根据后勤局的说法, 69 percent of organizations don’t have full visibility of their supply chains. 为什么这很重要?? Because full visibility is needed to improve 制造业 supply chain operations and reduce costs. 对于大多数制造商来说, supply chain data is siloed in various locations—often in multiple spreadsheets—which makes it difficult to uncover opportunities for improvement. 

The Logistics Bureau also notes that businesses with optimal supply chains have 15 percent lower supply chain costs, 不到50%的库存, and cash-to-cash cycles at least three times faster than those not focused on supply chain optimization. 这就是网络优化的用武之地. Network optimization provides visibility into all areas of a 制造业 organization’s data, empowering decision-makers with insights they can use to drive actions to improve operations and reduce costs. 

创建数字孪生 

Trying to manually model an entire supply chain using data from spreadsheets is a labor-intensive, 耗费时间的, 这是一个充满错误的艰巨过程. Network optimization streamlines the collection of a manufacturer’s supply chain data by creating a “digital twin.” The digital twin replicates relevant supply chain data to give a manufacturer the ability to gain a granular view of all its critical data elements. 典型的供应链数据元素包括:

• 位置
• 卷
• 起源
• 材料
• sku
• 产品重量
• 运输成本
• 吞吐量成本

Once all of the supply chain data is aggregated into the digital twin, manufacturers can use this granularity to identify their true total costs. It’s important to keep the digital twin up to date— otherwise it becomes an outdated picture of what once was. 有一个更新的数字双胞胎, supply chain managers can see lanes and flows in real-time, enabling them to fully understand what’s going on in their business. 

获取可操作数据

Having a manufacturer’s supply chain data in a digital twin is the right first step. But they need to take action based on that data to uncover its true value. The next step in a network optimization study is modeling. Once the data is consolidated in the digital twin, the modeling outputs are then input into sophisticated business intelligence tools to create an easily consumable, visual digital representation of improvements that can be targeted. 

有了这些数字模型, manufacturers can determine and compare trade-offs of potential changes to their supply chain network. The can quickly separate opportunities for measurable improvement from initiatives that won’t provide gains — or worse, 可能造成损失. Visualization tools make it easy to select scenarios, 查看可能的策略并计算结果. 

It’s not a one-size-fits-all when it comes to reviewing digital twin models. It really depends on the industry and the make-up of the manufacturer’s organization. 例如, an e-commerce organization with high demand volatility should review and adjust every three to six months. 与此形成鲜明对比的是, a large manufacturer with less demand volatility might opt to review scenarios and make improvements annually.

 一个全局网络优化研究

In 2020, 当时新冠肺炎大流行正如火如荼地进行着, a leading global manufacturer underwent a network optimization study to learn if there were opportunities to improve their supply chain operations and reduce costs. The manufacturer was spending more than $1 billion annually on shipping, 提供12,000种不同类型的材料到19,000 customers across six continents and operating more than 200 facilities across the globe. 

After a digital twin was created that integrated the manufacturer’s most relevant data, advanced modeling software was applied to analyze scenarios, identify areas for improvement and provide recommendations that were actionable. 通过网络优化研究, the manufacturer learned that much of their annual shipment spend was being wasted.

In the country where the manufacturer was headquartered, they had production plants on both coasts shipping the same products to different regions. The data and modeling also revealed they lacked strict processes for sourcing customers in different regions, 管理交货期和预定运输成本. 

The interactive presentation of the data was the eye-opening moment that the manufacturer needed. Their global inefficiencies were in plain view through actionable data and scenarios that provided clear opportunities to correct inefficiencies and optimize their global supply chain network. 最后, the manufacturer discovered the opportunity to save $60 million due to the network optimization study.

A network optimization study can help manufacturers drive performance and improve bottom-line results. The main measure for considering network optimization is whether a manufacturer is ready and willing to make changes to improve their operations and reduce costs. The visibility gained from a network optimization study simply isn’t enough. A manufacturer needs to be prepared to use the insights revealed by the study and take meaningful action to improve their supply chain network.