A key lesson acquired from the harsh economic climate is that supply chains need to be modernised, and logistics policies need to be innovated to address current challenges.
We have already begun this critical discussion about supply chain management. Today, we complete our focus on the Harvard Business Review (HBR) article that discusses this in significant detail.
Redefining the Supply Chain Strategy
The HBR article points out that traditional supply chain strategies have often focused on either operational efficiency or responsiveness. When operational efficiency is the priority, a firm strives to squeeze as much cost out of the supply chain as possible, and that goal drives supplier selection, manufacturing strategies, product design and distribution, and logistics. Typically, production and distribution decisions are based on long-term forecasts, inventories of finished goods are located close to customer demand, and components are often sourced from low-cost countries.
The objective of a responsive strategy is to compete on time to market, satisfy demand quickly, and eliminate stock-outs. Manufacturing or product assembly is based on actual orders rather than forecasts; products may be customised; inventories of components are maximised but inventories of finished goods are minimised; and speed is prioritised over cost in decisions about sourcing and transportation.
The article adds that although seasoned operations and supply chain executives understand the difference between efficiency and responsiveness, many are nonetheless confused about when to apply each strategy. That’s because different products have different characteristics, with some requiring a strategy focused on efficiency, some a strategy focused on responsiveness, and some a hybrid approach. Until recently, executives didn’t have the tools to segment products and decide which strategy was appropriate for a particular segment. But that has changed, thanks to digitization and analytics.
Companies need key performance predictors: metrics that indicate what the state of the supply chain will be in the next three to six weeks. These are central to smart execution.
The article points out that the CPG manufacturer began by exploring variations in sales data, focusing on products’ sales volatility, volume, and profit margin because each is directly related to risks associated with stock-outs, service levels, inventory, and transportation. The higher sales volatility is, the lower the forecast accuracy, and the riskier the product. That, in turn, translates into frequent stock-outs and lower service levels. Similarly, the higher a product’s profit margin is, the higher the risk is since missing an order will have a bigger impact on the bottom line. Volume, in contrast, is inversely proportional to risk—that is, the higher the volume, the lower the impact of missing an order, and the lower the risk. These relationships are consistent with those we’ve seen at other CPG and retail companies, though sometimes other companies focus on price or product cost rather than product margin, depending on which one is more stable and, as a result, easier to apply.
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The analysis revealed that CPG had four product segments, although other companies may have more segments given their products’ characteristics. Each segment required a different supply chain strategy. The first segment comprises products characterised by high volatility. Because their stock-out, service-level, and inventory risks are high, they require a responsive supply chain strategy. Finished goods inventories for them should be located in central distribution centers. Each centre will be responsible for many retail outlets, which allows a company to aggregate demand, improve forecast accuracy, and reduce the inventories needed to supply the retailers collectively while maintaining high service levels. Because fast delivery is critical, these products are often shipped through cross-dock regional facilities—at which items from incoming large trucks are reloaded onto outbound smaller trucks with no storage in between.
The article points out that the second segment comprises products with high volume and low volatility, which require an efficiency strategy. In their case, forecasts are reliable, and managing transportation costs is important. Because of this, the products are stored in regional warehouses close to customers, and inventory is replenished on a fixed schedule. That allows a company to load trucks fully, taking products from manufacturing facilities to regional warehouses, which keeps transportation expenses down.
The remaining two segments are both characterised by conflicting drivers: low demand volatility (which suggests that an efficiency strategy would be best) and low product volume (which alone would call for a responsive strategy). What distinguishes these two segments are product margins.
Let’s look at the high-margin ones first. Because these products are riskier, many are stored at both centralized locations and regional warehouses and replenished based on actual store sales. That strategy allows a firm to strike a balance between efficiency and responsiveness, though it leans toward responsiveness.
