adminSupply chains in Mumbai are intricate webs that keep the city’s businesses running at a lightning pace. Coordinating deliveries across Mumbai’s bustling streets, with traffic jams around every corner, is no easy feat. But getting supplies from point A to point B efficiently is crucial for companies to satisfy customers and stay ahead of the competition. This is where data science comes in. By harnessing the power of data, businesses can gain unprecedented visibility and control over their supply chains.
What is Data Science?
Data science refers to the interdisciplinary field of extracting knowledge and insights from data through statistical analysis, machine learning, and other computational techniques. Professionals in this field, known as data scientists, generate predictive algorithms and models by processing vast amounts of structured and unstructured data from various sources like databases, sensors, and online transactions.
While data science has applications across many industries, it is particularly well-suited to optimize supply chain operations. Let’s explore some of the key ways data science is transforming supply chain management in Mumbai.
Improving Demand Forecasting with Data Science
Accurate demand forecasting plays a crucial role in optimizing inventory levels and planning fulfillment operations. One of the most popular techniques used by data scientists is time series forecasting.
Time series forecasting uses historical demand data – such as daily or monthly sales volumes over several years – to identify trends and seasonality patterns. These patterns are then used to predict future demand values. For example, sales may be consistently higher around festival seasons in India. By analyzing multiple years of demand data, these seasonal effects can be modeled mathematically.
Other factors like price changes, marketing campaigns, economic conditions are also important influencers of demand that time series models account for. External data sources providing information on these explanatory variables are fed into the models as well.
More sophisticated time series techniques like ARIMA (autoregressive integrated moving average) analyze lag effects – the correlation between demand in the current period and demand in previous periods. This helps capture changing demand levels even without seasonal patterns.
Machine learning algorithms like LSTM (long short term memory) neural networks are also increasingly used for time series forecasting. They can model highly non-linear and complex demand timelines with high accuracy.
A large Mumbai-based retailer with 50 stores deployed a time series forecasting solution developed by data scientists. It analyzed 3 years of daily store-level sales data totaling over 150,000 data points. Demographic info from the retailer’s loyalty program and economic indicators from government sources were also incorporated.
Model Training and Evaluation
The data scientists tested various time series models and selected the optimal ARIMA and neural network variants. Patterns of weekly, monthly and festive season seasonality were discerned. The trained models were evaluated on validation data to optimize hyperparameters.
Implementation and Results
The refined models forecast demand 4 weeks in advance at an item-store level, down to SKU level. They were integrated into the company’s planning systems. This allowed for streamlined replenishment schedules aligned to demand signals.
As a result, out-of-stocks reduced by 20% while excess inventory carrying costs decreased 15%. Overall, demand forecasting accuracy went up over 15% compared to simple averages. The actionable insights from time series forecasting proved highly valuable for retailers seeking to gain an edge in the ultra-competitive Mumbai markets.
Optimizing Inventory Management with Data Science
Optimal inventory management is key to ensuring product or component availability while avoiding excess costs of overstocking. Data science techniques analyze inventory levels, reorder points, lead times, product substitution patterns, and demand forecasts to determine the best inventory policies.
A pharmaceutical company was facing inventory pile-ups of certain drugs while facing shortages of other critical medicines. Data scientists built predictive models by combining 5 years of sales and inventory data with manufacturing schedules, supplier reliability ratings, and medical guidelines. This helped optimize the company’s inventory targets and reorder quantities, significantly improving their service level at 15-20% lower inventory investment.
Enhancing Logistics Efficiency with Data Science
Transportation accounts for a major portion of supply chain costs. Mumbai’s dense traffic can balloon delivery timelines and fuel expenditures. Data science comes to the rescue by studying historical traffic patterns, road conditions, transit times, and location data to streamline logistics operations.
Route Optimization Models: By mapping delivery addresses and traffic insights, these models determine the most efficient route sequence and mode of transport to minimize distance traveled and reduce costs.
Demand-Driven Dispatching: Based on predicted demand hotspots, data science optimizes the schedule of vehicle dispatch to fulfill orders in the least number of trips. This benefited a food delivery firm in cutting 20% costs through right-sizing their delivery fleet.
Supply Chain Visibility with Data Science
Data is revolutionizing supply chain visibility. collecting IoT sensor data from assets like containers, vehicles, and machinery provides real-time monitoring of location, temperature, vibration, and other parameters. This is integrated with transaction records, purchase orders, inventory levels to build a digital twin of operations.
Any anomalies or disruptions are detected by advanced anomaly detection algorithms. With end-to-end visibility, companies can proactively address issues, reroute shipments, and gain a situational understanding of their extended supply network spread across the globe. They no longer remain reactive to problems.
Leveraging data science for visibility, an electronics manufacturer gained control over their sprawling supplier base in China. They traced quality issues to specific raw material shipments, helping identify and blacklist non-compliant vendors proactively.
Managing Supply Chain Risks with Data Science
Data science risk analytics techniques scan enormous datasets for predictive signals and patterns that can foresee potential risks to the supply chain. They analyze past disruptions, the impact of external uncertainties, and inter-dependencies between entities.
For example, analyzing 5 years of supplier performance data using machine learning algorithms, a Mumbai-based auto ancillary firm identified two tier-2 vendors who showed signs of financial distress. Timely intervention via bank guarantees and upfront payments helped mitigate the risks of these suppliers going bankrupt, averting a major production breakdown.
Case Study: Data Science in Action
Here are a couple of examples where Mumbai companies have leveraged data science to streamline supply chain operations:
A leading retailer analyzed purchase histories and demographics of over 1 million customers to improve demand forecasting accuracy for clothing by 25%. This helped reduce stock outs during peak seasons while lowering overall inventory levels.
An electronics manufacturer installed IoT sensors in shipping containers to monitor temperature, humidity and shocks during international freight transportation. Detecting any anomalies via predictive algorithms, they were able to address quality issues on certain product batches before delivery, avoiding costly returns and replacement costs.
The Future of Data Science in Supply Chain Management
As supply chains become even more globally distributed and complex, the role of data science will continue expanding. Emerging areas include prescriptive analytics, cognitive technologies and blockchains for track and trace.
By skilling up on a comprehensive data scientist course and domain-focused data science course in Mumbai for supply chain management, professionals can tap into the vast potential of optimizing Mumbai businesses’ logistics using cutting-edge data science techniques. This is a lucrative and in-demand career with immense scope for innovation and problem-solving.
Name: ExcelR- Data Science, Data Analytics, Business Analytics Course Training Mumbai
Address: 304, 3rd Floor, Pratibha Building. Three Petrol pump, Lal Bahadur Shastri Rd, opposite Manas Tower, Pakhdi, Thane West, Thane, Maharashtra 400602
Phone Number: 09108238354
