One trend that is as prevalent these days as the beards men are sporting in tandem with their sweatpants: to deal with spiking demand, constrained production capacity, or both, many companies are cutting off the tail of their product portfolios.
Companies have traditionally been willing to accept the fact that 80% of their product portfolio provides just 20% of sales. That’s because they’ve been able to manage capacity in a way that allows them to serve the demand tail and capture that portion of the revenue. Supply chain practitioners devote much effort responding to the Pareto Principle (or 80/20 Rule) in ways that maximize profitability, meet service requirements, and manage operational complexity.
However, many companies – particularly those in the CPG, food and beverage, and healthcare sectors – are being forced to rethink the value of niche products like mango habanero non-dairy creamer or lavender-scented disinfectant wipes because of the imbalance between demand and supply across the entire spectrum of their product portfolios. Though fans of these particular varieties may feel slighted, companies are reducing production of low-demand products while they ride out COVID-19 for a variety of reasons.
Demand has outstripped production capacity
Many products have seen considerable increases in either short-term or seemingly persistent consumer demand (think protective gloves, face masks, disinfectant wipes, hand sanitizer, eat-at-home breakfast cereals, milk, or toilet paper). By eliminating low-demand items, a company can reduce the time required for manufacturing line setups and changeovers, allowing lines to run longer and more efficiently. Our team has seen this first hand. One Chainalytics client has reduced the number of products they’re manufacturing by 33%, another client by almost two-thirds. Both are now setting record production levels at plants that were already running 24/7.
Capacity has decreased because of limited or intermittent labor availability
Some production facilities are facing capacity shortages even though demand is relatively unchanged. The coronavirus pandemic has reduced production capacity in several ways. These include reducing the number of workers that can safely work in a plant at the same time, curtailing the number of employees available to work because of quarantine or illness, or shortening the hours of operation because the facility has been taken off-line for cleaning. The meat and poultry industry, for example, was hit hard. According to the CDC, cases of COVID-19 have been found among workers in 115 processing facilities, stopping or slowing production. Items like ground beef and boneless chicken breasts have become hard to find. Halting the production of products in the 80% “tail” can help a company maximize its output of higher-demanded items.
Demand is flat or down compared to plan
Low-volume items tend to be more variable in demand. Predicting demand on these items is often difficult, even under normal conditions. In the current situation, some companies are choosing to not even try to forecast demand for these items. They’ve concluded, in the near-term, that the risk of unsold inventory of low-volume items coupled with the opportunity cost of serving low-volume demand is greater than the sales revenue they might forgo on faster-moving items.
Will these products make a comeback?
When life returns to normal, will these product portfolios remain tail-less? Should they? Like so much these days, it’s too soon to tell. However, you can leverage several data-driven approaches to decide what’s best for your company even before things start to return to normal:
Conduct a cost-to-serve analysis: Develop a detailed understanding of your production and distribution costs by customer and product. Actual costs can vary substantially across SKUs based on production complexity as well as the true costs of handling, storage, and shipping, which are driven by service levels. Once you understand your costs, you can determine which products are contributing to profitability, and re-evaluate your product portfolio strategy if needed.
Build a network model that considers capacity trade-offs: Going beyond a cost-to-serve analysis, network models can consider various forms of capacity in response to product proliferation, allowing for more proactive product portfolio decision making. Such network models should include:
- Production line-level manufacturing representation that takes into account the time – and units – lost to changeovers and setup can help companies better determine the effect of production decisions on manufacturing output, supply chain performance, and revenue.
- Inventory, storage, and handling requirements that differentiate fast vs. slow movers, steady vs. volatile products, or seasonal vs. non-seasonal products can accurately reflect the impact of long-tail items on working capital costs, warehouse space costs, handling/pick efficiencies, capacity utilization, and operational complexity.
Use a robust Sales & Operations Planning process: A properly implemented S&OP process supports a routine review of customer demand, forecast demand, supply resources, available inventory, and planned inventory, and facilitates a quantitative re-plan across an agreed rolling horizon.An S&OP process could also be used to facilitate more strategic decisions related to the mix of products in a product portfolio, particularly from a capacity perspective.
What do I think? Many companies will decide that they can operate more nimbly and profitably with fewer low-demand products. Manufacturers with little idle capacity are more likely to see these product eliminations stick. All companies grappling with these issues would be well-served by a data-driven approach to assessing their options and guiding decisions. These are the types of questions that my colleagues at Chainalytics and I love to solve.
As for me, the Covid beard is staying.
Tom Cisewski is a Principal of Supply Chain Design at Chainalytics, where he focuses on applying large scale optimization models to address strategic supply chain challenges. In addition to leading client engagements, he is responsible for acquiring, developing, and curating Chainalytics’ supply chain digital data assets. Tom has successfully led dozens of large-scale network analyses for clients in the CPG, high-tech, and process industries throughout the world.