The article adds that, in contrast, low-volatility, low-volume, low-margin products call for a hybrid strategy that leans toward efficiency. Indeed, because the risks and cost of holding inventory are low while demand is predictable, a firm can ship these products on fully loaded trucks to regional warehouses close to its customers, supply them from those locations, and minimise transportation costs.
Once a company has done the segmentation, it needs to develop detailed sourcing, manufacturing, and logistics strategies. One objective should be to identify synergies across the segments that will allow the firm to benefit from economies of scale. They can be achieved by leveraging volume across segments to reduce procurement costs, sharing capacity and infrastructure in manufacturing and logistics, and consolidating demand and supply information for better planning and execution. We’ll now look in more detail at that last activity.
Balancing Supply and Demand
The HBR article points out that an important supply chain management process that has been applied since the mid-1980s is sales and operations planning (S&OP). It continually balances supply and demand, and historically it has called for managers launching new products and leaders from manufacturing and distribution to come together and agree to a single plan. Typically, it involves analysis at the business unit level or the product family level, not the individual product level.
Traditionally, S&OP is simply an extension of the consensus forecast, and because of that it suffers from similar limitations: It doesn’t start with a unified view of demand, doesn’t create a plan at the SKU level, doesn’t distinguish between supply chain segments, and is driven mostly by common sense, experience, and intuition, not data and analytics. Because it’s a manual process, it generally takes a month.
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A better approach to S&OP replaces the manual process with an automated one that can be performed weekly, and ensures that the engineering, finance, sales, supply chain, manufacturing, sourcing, and trade functions are all working to achieve the same business goals. The new process begins when an analytics-driven optimisation system generates the SKU-by-SKU supply plan we described earlier. This plan will inform everything from master production schedules to materials planning to logistics, including inventory and transportation decisions.
The article adds that, while not every company or business unit needs to produce a plan weekly, such frequency is critical for products whose demand is highly volatile and whose marketing and promotion strategies often change.
The new S&OP process also calls for monitoring activities. Firms should collect information throughout the supply chain about key performance indicators (KPIs) such as supply lead times, raw-material and finished-goods inventories, and service levels, looking for any problem or deviation that could undermine the sales and operations plan. Firms can then work to address those issues and, if they turn out to be significant, adjust the plan itself.
The article points out that companies also need to keep an eye on data and events that portend what may happen in the near future. For example, while inventory and service levels may suggest that everything is going smoothly, shipment-tracking data may indicate that lead times are likely to increase and that; as a result, service levels could go down in the next few weeks, signalling a need to build inventories or expedite shipments. Similarly, if a disaster causes the shutdown of a supplier’s manufacturing facility in Asia, it could affect available supply down the road—perhaps forcing a firm’s manufacturing and assembly plants on the U.S. West Coast to lower or stop operations in five weeks. But traditional KPIs alone might not provide any warning.
For this reason, companies need key performance predictors (KPPs): metrics that indicate what the state of the supply chain will be in the next three to six weeks. KPPs are central to what we call smart execution, a new business process that complements smart S&OP. While S&OP focuses on the next 50 to 80 weeks, smart execution homes in on the short term (no more than six weeks) and tries to identify and quickly respond to disruptions and deviations from the plan.
The company adds that Smart execution involves three automated capabilities: (1) the real-time capture of internal and external data that reveals potential deviations from the plan, supply disruptions, or changes in demand; (2) artificial intelligence that identifies the potential impact of those developments on supply chain performance; and (3) analytics-driven optimisation that determines the best response, considering various trade-offs and objectives.
By gathering financial information on suppliers that are public companies and internal data on supplier performance (for instance, on lead times, service levels, or product quality), firms may be able to identify distressed suppliers. An AI system can then project the likelihood and impact of a supplier default on future commitments to on-time delivery and product quality. Finally, the automated optimisation system can identify an alternative supplier for sourcing the material.
Implementing improvements
As we head into the December shutdown, we need to take a long, hard look at our supply chain strategies and determine whether they are fit for purpose. What improvements can be implemented?
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