In last week’s blog, Lora Cecere emphasized the need to forego traditional forecasting and replenishment strategies in the long tail. In the recent Consumer Goods Technology (CGT) web seminar entitled “Driving Results Despite Item Complexity”, one slide quoted her as saying previously,“If the products have a skewed distribution and it is your desire to meet a higher service level, then the deterministic technologies of APS and ERP are not designed to meet your needs.”
Pat Smith, Vice President of ToolsGroup North America, then illustrated the growth of the long tail and drove home how to combat the challenges posed by it.
He showed how the long tail is fragmenting the demand signal as companies move to serving more niches. This means seeing many periods of zero demand with intermittent demand or "lumpy demand" spikes in between. And it’s getting worse as demand volatility increases. “The 80/20 rule, where 80% of the revenue is generated by 20% of the products, no longer holds,” he says. “Pareto is dead”.
Smith showed that it’s happening across many industries. He started with an internet retailer with a huge tail: 79% of their revenue. But fast moving consumer goods manufacturers are now seeing about 50% of their revenue from the tail. Even food and beverage companies can have 36% of their revenue from the tail. Lora Cecere added that the tail drives an even higher percentage of a company’s profits, since new offerings and more specialized products often carry premium margins.
The problem, Smith says, is that many organizations are stuck in with the old way of looking at their supply chain, before the tail became a big part of their revenue and profits. “They’re delivering service, but at high cost, erosion of margin, and lost market penetration,” he says. There is no real forward-looking visibility into and understanding demand volatility at the Item-Location level of detail."
“Everywhere I go,” he explains, “I see organizations overly focused on improving demand forecast accuracy. Forecast quality is crucial for everyday, fast-moving items. But for tail items, forecast accuracy becomes less and less relevant. For slow movers, focusing on forecast quality will not translate into improved service level. Customer service must come from inventories.”
The focus, Smith explains, needs to be on demand modeling and probabilities of future demand. Deploying demand modeling as a core competency moves companies from a single demand number to understanding demand probabilities. It provides visibility into the range of possible demand behaviors.
Smith says that this forward-looking “predictability” strategy enables companies to make better trade-offs and decisions in how they deliver service. “You really need to think more about understanding the probabilities of future events occurring and their risk to working capital and service level performance,” Smith says. “The long tail increases volatility. In many companies, the inventory mix is misaligned, and the organization is leaving revenue, margin, and cost benefits on the table.”
Smith quotes from a recent Deloitte study: “Companies that continue to utilize traditional supply chain models will struggle to remain competitive and deliver orders that are complete, accurate, and on-time.” He says it’s about reframing your point of view: Don’t get caught up in the forecast accuracy bandwagon – it works for the head items, but it’s only a small part of the solution for the tail items, where it’s more about identifying the range of possible outcomes and then leveraging inventory policies and multi-echelon inventory optimization to deliver service and mitigate risk.
This is part 2 of a series of 4 blog posts on the Managing the Long Tail of the Supply Chain topic. Below are all four blog posts:
- Part 1 - Managing the Long Tail of the Supply Chain
- Part 2 - Pareto is Dead
- Part 3 - How Dart Achieved a 99.6% Service Level
- Part 4 - Five Takeaways