Supply Chain Data Flow: Where Do Problems Begin?
While developing a manufacturing ERP, there was a persistent inconsistency in reporting shipment delays. Initially, I tried to optimize database queries, but the problem was deeper. At the root of these delays was an unhealthy data flow throughout the supply chain. Critical information, such as when the raw materials needed to produce a product would arrive, when production would be completed, and when the product would be ready for shipment, was getting lost or incorrectly processed at different points in the system. This situation reduced operational efficiency and increased costs.
Such problems require not only software developers but also a fundamental understanding of operational processes. In the real world, an ERP system is not just a pile of code; it's a complex set of flows that reflects an organization's operations. Every step of the supply chain – supplier selection, raw material intake, production planning, inventory management, quality control, packaging, and finally, shipment – is tightly interconnected. Any disruption in any link of this chain can affect the entire system.
The First Point of Contact: Data Entry and Consistency
Most often, problems start at the very first point of data entry. Entering a supplier order into the system, recording a goods receipt, or creating a production order... When these operations are done manually, the probability of human error increases. Inputs like incorrect product codes, missing quantities, or erroneous date information directly affect all subsequent operations. For example, entering a raw material with the wrong unit (like kilograms instead of pieces) can lead to completely wrong results in production planning.
In one case we experienced with our manufacturing ERP, a key raw material stock was consistently showing as insufficient. Upon detailed investigation, we realized that our colleague in goods receipt had recorded the incoming 10 pallets of product as a total of 10 pallets, rather than entering each pallet individually. This was a simple manual entry error, but it directly impacted the next day's production plan, preventing the planned production of 500 units. These types of errors are common in daily operations and can lead to significant cost increases.
ℹ️ Best Practices for Data Entry
- Tighten data validation mechanisms: Check the format, range, and logical consistency of data entered into fields.
- Detail user authorization: Ensure each user can only access and modify data within their authority.
- Increase automation: Reduce manual data entry wherever possible. Use barcode scanners, RFID tags, or integrated systems.
- Invest in training: Provide regular training to users to ensure they use the systems correctly and completely.
The Gap In Between: Integration and Communication Problems
The supply chain doesn't just consist of a company's internal processes; it involves multiple stakeholders: suppliers, manufacturers, logistics companies, and customers. Data exchange between these stakeholders often happens through different systems. In situations where integration is weak or non-existent, serious disruptions in data flow occur. When transferring information from one system to another becomes manual, delays and errors are inevitable.
At a large e-commerce company where I once worked, a custom middleware was used to transfer orders to the ERP system. Orders first landed in the e-commerce platform, then to the middleware, and then to the ERP. During a weekend incident, the e-commerce platform received an unexpected update, and its API changed. Since the middleware couldn't immediately detect this change, orders couldn't be transferred to the ERP. As a result, on Monday morning, chaos ensued with hundreds of orders missing and incorrect information being given to customers. These types of integration issues are more common, especially when systems with different technology stacks come together.
# Simple API integration example (erroneous scenario)
import requests
import json
def send_order_to_erp(order_data):
erp_api_url = "https://api.example-erp.com/v1/orders"
headers = {"Authorization": "Bearer YOUR_API_KEY"}
try:
response = requests.post(erp_api_url, data=json.dumps(order_data), headers=headers)
response.raise_for_status() # Raises an exception for HTTP errors
print("Order sent to ERP successfully.")
return response.json()
except requests.exceptions.RequestException as e:
print(f"Error sending order: {e}")
# Error logging or retry mechanisms can be added here
return None
# Example order data
order = {
"order_id": "ECOMM12345",
"customer_id": "CUST987",
"items": [
{"product_id": "PROD001", "quantity": 2},
{"product_id": "PROD005", "quantity": 1}
],
"order_date": "2026-05-20T10:00:00Z"
}
# If the API changed and this code wasn't updated, the send_order_to_erp function will error out.
# send_order_to_erp(order)
In this scenario, the raise_for_status() method of the requests library is used to catch error codes from the API. If the API's endpoint changes or authorization details become invalid, this function will raise an error, and the order cannot be transmitted to the ERP. It is critical to establish a robust error management and monitoring mechanism for such situations.
Production Planning and Inventory Management Dilemma
One of the most critical stages of the supply chain is production planning. An accurate production plan prevents idle capacity and ensures timely fulfillment of customer demands. However, this planning must be based on accurate and up-to-date inventory information. If there's a discrepancy between the stock quantity shown in the system and the physical stock quantity, the production plan can go awry. This can lead to either overproduction (increasing inventory costs) or production stoppages due to insufficient raw materials (delivery delays).
At a manufacturing company, we were using an AI model to optimize weekly production plans. The model determined the most efficient production route using current inventory data from the ERP. However, for a month, the plans generated by the model were inconsistent. After detailed analysis, we discovered a bug in the inventory management module that was recording returned products in a separate "returns stock" area instead of deducting them from the main stock. This caused the available stock quantity shown in the ERP to be lower than the physical stock. The AI model, assuming the current stock was accurate, was making incorrect plans. Fixing this bug and synchronizing the returns stock with the main stock took about 4 days.
