Commerce is global and regional at the same time, the world is getting smaller and more interconnected, and Consumer Packaged Goods (CPG) manufacturers operate in this build-anywhere and sell-anywhere market. Consumers are ever more conscious of value, sensitive to health and environmental issues – especially after the COVID pandemic, each demanding more options for their money. Retailers, especially in the developed world, demand collaborative practices, continue to increase the quality of their private label offerings, and are becoming significant competitors.
Here we have compiled a list of the top six challenges that CPG companies face in the post-pandemic market.
Autonomous Supply Chains
In this competitive environment, a CPG manufacturer needs to fight to get space on retailer shelves in each region, keep those shelves stocked, compete and collaborate simultaneously with e-commerce, and maintain its operating margins.
End-to-end supply chain visibility, planning, and execution support software are critical in agile supply chain performance. However, traditional on-premises or in-house software suffers from two fundamental drawbacks:
- They are compartmentalized with a need to be maintained by large IT departments
- The installed base is shackled by old technology
Companies make a significant effort to generate “optimal” plans, yet still, they must tell the execution systems what to do through Excel manipulation or rules (of thumb). This is because most classical planning solutions lack the modeling capability and computing power to accommodate different data sources, large SKU count, and detailed constraints and contingencies to build an immediately executable plan. The classical approach involves functional silos, sequential decisions, and Excel and people to render a plan executable.
CPG companies that utilize an autonomous supply chain technology see a reduction in their inventory and cost and an increase in revenue. The solution should integrate easily with various internal and external data sources and processes, model constraints at a granular level, use common parameters throughout the supply chain with a single source of truth, provide detailed plan scenarios, and leverage new technology for speed.
Concurrent Optimization for Lower Total Cost to Serve
Traditional planning systems operate within the scope of classical silo definitions: demand forecasting, inventory optimization, replenishment planning, production planning, materials planning, transportation planning, order fulfillment, etc., each with discrete plans generated typically in sequential batch runs.
Theory and practical implications are clear: optimizing each silo does not imply optimizing the end-to-end system. An overall best plan requires multiple silos to work together and perhaps compromise their own KPIs for the sake of the end-to-end system.
Planning systems should drive towards true concurrent optimization to achieve the best result, ideally creating multiple scenarios. For instance, the solution should optimize availability, fulfillment, source determination, routing, warehouse handling, and production capacity together and concurrently, focusing on minimizing Total Cost to Serve.
Big Data and End-to-End Visibility to Match the Speed of Business
Large E-commerce companies use unconventional supply chain processes and technology to manage huge numbers of SKUs, support continuous selling, change prices frequently, and extract, transform, and load data from diverse sources such as web searches, basket transactions, click rates, weather, and real-time signals from competitors and the marketplace.
Big data is used to understand a customer’s propensity to buy, the tendency to return, conversion of clicks to orders, demand sensing signals, individualized promotions, etc.
As many CPG companies adopt a direct-to-consumer business approach, the companies that utilize these technologies best will achieve better results than those that don’t.
Scenario-based Planning for Higher Resilience
Considering that nearly 85% of a company’s performance depends on external factors, CPG companies need a process that generates several plan scenarios and a user-friendly platform to evaluate them for possible execution decisions rapidly. The planning process should be automated, repeatable, and not dependent on Excel-based manipulation.
The importance of preparing for different scenarios became clear after the COVID pandemic. That’s why a supply chain planning platform should enable scenario-based planning that uses adaptive learning algorithms to select the right plan among the scenarios based on automated processing.
For example, the platform should be able to generate multiple manufacturing plans automatically, comprising multiple objectives and multiple constraints, each combination proving a feasible plan scenario.
Collaboration with Commercial Partners
Companies with advanced supplier-collaboration systems outperform their competitors. Retailers, especially in the developed world, demand collaborative practices with their CPG partners. Many innovative CPG companies also collaborate with their internal and external suppliers on net requirements planning for the factory and Purchase Orders (PO) for components and OEMs.
The collaboration practices that retailers demand from their CPG partners are similar to those the CPG manufacturers demand from their internal/external suppliers on a digital platform:
- Provide visibility of order and PO status for customers and vendors.
- Automate status updates via Electronic Data Interchange (EDI) or custom Application Programming Interface (API)
- Automate updates of Master Data, such as features, SKU transitions, dimensions, weight, volume, and pictures.
- Provide advanced notice of actions, including advanced shipment notice (ASN), an estimated time of arrival (ETA)
- Track order and PO life-cycle from multiple data sources to measure lead time and OTIF
- Share a non-committal forecast and collaborate on forecasting and replenishment
- Enable vendor-managed inventory replenishment based on availability metrics
Maintenance of Planning Parameters for Agility
Traditionally planning systems process deterministic input parameters (forecast, production capacity, customer service levels, inventory targets, lead times, etc.) to provide very specific output (production, fulfillment, transportation plans by product/date, etc.).
With cognitive computing, the planning solutions should be context-aware, recognize changes in parameters, understand implications (cost, time, customer service, etc.), alert the planner of the changes, offer alternatives to reset a planning parameter (or recommend the right parameters), allow easy mass-updates and ultimately get the planning system to update them automatically.
The solution should self-learn and deal with patterns in addition to deterministic numbers. The market creates a lot of noise on the topic, but just a few companies, including Solvoyo, use state-of-the-art algorithms and machine learning specifically to:
- Automatically track PO lifecycle, transportation lead times, forecast errors, and demand/supply variability. Collaborate with suppliers and carriers to adjust service levels and optimize inventory at SKU, component, and location level
- Find clusters and patterns to predict demand in highly dynamic and price-sensitive channels
- Measure the effect of promotions
- Provide product portfolio recommendations based on actual POS and external market data
Final Thought on Supply Chain Challenges
CPG companies need to embrace cognitive computing to achieve context-aware planning solutions. Traditional planning systems process deterministic input parameters, whereas cognitive computing enables planning solutions to be context-aware and self-learning, providing the agility necessary to maintain planning parameters.
Nilufer Durak is Chief Operating Officer, Head of Customer Success at Solvoyo. Nil is a highly motivated technology executive, passionate about implementing Solvoyo’s bold autonomous supply chain vision with clients. With over two decades of experience for Corporate America, Nil has developed a deep understanding of customer success and operational excellence. She is best known for her boundless energy and ability to get things done. Currently, she is COO and Head of Customer Success at Solvoyo, a leading supply chain planning and analytics SaaS company based in Boston. Nil has also been an active member of women’s professional networking groups, advocating for women’s leadership roles in the technology and entrepreneurship fields.