Technology challenges for logistics forecasting
Technology challenges are driven by the systems and information that create, manage, and transfer data throughout the supply chain system. Fragmented, disparate software, difficulties with integration, a lack of visibility, and data availability all contribute to overly complex and unpredictable forecasting.
- Continuing use of legacy systems and siloing data creates errors, bottlenecks, and delays in integrating, sharing, and acting on vital supply chain information
- Inaccurate stock monitoring and a lack of visibility into inventory levels results in under-ordering or over-ordering and carrying too few or too many of a particular SKU
- Unavailable, inconsistent, or unreliable historical sales data increases the margin of error when forecasting future demand and inventory levels
- Poor audit processes lead to unreliable trends and forecasts when amalgamating, analyzing, and understanding historic data
Supply chain network challenges for logistics forecasting
Supply chain network challenges are driven by the external, unpredictable factors that impact on every supply chain. From “black swan” events like the COVID-19 pandemic or a Suez Canal blockage, to everyday delays and bottlenecks between supply chain partners. These network issues stack up to result in erroneous forecasts.
- Black swan events disrupt the speed and flow of orders and products through the supply chain, throwing out logistics forecasts and delaying stock arrivals
- Limited supplier and manufacturer contingency planning results in delays and issues from supply chain partners that affects order fulfillment and the timely production, transport, and availability of products
- Disruptive events have a complex, compounded, and increased impact upstream and downstream in the supply chain that all partners need to react to
- Large variabilities in supplier, manufacturing, and transportation lead times result in misunderstanding and added complexity when forecasting how quickly orders will be fulfilled and products will be available
- Lack of central data control and coordination creates difficulties sharing and analyzing data between third parties and intermediaries
Taking account of these challenges allows us to build more robust logistics forecasting models that can react to risks and understand the likelihood and impact on inventory levels.
Read the previous post in our series: Introducing Supply Chain Forecasting or the next post in our series: Optimal strategies for logistics forecasting.
