke with Chainalytics VP of Industries Supply Chain Gene Long, who defined predictive analytics as collecting historical and near-term data with the intent of finding trends.
In the case of the hardware business, a company could scrutinize the rate at which commercial and residential construction projects are progressing. Real-time data can be applied on a constant basis to improve the accuracy of predictive models.
Keep in mind that having a specific goal is the best way to run a real-time analysis model. Creating a data visualization of your entire logistics operation may sound appealing, but it may prevent you from getting to the root of the problem.