Earlier in the day, demand planning was a guessing game that relied more on intuition, instinct, and inclination. But with the advent of the information economy and Artificial Intelligence (AI), this process became data-driven. Companies that leveraged the data they collected to enable more accurate demand planning, distinguished themselves from their competitors.
However, the pandemic upended all processes that involved manufacturing, supply, and delivery of goods. With stores either running out of basic necessities or remaining closed for months on end, the pandemic provided us with unparalleled ‘uncertainties’, amplified further by the economic downturn.
Food & Beverage giants like Kellog’s reported on the state of breakdown in supply chain management. The unavailability of raw materials and changing consumer patterns heavily impacted demand.
This scenario raises the question: “How do we build systems that enable better demand planning for manufacturers, and what are the processes that support these?”
Why do you need new-age demand sensing to solve modern problems?
Demand planning in the traditional sense relied heavily on a combination of limited and biased data and gut instinct. However, these models have tremendously faltered, when they have not altogether failed because of superior intelligence. Conditions of erratic demands, long-period metrics, and unavailability of raw materials have brought forth a plethora of problems, such as –
- Trouble Managing Capacity: With high peaks and troughs in demand for goods, capacity management has become a key concern.
- Underutilization of resources: With unpredictable demand, accurate deployment of employees, as well as capital management, becomes incredibly hard.
- Absence of Real-Time Data: Delayed delivery of performance metrics and consumer behavior sets off a chain reaction of excess, wastage, and loss of profits.
A common trend underlying these problems is ‘unpredictability’. If the parameters affecting demand and supply can be anticipated, these errors can be addressed. Not limiting this ability to just rectifying errors, analytics can also be leveraged into demand forecasting and profit generation.
This is possible by planning product mix, and product sales forecast effectively via demand sensing technologies.
Execute high-performing product mix & sales forecasts with AI
A crucial requirement for new-age supply chains is to synchronize operations with bigger supply chains. Supply chains have a heavily collaborative environment with raw materials sourced from multiple agencies and locales. This leaves a lot of scope for errors at multiple points. Therefore, it becomes crucial to ensure a smooth flow of products.
AI unlocks a series of potent applications, which when paired with the deficiencies of the demand planning setups, can create a powerful engine of change.
Strategies for a robust product mix planning
A diversified product mix creates both a robust brand identity as well as market domination. The length and girth of a company’s product mix can amplify its authority as a market force as well as add to its bottom line.
Strategies that enable a competitive Product Mix include-
- Expansion: Adding products to the product line to span larger widths or compete for a better market share. AI can help conduct a market gap analysis to better predict what products can be integrated into an existing product mix. AI’s ability to process real-time analytics from retail chains, supermarkets, and small businesses gives it unparalleled power to predict future trends and forecast demand.
- Enhancement: Another strategy for a company to upgrade its product mix is to add value to its existing products and focus on publicizing the newly added value. An AI-driven software can easily analyze competitor products to deliver data on components that are leading to sales. This information can be leveraged to upgrade to higher-performance products.
- Elimination: Eliminating products that have not been performing well is a beneficial strategy for product mix optimization. Examining the internal performance data of a company and weighing it against the big data of competitive market forces is one of the most effective ways in which AI can be leveraged into forecasting demand and enabling a desirable sales forecast.
- Differentiation: Creating a new range of products to completely distinguish a brand from its competitors helps achieve market domination. AI can identify untapped markets and niches within an existing market. Conquering these while planning product mixes can lead to better revenues for the company as well as greater brand visibility.
Make accurate decisions with effective product sales forecast
Product sales forecasting enables the enterprise to optimize its product mix effectively. Product sales forecasting enhanced by Machine Learning helps the company make decisions regarding the below aspects
- When to roll out a new product: Depending upon AI-based analysis of market gaps and peak purchase periods, new products can be rolled out at the right intervals. This helps ensure product visibility and viability.
- When to accelerate production: Depending on the product sales forecast of the distant future, production can be accelerated to meet market demands without a lag in supply. This ensures that competitor brands do not gain control of a market niche created by your enterprise.
- When to stop production: Since AI enables the synchronicity of smaller supply operations to a bigger supply chain, it becomes easier to anticipate production needs. With Machine Learning Intelligence, production can be restricted to periods where it is deemed profitable in the future, even if the current-day metrics of sales are contradictory.
AI-enabled demand planning for the post-pandemic world
Agility has become the key need for businesses in Supply Chain Management. The ability to reroute and adapt according to newer market conditions facilitates accelerated growth trajectories.
Machine Learning methods that enable Product Mix Planning and Product Sales Forecast monetize its ability to reduce prediction error.
Where a manual forecast is defined by a set of rules predetermined by a human being, a Machine Learning forecast (through its Big Data analytics) relies on supervised learning. This enables it to determine seasonal variations, holiday sales, relationships between global events, and product sales.
Differentiated demand planning with ThroughPut
Demand Planning can help companies achieve desired profit margins by adopting a holistic planned and integrated approach. ThroughPut’s AI Software Suite is built on the core principles of Machine Learning. Leveraging the core competencies of Machine Learning and Deep learning enterprises, ThroughPut enables companies to predict trends accurately.
Along with end-to-end visibility, accurate demand forecasting and planning help overcome supply chain bottlenecks and uncertainties for desired bottom-line efficiencies.
To find out more about ThroughPut’s Software Suite and how it can help your business, schedule a free demo today.