Why Using Last Week's Data Slows Supply Chain Response

What happens when data slows down supply chain management response? Is your company able to adapt quickly and respond to changes that affect supply chain and operational performance? Does the flow of information keep pace among sales and ops planning, demand planning, inventory planning, and supply planning? How are demand, inventory, and supply planners even able to perform analyses when even the smallest changes immediately makes the data they’re working with obsolete?

“Last week’s data” means less effective supply chain decisions

Let’s view the problem another way. Can you imagine making decisions for an upcoming road trip using weather, traffic, and road data from yesterday or a week ago? That's what supply chain planners are often forced to do. No one wants to look at last week's traffic report to plan a road trip for today, because traffic conditions can change dramatically from week to week, day to day, and even hour to hour. For effective trip planning, drivers benefit more from real-time traffic information that reflects current or very recent conditions.

Supply chain planning often follows the same pattern: Planners use data from a fixed point in time, then manually analyze it for days or even weeks to makes business decisions and determine next steps to take.

However, last week's demand, inventory, and supply report is a snapshot that reflects past conditions, not today’s situation. The current demand, inventory, and supply planning might be influenced by different factors such as equipment failures, weather, sudden economic shifts, accidents, supplier or logistical disruptions, or other events that were not present last week. Relying on outdated data in supply chain planning doesn’t allow a planner to respond appropriately to changes in demand, inventory, or disruptions in supply routes. The result could be overstocking, stock-outs, or production delays.

The unfortunate result of this is poor decision-making, making the plan less effective and more prone to substantial inaccuracies.

What's makes “last week’s data” ineffective?

Supply chains are often slow to adjust to issues due to several inherent characteristics and constraints:

Inertia and rigidity: Supply chains are typically designed for efficiency and stability rather than agility. They often involve long-term contracts, fixed schedules, and optimized processes, all of which can change slowly once established.

Long lead times: Many products require long lead times for sourcing raw materials, manufacturing, and transportation. These processes are planned well in advance, making it difficult to accommodate sudden changes without causing delays.

Complexity and interdependence: Supply chains involve numerous interdependent businesses, including suppliers, manufacturers, distributors, and retailers, as well as inter-departmentally among demand planning, inventory planning, and supply planning. Changing one part of the chain requires orchestration across the entire network, which can be time-consuming and difficult to execute quickly when done manually.

Limited visibility and communication: Information is often siloed across a supply chain. Delays or inaccuracies in communication between different entities can slow the response to changes. Without real-time visibility, companies may be unaware of changes until it's too late to respond effectively.

Capacity constraints: Suppliers and manufacturers often operate at or near full capacity to maximize efficiency. When a change occurs, there may not be enough excess capacity to handle it, leading to delays while additional resources are mobilized.

Inventory management: Companies often keep minimal inventory on hand to reduce costs, relying on just-in-time (JIT) strategies. When a change occurs, there may not be enough inventory available to meet new demands, causing delays while additional stock is sourced.

Transportation and logistics challenges: Shipping and transportation are typically scheduled in advance. Adjusting these plans on short notice can be difficult, especially if goods are already in transit or if there are logistical constraints like limited carrier availability or routing issues.

Regulatory and compliance barriers: In industries with strict regulatory requirements, any changes to orders may require new approvals or compliance checks, slowing down the process.

Cost considerations: Rapid adjustments often incur additional costs, such as expedited shipping, overtime labor, or sourcing from more expensive suppliers. Companies may weigh these costs against the urgency of the change, sometimes opting for slower adjustments to manage expenses.

Risk aversion: Companies may be hesitant to make rapid changes due to the risk of errors, which can lead to quality issues, customer dissatisfaction, or even regulatory violations. This cautious approach can contribute to slower adjustments.

All of these factors contribute to a less-than-ideal reaction time to changes in supply, inventory, and demand.

Impact of “last week’s data” runs deep

Supply chains require orchestration and agility across internal planning activities/processes. Demand planning, inventory planning, and supply planning are closely interrelated. They affect interdependent activities across the supply chain, and each one influences and relies on the others to ensure that the entire system functions effectively and efficiently. Demand planners, inventory planners, and supply planners are often overwhelmed with time-intensive manual processes.

Due to their interdependency, changes or data delays at any stage can cause supply chains to become a constant fire fight. However, when real-time data is available and applied, this interdependency ensures that the supply chain operates smoothly, balancing costs with service levels to meet customer needs.

• Demand planning sets the stage by predicting what needs to be produced or procured.
• Inventory planning ensures that products are distributed across the company network appropriately to meet demand while minimizing costs.
• Supply planning ensures the necessary resources and products are supplied to meet demand forecasts, considering the current inventory levels.

Each of these activities must be aligned and continuously adjusted in real-time in response to changes in the other areas; however, when performed with spreadsheets or manually, it could take up to three weeks or more, depending on the changes to the demand, to achieve an updated and accurate supply plan. Any change in demand will require a week's worth (or more) of adjustments in inventory and supply plans and can cause weeks of production delays. So, by the end of the month, it becomes a constant churn of events, and the planners seem to never seem catch up. They constantly struggle to strike the balance across the three core functions: demand planning, inventory planning, and supply planning.

How do companies solve the “last week’s data” problem?

There are now modern applications that enable real-time orchestration across all three functions: demand, inventory, and supply planning, speed up the flow of information to real-time, and enable agility and more effective decision-making. Manufacturing enterprises that leverage modern supply chain management platforms position themselves to create an agile, accurate supply chain. They create an ecosystem that abandons the strict view of planning focused on accuracy and execution, and instead emphasizes agility.

Gone are the days of sequential, linear processes, making way for planning and execution that happen simultaneously and bidirectionally – where execution responds to disruptions, and information flows automatically in real-time. The result is an agile, responsive supply chain and manufacturing enterprise.