Goodpack Transforms S&OP Process with Artificial Intelligence and Advanced Analytics
Customer Company Size
Large Corporate
Country
- Worldwide
Product
- OPTIMUM
Tech Stack
- Machine Learning
- Advanced Analytics
- Cloud-based Platform
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Cost Savings
- Revenue Growth
- Productivity Improvements
- Customer Satisfaction
Technology Category
- Analytics & Modeling - Predictive Analytics
- Platform as a Service (PaaS) - Data Management Platforms
- Application Infrastructure & Middleware - Data Visualization
Applicable Industries
- Retail
Applicable Functions
- Sales & Marketing
- Logistics & Transportation
- Business Operation
Use Cases
- Supply Chain Visibility
- Demand Planning & Forecasting
- Inventory Management
Services
- Data Science Services
- System Integration
- Training
About The Customer
Goodpack is a world leader in steel Intermediate bulk containers (IBCs), a multi-modal, reusable metal box system that provides packaging, transportation, and storage for global industries. Goodpack owns and operates more than 4 million IBCs across 78 countries at 442 depots, 352 ports, and 8,031 customer locations. An IBC is a large vessel that stores or transports fluid and bulk materials. Goodpack rents IBCs to customers. Customers rent IBCs, with Goodpack delivering the IBC to the customer's specified location and retrieving it on rental expiration.
The Challenge
Identifying customer demand, locating inventory in the right location, delivering the IBC and retrieving IBCs involves a complex series of decisions. Goodpack’s primary challenges were to improve asset utilization, reduce unproductive transport costs and decrease delayed or lost revenue. To address these challenges, Goodpack retained Antuit to transform their global S&OP process. The existing planning team lacked an analytical decision support engine that could enable optimal business decisions and as a result, drive effective sales and operations planning/ S&OP cycle. Specific challenges include: Systematic over or under forecasting due to lack of predictive forecasting for both supply and demand of containers/IBC. Suboptimal balancing of supply and demand leading to unproductive global movement of containers/IBC between depots. Suboptimal asset/container injection in the network. No automated allocation of inventory to sales order. What-if analysis to evaluate various scenarios on supply & demand balancing was lacking. Visualization and reports tracking business KPIs and data-driven insights were missing. Poor adherence to the global S&OP process by the sales and logistics team.
The Solution
Antuit deployed its planning and analytics platform, OPTIMUM, to address these challenges. Antuit assembled a team of business consultants, statisticians, operation research scientists, and technologists to build the solution. The team combined technology, science and domain expertise to transform the client’s S&OP process radically and unlock value. Specifics of the solution: Forecasted supply and demand using proprietary machine learning techniques. Built an ensemble of 20 advanced statistical models, plus a custom statistical model to consider specific business complexities. Created an optimization mechanism to globally assign inventory to the right location, to meet the demand at the lowest cost without compromising service levels. At an operational level, designed an order allocation algorithm to allocate inventory to the sales orders based on the business rules and to build efficient truckloads. Implemented an easy-to-use scenario workbench capable of running multiple scenarios in parallel. Configured an Inbuilt visualization platform to deliver various reports, KPIs, and insights. Re-designed S&OP process aligned with industry best practices. Provided comprehensive training program to central planning team and key stakeholders that covered the new S&OP process, statistical techniques deployed, and optimization concepts used. Also established a refresher training program to drive higher adoption of the solution.
Operational Impact
Quantitative Benefit
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