How to Unlock Better Demand Forecasting For Pharma Retail

Beyond Weather Forecasting: How Climate Data is Shaping Demand Planning in Retail and Pharma

Retail and pharmaceutical industries have always been sensitive to seasonal changes and weather patterns, but simple weather forecasting is not enough today. In addition to traditional weather parameters, real-time climate data is becoming increasingly crucial in inventory and supply chain management.

‍

This article explores how businesses can use climate intelligence to improve demand forecasting and optimize inventory levels, reducing waste and enhancing efficiency.

‍

Climate’s Influence on Retail Demand

Across industries, seasonal changes have influenced market demand forever. However, there is a lack of precise demand forecasting that is critical to survive in the highly competitive and evolving retail and pharmaceutical markets. While traditional demand forecasting relies heavily on historical sales data and market trends, there's an untapped data source that offers unprecedented accuracy: real-time climate intelligence.

‍

Climate intelligence presents a new frontier of business intelligence that can transform companies' real-time decisions.

‍

The kind of accurate, hyperlocal, and real-time environmental data we have built at Ambee — air quality, pollen, extreme weather, forest fire, natural disasters, and weather condition data— can optimize business models by providing hyper-accurate demand forecasts.

‍

The Role of Ambee’s Climate Data in Pharma Demand Forecasting

Pollen and Allergy Medication Demand

Pharmaceutical companies face recurring challenges in accurately forecasting demand for seasonal drugs due to regional and temporal pollen-level fluctuations. By integrating Ambee’s Pollen Data API, pharmaceuticals can optimize their supply chains by adjusting production schedules and distribution strategies based on this data, ensuring that products are available where and when needed.

‍

This level of granularity can inform decisions about drug production, marketing campaigns, and supply chain adjustments, ensuring that pharmacies and healthcare providers are well-stocked ahead of allergy season.

‍

Python Code Example for Pollen Data Integration

import requests
import pandas as pd

# Ambee Pollen API
api_key = "your_ambee_api_key"
url = "https://api.ambeedata.com/latest/pollen/by-lat-lng"
params = {
'lat': '40.7128', # New York latitude
'lng': '-74.0060', # New York longitude
'apikey': api_key
}

# Request pollen data
response = requests.get(url, params=params)
data = response.json()

# Extracting data
pollen_levels = data['data']
pollen_df = pd.DataFrame(pollen_levels)
print(pollen_df.head())

‍

Predictive Analysis for Antihistamine Demand

Once we have the pollen data, we can correlate it with historical sales data of allergy medications and build a predictive model.

from sklearn.linear_model import LinearRegression

# Sample sales data
sales_data = {'date': ['2024-09-01', '2024-09-02', '2024-09-03', '2024-09-04'],
'antihistamine_sales': [1500, 1700, 1800, 2100]}
sales_df = pd.DataFrame(sales_data)

# Merging pollen data and sales data
merged_df = pd.merge(pollen_df, sales_df, on='date')
# Building a linear regression model
X = merged_df[['grass_pollen', 'tree_pollen', 'weed_pollen']]
y = merged_df['antihistamine_sales']

model = LinearRegression()
model.fit(X, y)

# Predict sales based on pollen levels
predicted_sales = model.predict([[100, 50, 200]]) # Example pollen levels
print(f"Predicted antihistamine sales: {predicted_sales[0]}")

‍

Visualization

import matplotlib.pyplot as plt
plt.scatter(merged_df['grass_pollen'], merged_df['antihistamine_sales'], color='gr
plt.plot(merged_df['grass_pollen'], model.predict(merged_df[['grass_pollen', 'tree
plt.title('Grass Pollen vs Antihistamine Sales')
plt.xlabel('Grass Pollen Levels')
plt.ylabel('Antihistamine Sales')
plt.show()

‍

Air Quality and Respiratory Drug Demand in Pharma

Ambee’s Air Quality API provides hyperlocal air quality readings, including data on pollutants like PM2.5 and PM10, providing pharmaceuticals with crucial insights to anticipate surges in respiratory-related illnesses. This will lead to efficient medical inventory management for critical illnesses like asthma or chronic obstructive pulmonary disease (COPD).

