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:

1. 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: 

  1. They are compartmentalized with a need to be maintained by large IT departments
  2. 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.

2. 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.

3. 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.

4. 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.

5. 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

6. 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 

Tackle CPG Challenges with the Solvoyo Digital Platform

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. 

Solvoyo offers a disruptive alternative that removes the partitions among critical functional silos: demand, inventory, production, replenishment, and transportation planning in one platform working with data consistent across operational, tactical, and strategic horizons. 

A $3.5B consumer products company has been using Solvoyo’s digital platform for its daily Order Fulfillment/Allocation and multi-stop multi-mode Route Planning. The plans are automatically created each day with three different objectives: 

  • Minimize total transportation cost; 
  • Maximize total margin; 
  • Meet a specified sales amount in any level of aggregation, e.g., geographic, item group;

Now, the entire process is managed daily by one highly analytical member. The UAR is 95%! No Excel upload! And 18% lower overall spending and higher fill rates within a year of go-live, based on prior baselines.

The Solvoyo platform successfully helped the Fortune 100 consumer packaged goods company optimize its supply chain for food and non-food products end to end. Our innovative platform generates multiple manufacturing plans automatically, taking into account multiple objectives and constraints simultaneously, resulting in feasible plan scenarios. The objectives include maximizing output and minimizing lateness, while the constraints vary from hard to soft, such as due dates, worker capacity, and material availability. The input data are automatically extracted from the ERP, and the output data are automatically uploaded back to the ERP. The platform also automates fulfillment & transportation decisions with User Acceptance Rates of 90%+. 

In the post-pandemic market, Consumer Packaged Goods (CPG) manufacturers who implement autonomous supply chain technology, employ concurrent optimization, utilize big data, leverage scenario-based planning, prioritize collaboration, and adopt agile planning parameters will be the ones that thrive.