Harnessing Analytics to Get More Granular Supply Chain Visibility
In reality, demand forecasting in supply chain and inventory management is already an established concept – especially in the light of the trend toward ever-growing product assortments. Traditionally, a typical inventory management strategy has revolved around holding safety stocks and creating an inventory buffer to address potential errors in forecasting or supply chain disruptions. But as the pandemic proved – this model has a downside. Sudden lockdowns meant that companies were holding excess stock which resulted in price discounts or write-downs in worst-case scenarios.
With predictive analytics-driven supply chain forecasting, companies can create a mathematical model that closely represents the demand and supply trends they are trying to understand. This may entail testing several forecasting models to select the one that most closely represents the exact situation at hand. This analytical model will simply leverage demand forecasting, historical data, and both micro and macro-economic trends to predict future events and demand trends. For this model to be successful, it’s also important for companies to harness large streams of high-quality data, since this increases the probability of an accurate forecast.
By implementing predictive supply chain analytics, companies can derive multiple benefits such as:
- Increase cash flow to accommodate the working capital reduction
- Enhance customer satisfaction by catering to diverse consumer needs
- Optimize machine utilization and worker productivity within procurement channels
- Quickly automate and optimize the entire fulfillment process
- Optimize reverse logistics to minimize costs and maintain stock
- Drive effective supply chain planning
- Reduce scraping, wastage, and damage to inventory
- Predict the exact amount of goods a facility requires to eliminate stockouts and redundant stocks
Sigmoid’s demand forecasting solution for one of its clients is an excellent example of how proper utilization of supply chain analytics can help companies drive sales, shorten demand planning cycles, reduce the risk of stock-outs and optimize inventory management.
The client – a leading cosmetics company wanted an effective demand forecasting solution that would work for diverse product categories. The existing solution did not offer a single source of information that could be consumed by different business teams. The company wanted to shorten the current forecasting cycle and make the overall process more efficient. Sigmoid enhanced their existing forecasting solution by leveraging ML algorithms to improve Weighted Absolute Percent Error (WAPE), Forecast BIAS, and coverage. The process involved removing the seasonality and trend from the products to get the base sales. The ML algorithm analyzed different data patterns such as high volatility, non-linearity, extreme values, and so on.
While the benefits of leveraging supply chain analytics are obvious, there are also several obstacles that a company may face while fully utilizing analytics. Lack of timely data, inaccurate data, and legacy data processing methods are some of the major hurdles that often undermine the success of an analytics project.
The following are some of the ways through which companies can offset the challenges mentioned above and ensure that supply chain analytics projects yield tangible results:
- To achieve tangible results from supply chain analytics, companies need to ensure that all process stakeholders get an opportunity to leverage the analytics systems. Getting buy-in from process managers and employees for analytics projects becomes seamless when the mandate comes from the top.
- In order to seamlessly implement a supply chain analytics model, companies will have to make sure that the analytics system is simple and intuitive enough for all employees to grasp. The more complex the system, the more likely it will create a barrier in usage.
- To develop the most effective supply chain analytics model, companies need to build on the requisite capabilities to collate, archive, and analyze data generated from all the key processes in the value chain.
- Time can be a defining factor in any successful analytics implementation project. Companies can get effective results only if they trust the numbers that analytics provide. One way to build trust in the analytics system is to create a closed-loop change management framework based on KPIs that accurately depict the current state of a supply chain ecosystem.