Plan or Perish: Cold Chain Logistics
If it’s hot in August, you can duck in and out of air-conditioning. But that’s not the case with goods in the supply chain that always need to be kept cold. Cold chain logistics pose a major execution problem—maintaining product integrity across dimensions like temperature and humidity.
There are supply chain planning implications as well. In planning for perishable products, a key issue is “residual shelf life”—how much shelf life remains after the product is delivered. This applies to fresh foods, dairy, pharmaceuticals, and any category where perishability is vital.
Supply chain planners need to optimize inventory to maximize residual life while meeting service level targets. When shelf life is not an issue, you can stock up to meet service level goals. But loading up on short shelf life products begets obsolete inventory as products hit their expiry dates. Even products only approaching expiration can become outdated if customers shun them.
Two great examples of cold chain logistics in action
Findus Sverige AB is one of Sweden’s leading food companies, producing and distributing more than 800 finished goods items. Like many food manufacturers, Findus often favored high service levels to meet customer demand. This meant holding artificially high safety stocks to ensure availability—but courting perishability and inventory loss.
New demand planning software allowed the firm’s supply chain team to change from a one-size-fits-all channel strategy to modeling each channel according to its unique demand patterns, volumes and cold chain properties. The company beat its goal to cut inventory by more than 10 percent, reducing working capital requirements by 12-13 million SEK ($1.5 million).
A second example is Granarolo, a leading producer and distributor of fresh milk and yogurt. Their dairy market is characterized by short shelf life products and strong promotional pressures. Granarolo runs thousands of trade promotions annually, producing 34,000 item-promotion forecasting combinations; demand can jump up to 30x baseline sales, creating havoc in demand and inventory planning.
Granarolo added new software to model a reliable demand plan for these perishable, highly promoted products. Machine learning translates historical data into reliable estimates of future promotions. The software identifies possible deviations from the plan, and optimizes inventory to maximize service levels while complying with logistical and cold chain constraints.
Granarolo raised average forecast accuracy from 80 to 85 percent—with a crest of about 95 percent for fresh milk and cream, and 88 percent for yogurt and dessert. It reduced inventory levels by more than 50 percent, also cutting capital and lead times by 50 percent.
Read blog: What’s Your Forecast Accuracy Target?
The big picture: There are millions of tons of avoidable perishable waste along the food supply chain. A considerable share of these losses is caused by non-optimal supply cold chain processes and management.
Read the full Granarolo case study by clicking below: