Ikigai Case Studies

Tech Co. wanted to improve their in-house efforts to do demand planning

Tech Co. had a top-notch team of internal data scientists, but they were still looking for a third party demand planning solution that would help them make better business decisions

In particular, Tech Co. was looking for product that would to help them do three things:

1. Improve the accuracy of their demand forecasts to enable better planning
2. Forecast demand for new SKUs for which they had no historical sales data
3. Incorporate external event data (e.g., holidays, seasonality) into their forecasts

Core Demand Forecasting solution

• Created highly accurate demand forecasts for Tech Co.’s products at a granular level, including region, product category, and SKU
• Enabled sales projections up to 12 months into the future, with transparent error rates available to increase trust from stakeholders
• Incorporated seasonality, holidays, and promotions into forecasts to uncover key drivers of demand

New Product Introduction module

• Implementing solution to enable demand forecasting for new products by modeling similarity between new products and existing products in Tech Co.’s portfolio
• Designing demand forecasts to be updatable upon launch of new products to allow for adjustments to forecasts pending receipt of real-world sales data

Distribution Co.'s customers frequently place orders with insufficient lead time for Distribution Co.'s suppliers

Distribution Co’s suppliers often require a a lead time 12-24 months, but customers’ orders routinely require much faster timelines

As a result, Distribution Co. regularly experienced millions of dollars of overstocks and stockouts

Distribution Co. often overstocked inventory to avoid delays in fulfilling customer orders, which ate into profit margins; even with this excess inventory, Distribution Co. still often delivered goods months late to customers due to unanticipated stockouts

Distribution Co. needed better forecasts for their customers’ orders to avoid stockouts and reduce the need to carry excess inventory

Better forecasts would allow Distribution Co. to more accurately predict inventory needs, which would both reduce the chance that a customer would order something Distribution Co. was sold out of, as well as limit the need for Distribution Co. to intentionally stock inventory reserves that may not be needed

Before Ikigai, Distribution Co. had no forecasting solution to prevent stockouts

To guide their purchasing, Distribution Co. was stuck querying their inventory using a terminal-like, highly manual interface and working in spreadsheets

Core Demand & Inventory Forecasting solution:

• Forecasted demand at a granular level (split by individual product and geographical division) to create 2 years of advance visibility into expected orders
• Developed stockout alerts to proactively recommend what products to order, when to order those products, and to which facilities to send those products
• Integrated Ikigai's solution with Distribution Co.'s ERP to ensure data freshness

Manufacturing Co.'s customers frequently make large orders of products with insufficient lead time

When orders come in with insufficient lead time, Manufacturing Co. is unable to produce and deliver on-time via standard distribution channels

When orders come in that Manufacturing Co.can’t fulfill by the requested deadline, there a substantial costs to the business

Manufacutring Co. must choose between two undesirable options: either (a) a delay in delivery, jeopardizing their business relationship with the customer or (b) an expensive express delivery via air, significantly impacting margins

Manufacturing Co. therefore needed a forecast of customers’ orders to ensure they were always stocked with the right products in the right places

This would allow Manufacturing Co. to avoid costly last-minute air transfer

Developing this forecast posed a signficiant challenge

The underlying data was sparse and Manufacturing Co. had previously just relied on human intuition to predict when orders would land

Core Demand Forecasting solution:

• Demand forecasts at the individual customer level enabling granular modeling of ordering behaviors despite sparse data
• Proactive alerts to sales team based on forecasts to ensure Manufacturing Co. is prepared to serve customers’ future orders before orders are even placed

Retailer wanted better demand forecasts across both their retail and wholesale business at a highly granular level (e.g., by SKU)

Their existing forecasts were manual, relied primarily on human intuition, and were not at the SKU level

Retailer was looking to launch a new product, but had no historical data to use in forecasts

They weren’t sure how to forecast demand for this new product and whether the introduction of this new product would cannibalize sales of existing, similar SKUs

Retailer wanted to understand how discounts they were considering for their products would affect demand

While they assumed that discounts would drive greater demand, discounts also ate into their margins; they needed to find theright discount rate to optimally balance margins and unit sales

Core Demand Forecasting solution:

• Highly accurate demand forecasts 12 weeks out, filterable by granular attributes including State, Store, Category and SKU
• Revenue forecasts using forecasted demand and unit prices

New Product Introduction module:

• Demand forecast for brand new SKU using internal & market data combined with proprietary AI
• Forecast includes anticipated impact of introduction of new SKU on existing product demand

What-If Analysis module:

• Interactive tool allows retailer to model projected demand impacts of potential discount programs in real-time

Accuracy of retailer's existing forecasts was sub-optimal

Improving forecasts was made difficult by complex business hierarchies, shifting customer behavior, and high number of SKUs

Retailer had been using spreadsheet-based forecasts, which were slow and inflexible

These highly manual forecasts limited the business’s ability to adapt to market conditions in real-time

Retailer had no historical data for new products and new stores introduced each season

With limited data to inform traditional forecasting methods, this retailer was highly uncertain on how to plan for critical SKUs

Core Demand Forecasting solution:

• Built best-in-class demand forecasting using both historical & live data to continuously discover patterns
• Incorporated external event data to refine forecast precision
• Retailer now able to produce accurate, auditable, and adjustable forecasts that update weekly

New Product Introduction module:

• Modeled similarities between existing SKUs & new products to predict demand for new releases without direct historical data