Transforming Supply Chain and Demand Planning in E-Commerce and FMCG: Harnessing the Power of AI
Despite huge potential, FMCG companies face numerous challenges when it comes to managing supply chains and dealing with demand online. Within this environment, a concerted effort is needed to design robust supply chains by adopting intelligent technology solutions.
Over recent decades, the fast-moving consumer goods (FMCG) sector has been transformed by electronic commerce. Online sales have been ignited by technological advances and consumer acceptance, eliminating roadblocks and opening up new opportunities. The industry has always been highly dynamic, and the growth of e-commerce has affected both supply chains and consumer demand. In recent years, artificial intelligence (AI) has emerged alongside internet penetration as one of the biggest catalysts for industry evolution.
Despite huge potential, FMCG companies face numerous challenges when it comes to managing supply chains and dealing with demand online. Due to the quick nature of product turnover and the low cost of individual sales, there is very little room for error. Within this environment, a concerted effort is needed to design robust supply chains by adopting intelligent technology solutions.
Integrated business planning (IBP) is an important piece of the puzzle, with this common-sense framework based on effective decision-making. From allocating resources to managing people, time, and money, IBP is about meeting market expectations without compromising profits. To do this effectively, it's important to solve problems and integrate key business functions. When multiple avenues are available to balance supply with demand, organisations can model and implement positive changes from within.
The Current State of Supply Chain and Demand Planning
Traditional supply chain and demand planning models have been adopted over many decades. These two terms are often used interchangeably, but they describe two separate processes. Supply chain models determine how a business will fulfil demand, and demand planning is about predicting this demand. The former modelling approach pays strict attention to financial and service goals, and the latter is more concerned with specific intervals or events.
A number of different supply chain and demand planning models have been established over time to meet the needs of different use cases and industry sectors. These models do many things, but at the end of the day, they're all about managing risks and controlling costs.
Supply Chain Models
The following six supply chain models are standard:
- Continuous flow model. This model prioritises stability, which makes it well-suited to businesses that produce uniform goods and face stable demand. The continuous flow model can break down when there's a lack of stability between supply and demand.
- Fast chain model. This model is suitable for product lines with short life cycles. The fast chain model is ideal for seasonal operations and quick turnover; however, robust modelling is needed to accurately predict demand.
- Efficient chain model. This model is used by hyper-competitive industries, as it is able to maximise efficiencies. However, accurate production forecasts are required, and disruptions can cause unwanted ripple effects.
- Agile model. This model is ideal for specialty items, especially those that require extra care. The agile model requires industry expertise and is not designed for high volumes. For these reasons, it often involves a premium price.
- Custom-configured model. This model requires custom setups during assembly and production. The custom-configured model combines the benefits of agile and continuous flow methods, but it can be costly and only works on an end-to-end basis.
- Flexible model. This model is designed for high-demand scenarios and market peaks. The flexible model demands particular supply chain management software and industry expertise, which can make it inflexible in many real-world situations.
Demand Planning Models
The following four demand planning models are standard:
- On-demand planning. This projection method attempts to forecast future sales in order to manage capacity. While on-demand planning is used heavily by manufacturing and purchasing teams, choosing the right products to model can be challenging.
- Existing product planning. With this model, forecasts are based on standard past sales or promotional sales. Rolling average statistics or large sample groups are needed for an accurate result, with this model also open to short-term variability.
- New product planning. With new products, teams often need to work without a sales history. Using data from new product lines can be unrealistic, which is why people use a combination of similar existing products, optimised data analysis, and market research.
- Past performance planning. Conducting a detailed analysis of past performance is an accurate way to measure future demand. While this model is not ideal for seasonal operations or flexible product lines, it works in most situations.
Along with these four basic models, there are qualitative methods of forecasting based on expert analysis or team consensus. These methods are typically used when there is little or no historic data. Examples include the expert's opinion method, the Delphi method, the sales force composite model, the historic analogy method, and buyer surveys.
AI Can Be an Effective Solution
Regardless of the modelling you adopt, AI technology can be an important supply chain management tool. The relationship between supply and demand is often opaque, with AI tools using data to connect disparate systems and solve complex problems. Whether it's forecasting trends, analysing markets, or managing inventory across borders, AI is an effective modelling solution and an even better everyday business tool.
