Improving predictability in outcomes is valuable, but ensuring your supply chain is nimble enough to act on the data is even more so. With demand forecasting being complex, it is easy to lose supply chain visibility. We explore how predictive analytics and big data with human sentiments can add value and amplify their supply chain strategies.
What this blog is about:
Leveraging predictive analytics for forecasting and visibility
The importance of machine learning and intelligence combined with human touch for optimized decision making
The presence of data throughout the supply chain is vital to its evolution
The pandemic impact on the global economy has left supply chains reeling under various bottlenecks and pressures. 51% of the respondents in a global Reuters survey felt that the most perplexing challenge is the unpredictable nature of consumer demand.
Demand forecasting is hard. However, that does not mean that businesses can continue in a tricky supply chain world without visibility. It is certainly harder to run an enterprise without forecasts or with incorrect future predictions. Having random data is meaningless without insightful analytics.
In today’s fast-paced world, there are factors that affect the demand of a product. For retailers and manufacturers, identifying the demand of their merchandise and creating the necessary lead-times is becoming an incredibly complex challenge. Approaching a demand analysis with historical data and seasonal variations is no longer competent.
The drivers for growth are not simple factors like the price or quality of a product. Customer expectations and the buying capacity of the purchaser coupled with the cost of substitutes or complementary goods also play an important role. Brand identity, interactive marketing campaigns and differential lifestyles complicate a perfect trend analysis model.
So, how can one be able to plan more effectively and be resilient in their supply chain game? The trick here is to be adaptive and create plans for every possible situation. In theory, it is very easy to preach this concept of being in pace with the ever-changing world. However, how pragmatic is this solution and how does one put this into practice?
Concept of Predictive Analytics
With the rise of big data, businesses must invest in machine learning algorithms and use predictive analytics to enhance their demand forecasting approach. Of course, these sophisticated automated models will not give the full picture either, especially in a market run by the pandemic. Human involvement and emotional intelligence are also equally important.
Predictive analytics allows for simulation testing and provides an opportunity to optimize plans.
The use of predictive analytics gives a competent edge and competitive differentiation in these unpredictable economic conditions. Predictive analytics allows for simulation testing and provides an opportunity to optimize plans. The data churned out from here tends to be real-time and hence, more impactful.
Predictive analytics comes into the picture when future scenarios are uncertain. It is the use of data, statistical algorithms, and machine learning methods to identify the possibility of future situations based on historical data. The aim is to go beyond knowing what has happened to provide the best assessment of what will happen in the future.
Combining regular data analysis with human touch and giving context to these results will create meaningful decisions. Predictive analytics will also bring behavioral evaluation into perspective.
Predictive analytics as a market is projected to reach approximately $10.95 billion by 2022. According to a report issued by Zion Market Research, the compound annual growth rate is at 21% between 2016 and 2022.
Simple Examples of Predictive Analytics
Predictive analytics is used in every industry. It can answer questions like how much peanut butter to stock. The analysis can identify how much to sell a flight ticket for or how much demand would be there for a hotel room. Amazon shopping is a clear example of how your historical shopping trends are churned to predict what you would require in the future. It gives prompts to refill regular purchases into the cart and aligns your feed in line with your search history.
Every step of the way can use predictive analysis. It could range from identifying production capacity to setting up lead-times; from creating cross-sell opportunities and retaining customers; from finding cost-prices to creating a profitable business plan.
Virtually all predictive analytics analysts use tools created by external developers. Many such tools are tailored to meet the needs of specific enterprises. Major predictive analytics software providers include Acxiom, IBM, Information Builders, Microsoft, SAP, SAS Institute, Tableau Software, Teradata and TIBCO Software.
Decision trees, that rely on a tree-shaped diagram determine a course of action or show statistical probability, is the most used predictive analysis method. The branching method can also show every possible outcome of a particular decision and how one choice may lead to the next. Another cutting edge technique used in predictive analysis is neural networks. It is a set of algorithms designed to identify underlying relationships within a data set by mimicking the way a human mind functions.
Supply Chain Visibility With Predictive Analytics
Predictive analytics enhances customer experience all the way through to the delivery day. With more customers demanding immediate deliveries within a last-mile radius, predictive analytics provides more visibility and helps ensure reliable, and on-time arrivals.
In the shipping industry, predictive analysis can forecast potential maintenance issues and pinpoint optimal transport routes. It now plays a major role in ensuring that delivery schedules are met on time.
Data should be collected at every point of the way. Metrics and performance charts on lead-time, inventory storage factors, manufacturing requirements and shelving needs should be tracked. These should be monitored, tweaked, validated, and measured continuously until equilibrium is achieved. The data that is now visible should also be shared with relevant supply chain partners to ensure a more robust approach.
With the visibility that predictive analytics provides in terms of demand forecasting and inventory management, enterprises can build their supply chain. Decisions need not be created through assumptions; analytics provide the hard facts. It shows where the trend will change, where to cut costs, how to market, what to stock and how to ship. It tries to remove the inefficiencies.
This new age development of predictive analysis is the ultimate solution to create business continuity in an ever-evolving supply chain.