‍

The demand for inhalers and other respiratory aids increases significantly in periods of poor AQI. By integrating precise, hyperlocal AQI data into their forecasting models, pharmaceuticals can adjust production schedules and supply chains to meet the anticipated increase in demand.

‍

Python Code Example for Air Quality Data Integration

# Ambee Air Quality API
aqi_url = "https://api.ambeedata.com/latest/air-quality/by-lat-lng"
params = {
'lat': '12.9716', # Bangalore latitude
'lng': '77.5946', # Bangalore longitude
'apikey': api_key
}

# Request air quality data
response = requests.get(aqi_url, params=params)
aqi_data = response.json()

# Extracting AQI data
aqi_values = aqi_data['stations']
aqi_df = pd.DataFrame(aqi_values)
print(aqi_df.head())

‍

Predictive Analysis for Respiratory Drug Demand

# Sample respiratory drug sales data
respiratory_sales_data = {'date': ['2024-09-01', '2024-09-02', '2024-09-03', '2024
'inhaler_sales': [1200, 1300, 1500, 1800]}
respiratory_sales_df = pd.DataFrame(respiratory_sales_data)

# Merging AQI data and sales data
merged_aqi_sales_df = pd.merge(aqi_df, respiratory_sales_df, on='date')

# Building a linear regression model
X_aqi = merged_aqi_sales_df[['PM2.5', 'PM10']]
y_aqi = merged_aqi_sales_df['inhaler_sales']

model_aqi = LinearRegression()
model_aqi.fit(X_aqi, y_aqi)

# Predict sales based on AQI levels
predicted_inhaler_sales = model_aqi.predict([[150, 100]]) # Example AQI levels
print(f"Predicted inhaler sales: {predicted_inhaler_sales[0]}")

‍

Visualization

plt.scatter(merged_aqi_sales_df['PM2.5'], merged_aqi_sales_df['inhaler_sales'], co
plt.plot(merged_aqi_sales_df['PM2.5'], model_aqi.predict(merged_aqi_sales_df[['PM2
plt.title('PM2.5 vs Inhaler Sales')
plt.xlabel('PM2.5 Levels')
plt.ylabel('Inhaler Sales')
plt.show()

‍

Weather Data for Retail Demand Forecasting

Ambee’s Weather Data API provides hyperlocal, real-time weather data that allows retailers to plan their inventory based on upcoming temperature shifts, rainfall, or extreme weather events, avoiding stock shortages or excess inventory.

‍

With such accurate predictive tools, retailers can drive timely sales while ensuring optimal inventory levels. They can easily plan their winter gear inventory around upcoming snowstorms, and ice cream stores can ramp up inventory in hot, sunny periods.

‍

Code Example for Weather Data Integration

# Ambee Weather API
weather_url = "https://api.ambeedata.com/latest/weather/by-lat-lng"
params = {
'lat': '40.7128', # New York latitude
'lng': '-74.0060', # New York longitude
'apikey': api_key
}

# Request weather data
response = requests.get(weather_url, params=params)

‍

Companies can use this analysis to calculate inventory risk and make informed decisions on whether to shift inventory to safer locations or preemptively reroute shipments to minimize the impact of natural disasters.

‍

Future-Proofing Supply Chains with Climate Data

In an era of climate emergency and increasing environmental disruptions, incorporating climate intelligence into supply chain planning is no longer optional—it’s essential. By leveraging Ambee’s real-time climate data, businesses can enhance their forecasting accuracy, minimize risks, and ensure a smoother flow of goods through their supply chains.

‍

Whether in the retail industry or pharmaceuticals, the ability to predict demand based on climate factors gives you a competitive edge. With advanced predictive models and climate-driven insights, businesses navigate the uncertainties of today’s global marketplace and ensure they are prepared for whatever comes their way.

‍

Unlock the full potential of your supply chain with Ambee’s APIs. Discover how our real-time climate intelligence can transform demand forecasting, inventory management, and risk mitigation strategies. Get in touch today to explore tailored solutions to meet your unique business needs. Let’s build a smarter, more resilient supply chain— together!