Unpacking Integrated Business Planning
Enabled through digital transformation, IBP is the cornerstone of many supply chain operations. IBP helps to translate desired business outcomes into real-world requirements. It takes financial constraints, operational processes, and business resources into account while attempting to maximise profit margins and manage risk.
Typical IBP processes can be targeted towards various outcomes. From revenue and cash flow to demand and inventory, it's about aligning inputs with outputs by integrating disparate systems. IBP represents the evolution of sales and operations planning from the 1980s in a way that meets the demands of modern businesses. This approach allows senior management to plan and manage operations over an extended time frame in order to align strategic plans with tactical efforts. While horizons differ between implementations, 24-36 months is typical.
The Functions and Benefits of IBP
When implemented properly, IBP can be an incredibly valuable asset for FMCG operations. This comprehensive tool helps with a wide array of critical business processes, from procurement and planning to business integration and cost management. In the context of e-commerce and the FMCG market, IBP has the potential to streamline every aspect of operation. While there is no universal way to describe integrated processes, the following capabilities must be enabled:
- Enterprise modelling — including supply chain modelling, demand planning, and finance chain modelling
- Integrated planning — including multi-function planning, predictive planning, and collaborative planning
- Enterprise optimisation — including multi-constraint optimisation and financial integration optimisation
IBP aims to streamline a vast array of processes, helping to boost efficiencies and foster growth on every scale. FMCG companies that adopt IBP can expect tighter integration between business processes, greater control over their supply chain, and better scenario modelling. When adopted early and aligned with other systems, it helps businesses to stay on the front foot.
The Role of AI in Revolutionising Supply Chain Management and Demand Planning
AI has vast potential to catalyse supply chain management and demand planning. Driven by big data and steered by agile decision-making, AI tools can solve many of the problems facing FMCG companies. The sector faces numerous challenges, including diverse product lines, disparate datasets, and long planning cycles. These challenges can be incredibly disruptive, compromising flexibility while curtailing innovation and slowing down response times.
Perhaps more than any other sector, the FMCG industry needs to move quickly and adapt to the market in real time. While traditional supply chain systems are designed to solve some of these problems, they lack the ability to respond quickly in the event of supply disruptions and unforeseen market shifts.
AI Trends and Benefits in the Context of Supply and Demand
Advanced digital connectivity has fostered a new world of shared data and fluid real-time decision-making. When it's implemented properly, AI technology is proactive, not reactive; integrated, not isolated; and capable of handling diverse market scenarios. Depending on the technology used, AI offers the following benefits for supply and demand planning:
- End-to-end tracking and advanced network modelling support supply chain planning.
- Access to comprehensive real-time data enables accurate demand forecasting.
- Data analysis and pattern matching support detailed price and revenue optimisation.
- Optimised scheduling and performance management provide better workforce planning.
- Machine learning and predictive analysis improve risk identification and mitigation.
In the FMCG sector, it's essential to identify and implement AI technologies based on particular supply chains and operational needs. Along with reducing costs, each model needs to manage timelines and analyse risks based on the demands of the product line. When you get it right, AI helps to improve efficiency, build resilience, and mitigate risk to give you a competitive advantage.
Case Study: AI in Action
There are many examples of companies successfully adopting AI to help manage their supply chain. From market analysis and predictive forecasting to inventory management and customer engagement, AI tools have an incredibly broad scope. One recent case highlights the huge potential of AI in the supply chain, with a growing web of business activities successfully managed by German logistics giant DHL. From warehouse robotics to back-office databases and customer-facing apps, DHL is heavily invested in AI.
DHL is one of the biggest logistics operations in the entire world. It was founded back in 1969 as the world's first international door-to-door delivery service. The company has spread across the world over the decades, adopting various technologies along the way to improve safety standards and streamline operations.
Recently, DHL started using autonomous forklifts to increase operational efficiencies and boost workplace safety. According to DHL, almost 30% of its material-handling equipment will have robotic automation capabilities by 2030. Robots are used to perform repetitive tasks in a predictable manner, which frees up human workers to focus on other things. DHLBots have been successfully integrated into the supply chain, sorting parcels, moving boxes, and making warehouses both safer and smarter.
