Analytics can help you create a transparent and resilient supply chain

Companies are resuming global operations after the pandemic, and the effects of prolonged periods of supply-chain disruption have been becoming more evident. COVID-19 has impacted some of the busiest supply chains in the world and has forced customers to adopt new lifestyles and behavior that could well become the norm after the initial pandemic. PWC found that consumers are becoming more digitally savvy over the six-month period between October 2020 and March 2021. Around 44% of respondents used a smartphone to purchase a product.

This unexpected and sudden change in customer behavior is expected to disrupt brands’ engagement with customers. Companies must look for ways to cater to customers wherever they are. To do this successfully, they will need to reinvent their demand planning and forecasting models. To achieve supply chain inventory management success, it is important to monitor and reduce model forecasting errors.

Companies must pay more attention to inventory-data visibility and channel visibility due to the abrupt shift in demand trends based on goods and services. To narrow down product offerings, tighten control and increase visibility on stocking inventory, and minimize wastage, companies must analyze channel data from promotions, stores, events, and engagement. This is where supply-chain analytics can make a difference.

Use Analytics to Gain More Comprehensive Supply Chain Visibility

Demand forecasting in supply chain and inventory management are already well-established concepts, especially considering the trend towards ever-growing product ranges. A typical inventory management strategy revolves around creating an inventory buffer and holding safety stock to deal with potential supply chain disruptions or forecast errors. This model does have a downside, as the pandemic demonstrated. Companies were held in excess stock, which led to price discounts and write-downs in the worst-case scenario.

Companies can use predictive supply chain forecasting to create a mathematical model that accurately represents the demand or supply trends they are trying to understand. It may be necessary to test several forecasting models in order to find the best one for the specific situation. This model uses historical demand data and micro- and macroeconomic trends to forecast future events and trends. This model will be more successful if companies have large streams of high-quality data. It increases the likelihood of a precise forecast.

Companies can reap multiple benefits from predictive supply chain analytics:

To reduce working capital, increase cash flow 

Customer satisfaction can be improved by meeting the diverse needs of customers 

Maximize machine utilization and worker productivity in procurement channels 

Automate and optimize your entire fulfillment process quickly 

Reverse logistics optimization optimizes stock management to reduce costs and keep stocks. 

Ensure a smooth supply chain planning 

Inventory damage and scraping can be reduced by reducing wastage and scraping 

Calculate the quantity of goods that a facility will need to eliminate stockouts or redundant stocks 

Sigmoid’s demand-provisioning solution for one of its customers is a great example of how supply chain analytics can be used to help companies increase sales, reduce demand planning cycles, minimize stock-out risk, and optimize inventory management.

A leading cosmetics company needed a reliable demand forecasting system that could be used for different product categories. The current solution didn’t provide a single source of information that could be used by different business departments. The company needed to reduce the forecasting cycle time and improve efficiency.

Sigmoid incorporated ML algorithms into their forecasting system to increase coverage, Weighted Absolute Error (WAPE), and Forecast BIAS. To get the base sales, the process required removing seasonality and trends from the products. The ML algorithm analyzed data patterns that included high volatility, nonlinearity, extreme value, and so forth.

Although the benefits of using supply-chain analytics are clear, there are some obstacles that companies may encounter while fully utilizing analytics. An inability to obtain timely data, inaccurate data, or legacy data processing methods can all be major obstacles that hinder the success of an analysis project.

Here are some ways companies can overcome the above-mentioned challenges and make supply chain analytics projects tangible.

Companies must ensure that all stakeholders have the opportunity to benefit from supply chain analytics in order to achieve tangible results. When the mandate is given from the top, it becomes easy to get buy-in from employees and process managers for analytics projects. 

Companies must ensure that their supply chain analytics management system is easy to use for employees in order to smoothly implement it. Complex systems are more likely to be difficult to use. 

Companies must be able to collect, archive, and analyze all data from the value chain to create the most efficient supply chain analytics model. 

Any successful implementation of analytics requires time. Only companies can trust the data provided by analytics to achieve effective results. A closed-loop management framework that uses KPIs to accurately reflect the state of a supply chain environment is one way to increase trust in analytics systems. 

Control Towers simplify predictive supply chain planning

Companies need a supply chain control tower (SCCT) to create a data network to share real-time data analytics from their supply chains. SCCT is essentially a hub for supply chain forecasting, leveraging data intelligence (IoT) and the Internet of Things. High-end sensors are used to track goods in real time in this model. Some raw materials are required to be heated in order to make products. Smart sensors are useful in such situations to monitor environmental conditions like temperature, humidity, and light intensity to make sure raw materials do not become contaminated. Companies can harness the power of these control towers to quickly access real-time analytics and get actionable insights.

IoT sensor data plays an important role in the operation of supply chain control towers. Data interpretation errors can be reduced, aggregated data is faster compiled, and predictive analysis can be used to expand upon that information.

Conclusion

Companies can minimize supply chain risk by using predictive supply chain analytics. There are many parts of the supply chain that interact with stakeholders. A disruption could throw off the balance of these touchpoints, which can impact customers and ROI. It is crucial to know when and how risks occur in order to effectively manage risk. Supply chain Companies must use predictive analytics to ensure that everything is on track. This allows for complete visibility into supply chain operations.

Companies have had to adapt to the pandemic as it has been a stressful time. Companies with solid data foundations have been able to adapt faster by adopting data-driven strategies that work for them. If companies are proactive enough, data and analytics can continue to strengthen organizational resilience in this new normal.

Click to learn more about Sigmoid’s supply-chain analytics solutions.

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