A fast-growing pizza chain restaurant was experiencing frequent stockouts in many of their locations across the country. The issue was causing them not being able to serve some of their best-selling pizza to customers. The VP of Supply Chain contacted us to help them fix this issue.
We took a data-driven approach to profile the demand by geography and ingredients. Then we used statistical methods to profile the demand pattern for all the ingredients. Finally we selected the best statistical model for each ingredient family that minimizes forecast error.
Result? We were able to help this nationwide chain improve their accuracy by 20% through a 3 month study.
Predict the Unpredictable
For some industries, demand is just simply unpredictable. For this particular customer, the demand for 50% of their business is highly unpredictable. They often had to short-ship customer orders, and also have to carry high level of safety stock to counter the unexpected surge in demand.
The VP of Operations turned to us to see if artificial intelligence and machine learning could help improve their inventory and fulfillment dilemma.
We profiled their demand patterns for all of their product families and defined their existing forecast baseline. Then we forecasted their data using machine learning models, which has capability to automatically detect seasonality and choose the best model for each SKU that minimizes forecast error.
Result? The machine learning models were able to more accurately model the pattern and behavior in the data, and therefore was able to reduce the noise (error), with inventory reduction of approx. $1M was achieved.
Supply Chain Network
Faced with pressure to deliver more profit back to the company, a CEO of a furniture distributor turned to us to make their supply chain more lean and responsive. They supply both the USA and Canada market and they pay over $100,000 annually for penalty due to late shipment. Not to mention they also pay hefty sum for tariff and ground transportation.
We analyzed their customer base, logistic costs, and segmented demand by product and by customer base. We analyzed the total cost of several supply chain configurations and decided on the 3PL model.
From there we re-designed the client's supply chain network, conducted RFP on behalf of the client to source for 3PL providers and additional logistic carriers. We also took the opportunity to re-negoitate with existing carriers on behalf of the client.
Result? We launched two 3PL facilities, reduced overall logistic costs between 25- 45%, and increased supply chain responsiveness by 60%!
Coach and Training
Each employee is valuable. This is especially true for those who have been with the company for a period of time. They know the process, they know the people, and they know how to get things done. However, companies often find that majority of their hard-working employees have hard time keeping up with the latest leading practices and have even harder time adopting new technology. For these reasons an industrial equipment manufacturer contacted us to perform competency assessment for all of their supply chain employees globally to assess gaps in performing to the next level.
We conducted competency assessment for over 600 employees across 5 countries. We assessed each employee against their job-role competency. We provided gap analysis for each job-role and also recommendations on how to bridge those gaps.
Result? Through a mixture of online and in-person training, we were able to improve employee's competency level by at least 1 level.