The use of AI is not limited to the warehouse floor, however, with the company also adopting software tools to improve e-commerce and logistics. It has adopted a range of interactive AI technologies, which are used in everything from geolocation and navigation to speech recognition and e-payments. These tools play a vital role in logistics, bringing greater efficiency to the supply chain and helping to automate a more positive customer experience.
The Future of Supply Chain Management and Demand Planning
In recent years, various AI technologies have been adopted to support supply and demand planning. These tools are used across the board, improving performance while helping to reduce costs and boost customer satisfaction. AI can be used almost everywhere, with automation and optimisation tools helping businesses to identify gaps, improve workflows, and increase response times throughout the supply chain.
The following trends will be shaped by AI in the coming years:
Global uncertainty and market volatility continue to emphasise the importance of agility. AI tools can help to build and manage micro supply chains, allowing companies to streamline operations by going modular. Instead of using cumbersome models based on outdated economies of scale, AI can link diverse microservices through composable applications.
Supply chain leaders in FMCG and elsewhere are looking to automate pretty much everything. From warehouse robots to software chatbots and back-office spreadsheets, automation allows for efficient scaling and greater control. AI enables effective automation, with well-chosen tools able to reduce manual efforts and lower business costs.
Boosted Digital Visibility
Visibility will have a huge influence on supply chain management over the coming years. Cloud-based technologies driven by AI will provide new levels of transparency, including the leading industry models' digital twins and control towers. A digital twin provides a virtual replica of supply chain assets and their interactions, and a control tower enables cross-functional teams to orchestrate responses from AI suggestions.
Having the ability to manage inventory effectively is central to every FMCG company. Recent global stressors have stretched supply chains in every direction, which has led to bottlenecks and inventory stockpiling. AI tools can help to optimise inventory by using predictive analytics to identify patterns and forecast demand. In a volatile world, AI helps to avoid unwanted stock fluctuations.
E-commerce continues to impact FMCG supply chains around the world. Online trade has been growing for many years, with the global pandemic accelerating demand and stretching supply chains to breaking point. B2C and B2B e-commerce are likely to grow further over the next few years, with AI technologies helping to predict demand, manage supply, and support customer interests.
How Companies Can Prepare
AI represents a brave new world for the FMCG sector, and preparation is everything. To ensure effective stock control while meeting demand and reducing waste, it's important to adopt smart technology systems. Working with a proven e-commerce provider is the best way to utilise AI and ensure the integrity of your organisation.
How Skipped.ai Facilitates Smart Supply Chain Management and Demand Planning
If your company is involved with fast-moving consumer goods of any kind, it's vital to stay on your toes. The smaller and less expensive your product lines are, the more flexible and agile you need to be. Measuring consumer demand and addressing supply chain issues is a huge piece of the puzzle, and AI technology has a massive role to play.
At Skipped.ai, we have the expertise you need to identify key procedural issues and solve complex problems. We connect brands and retailers through a collaborative fulfilment platform, with our intelligent system enabling the sale of out-of-stock products and variants. Customers can complete their purchases directly on your website without cancelling their entire shopping cart. When you align supply with demand, anything is possible.
The benefits of Skipped.ai are clear:
- Sell without limits
- Reduce overstock
- Instant additional sales
- Move overstocked items
- No extra integration needed
- Maximise your revenue
The FMCG sector is growing at a rapid rate. But competition is tough, and supply chain challenges are immense. Now more than ever, it's important to identify and adopt smart tools to give you an edge. When it's implemented properly, artificial intelligence has the potential to transform your company from the inside out.
In this article, we've taken a deep dive into the supply chain. We've looked at traditional models and seen how they function as a form of integrated business planning and reviewed the impacts and benefits of AI within e-commerce. Regardless of the model you use, AI has the potential to revolutionise many aspects of your supply chain and, therefore, your entire business.
By understanding the benefits of this technology and being aware of relevant trends, you can be confident about using AI tools in your organisation. The future of supply chain management is dependent on AI, and it's important to be prepared.
At Skipped.ai, we provide simple solutions to common supply chain problems. If you want to ensure an efficient and effective supply chain that meets the demand of your FMCG company, we have the solution. Contact Skipped.ai today and start selling without limits.