By Dr. John Gattorna
(Dr. Gattorna is one of the world’s leading supply chain consultants and author of the recent book Dynamic Supply Chains. He will be a keynote speaker at Oracle’s Modern Supply Chain Experience conference February 13-14, 2017, in San Jose, California.)
The Hatchimal toy was one of the big hits of the 2016 Christmas season, leaving desperate parents clamoring to find the coveted gift in time.
Producing such a hit could be a toy company’s dream—or nightmare, if their supply chain is not prepared to handle the surge in orders.
For the company producing the product, there are a number of methods that can help them meet demand if they have the “it” toy of the season (or a hit anytime during the year)—while not being stuck with warehouses full of product if demand isn’t there.
- Predicting launch and early stage demand. The company could use data analytics around sales performance of similar products in previous years, or look for geographic/retailer ‘bell-weather’ locations to point to demand take-up at the very early stages of launch—and use that information to decide the level of follow-up orders and production. Ultimately, the company could use heuristics that combine information from multiple sources—consumer demographics, consumer segments, channel segments, retailer shelf space, competing launches, weather trends—to predict demand and channel behavior.
- Multi-strand manufacturing. The company could estimate its baseload supply—the initial and follow-up production quantities that will be needed—and place a corresponding order for components with upstream suppliers around the world. These would most likely be preferred suppliers.
If the observed consumer take-up is good, the company may need to call upon a larger number of alternative manufacturers/suppliers in its supply-side network to achieve quick replenishment by taking advantage of available capacity.
As a back up, the company could book spare capacity through its selected manufacturers in anticipation of a certain level of sale, and cancel (at a cost) if the anticipated demand doesn’t happen. Some suppliers might adopt postponement protocols to speed their response.
- Hold back and optimize positioning. The company should closely monitor the offtake pattern of the initial production batch, and hold off fully committing stock. It could, for example, only commit 60% of the initial production run to certain markets/channels, then use the balance to quickly respond where the uptake is strongest.
- Prioritize allocation. By deciding to commit a certain percentage—say 80% of the initial 60% produced— to trusted outlets where the relationship is truly collaborative, the sales pattern data is more likely to be transferred back to the company on a close to real-time basis, and those customers with long-standing, loyal relationships are rewarded.
- Leverage multi-channel fulfillment. Looking downstream on the distribution side, the company could use its chosen omni-channel structure to reach consumers as quickly as possible when sales exceed expectations—like emphasizing eCommerce channels to minimize pipeline time, taking orders online and fulfilling direct through a global air express delivery company—or by support wholesalers and retailers via drop-shipments designed to be distributed to their consumers, separating the physical and the commercial fulfillment.
- Minimize pipeline time. The key for all parties in multi-distribution channels is to keep the supply pipeline as short (in time) as possible, thereby defraying the risk of being caught with excess stock if/when sales tail off. For example, Zara does this very well by monitoring its sales on an hourly basis at its retail outlets, coupled with its 3-weekly cycle to launch a new range of products, leading to much less inventory at risk of mark-downs.
- Organize strategically. The other aspect to this problem of launching new and potentially volatile products is the internal organization design. Companies like Zara, Li & Fung and Adidas use multi-disciplinary teams or clusters, co-located, and focused on particular customer segments. In this way, the loss of communications time internally is minimized, and is a big factor in improving responsiveness in the face of volatile demand patterns.