⚠️ The Delicate Balance Between Inventory Management and Production Planning
The synchronization between these two modules is the backbone of the supply chain. Even a small deviation in stock can lead to large fluctuations in production plans. Especially in industries with variable demand and short lead times, perfect synchronization is vital.
Lack of Reporting and Visibility
Many of the problems experienced throughout the supply chain stem from a lack of adequate reporting and visibility. When there are no clear answers to questions like which product is where, when it will be delivered, or at which stage there's a delay, proactive measures become impossible. Standard ERP reports often provide general information but lack the depth needed to analyze the dynamic nature of the supply chain and potential bottlenecks.
On a project for one of our clients, we wanted to improve their shipping processes. Their self-generated reports only showed completed shipments. However, they didn't provide clear information about the status of products that were in transit, waiting at customs, or being prepared in the warehouse. Therefore, understanding why a product's delivery was delayed required manually gathering information from multiple sources, which led to an average of 3-4 hours of overtime.
To solve this problem, we developed a custom dashboard that tracked the lifecycle of an order within the ERP. This dashboard visualized every stage, from the moment the order was received, to the creation of the production order, the preparation of raw materials for shipment, the packaging of the product, and finally, its dispatch. It also showed the expected times and current delays for each stage. This allowed us to instantly see which orders were at risk. For instance, we could see that an order was waiting for 2 days at the raw material procurement stage and contact the relevant supplier to resolve the issue.
# Example of log analysis with Bash command (detecting delays)
# Default log file: /var/log/erp/shipment_tracker.log
# Using grep and awk to find delayed orders
# Example log format:
# 2026-05-20 09:15:32 INFO [SHIPMENT_TRACKER] Order ID: ORD7890, Status: WAITING_FOR_MATERIAL, Expected completion: 2026-05-19 17:00:00
LOG_FILE="/var/log/erp/shipment_tracker.log"
CURRENT_DATE="2026-05-20" # Current date
echo "Orders expected yesterday but still pending:"
awk -v current_date="$CURRENT_DATE" '
$3 == "ORDER_ID:" { order_id = $4 }
$3 == "STATUS:" && order_id {
status = $4
# If the order status is "WAITING_FOR_MATERIAL" or similar and the completion date has passed
if (status ~ /WAITING|PENDING/) {
# Find the expected completion date
match($0, /Expected completion: ([0-9]{4}-[0-9]{2}-[0-9]{2} [0-9]{2}:[0-9]{2}:[0-9]{2})/, completion_time)
if (completion_time[1]) {
if (completion_time[1] < current_date) {
print "Order ID: " order_id ", Status: " status ", Expected: " completion_time[1]
}
}
}
order_id = "" # Reset order_id
}
' "$LOG_FILE"
This script scans the records in the shipment_tracker.log file and lists orders that have passed their delivery date but are still in a waiting or pending status. Simple log analyses like this can help quickly identify the root cause of a problem. The awk command is very powerful for text processing and is ideal for such tasks.
Cost Increases and Lost Opportunities
The ultimate consequence of errors in the supply chain data flow is direct cost increases and lost opportunities. Delayed deliveries lead to customer dissatisfaction, which in turn reduces brand value in the long run. Holding excess inventory increases storage costs and ties up capital. Production stoppages mean wasted labor and machine costs, as well as lost revenue from unsold products.
While examining the ERP system of a textile company, I found that their annual inventory costs were 15% higher. The primary reason for this was the inability to properly manage stock levels of fabrics used in production. Frequent last-minute orders were placed, which increased express shipping costs and led to purchases from suppliers at higher prices. Analysis revealed that lack of visibility in the raw material supply chain and inaccurate forecasting were causing this situation.
To resolve the issue, we began sharing weekly demand forecasts in closer collaboration with suppliers. We also implemented a mechanism that generated automatic alerts when stock levels in the system dropped below a certain critical point. As a result of these changes, we achieved approximately a 10% reduction in annual inventory costs and a 30% decrease in the number of urgent orders. This not only resulted in cost savings but also improved customer satisfaction.
💡 The Relationship Between Efficiency and Cost
Increased efficiency in the supply chain directly translates to reduced costs. Every delay, every error negatively impacts cash flow. Therefore, supply chain optimization is not just an operational improvement but also a strategic financial investment.
Conclusion: A Data-Driven and Integrated Approach
The way to overcome errors in supply chain data flow is through a data-driven and integrated approach. Systems need to be designed not only from a software perspective but also by considering operational processes. Consistency and visibility must be ensured at every step, from data entry to integration, production planning, inventory management, and reporting.
This often requires reviewing and improving existing systems or integrating them with new ones. Automation, data validation, user training, and strong inter-stakeholder communication are the cornerstones of this process. It's important to remember that ERP systems are just tools; what truly matters is effectively using these tools to seamlessly connect all links of the supply chain. In future posts, I will examine these integrations in more detail.
