Bringing efficiency in Demand Planning process through Machine Learning

Forecasting with Machine Learning

The adoption of big data analytics in demand forecasting is very limited because companies continue to struggle with managing and analyzing structured data. Data Types and volume continue to increase driven by multiple factors like social network sites, Internet of Things (IoT) and real world data in health sector and other government agencies for example.

Study shows Industrial companies are utilizing the big data analytics more than consumer goods manufacturers. Organizations without analytics capabilities might face elimination threat from the ones who are making Big Data analytics a competitive advantage.

Challenges in Demand Planning

Historically business have faced below challenges across product, processes and business response while doing their demand planning

1. Seasonality of Product demand

2. Highly sporadic and irregular demand

3. New product and store introductions

4. Promotional Demand

5. Inadequate understanding of historical data

6. Limited statistical knowledge among the planners

7. Execution of non-standard forecasting processes

8. Wrong usage of statistical methods

However, the fast changing scenario of demand planning landscape is influenced by sheer availability of true data with the help of Technology. This has added more astute complexities in the process-

1. Understanding non-linear effect of causal factors

2. Application of correct forecasting models

3. Limited knowledge on applying external factors

Impact on Business

Business suffers severely with a poor forecasting capability within the organization. Impacts of it ranges from operational inefficiency, cost implications and service issues-

1. Forecast inaccuracy

2. Non-optimal inventory

3. Excess clearance and markdowns

4. Out of stock and lost sales

5. Inadequate safety stock

How Machine Learning fits here

Machine learning concepts have been there since 80’s but it is really getting pace and momentum in recent years. Machine learning techniques are focused on bringing new solutions through better analysis of data available and taking cognizant decisions.

1. Machine learning algorithms are capable of building automated analytical models and algorithm which can iteratively learn from the data and independently adapt to new data.

2. They do not take cue from a pre-defined modeling technique and rather look at data in more holistic way which is bring a clear advantage in presence of complex non-linear interactions

3. If used properly, can better respond to the rise of new patterns and behaviors, allowing a real-time analysis

4. Moreover, in a natural way, provide solution that tend to reduce variance and guarantee robustness

Benefits of Machine learning

1. An integrated, end-to-end platform for the automation of the issue to outcome process

2. Automated ensemble model evaluation to identify the best performers

3. Efficient forecasting with comparison of different machine learning techniques to select the optimum method

4. Unleash the power of big data and Real time analytics

5. Self-Automated feature selection modeling using non-linearity

Milk-Run Consultancy

https://milk-run.co.in