A market leader with 50+ terminals across the U.S. is using data to drive a successful forecasting, increase on time deliveries, and maintain price.
The producer had more than doubled in size in 5 years since acquiring two more plants, opening more terminals and recently doubling production capacity at another plant location. Sales, logistics and operations were briefly communicating formally every week to update each function on the state of the union.
The business had reached a size and complexity that had outgrown their current planning process and distribution strategy. This was evidenced by:
- Customer shipments were delayed because of low inventories of finished product, despite very high levels of raw materials WIP
- Inaccurate metrics. Bottlenecks had changed at key plant locations since the capacity expansion, effectively pushing the limiting factor to another area. There were few ways to know predict problems before they occurred.
- Railcar movement was slowed down significantly from torrential rains in the Midwest. This provided another supply bottleneck and underscored the inefficiency of current loading practices at some facilities.
- Communication and Responsibility failures. For example, Plant A could have produced more product, but they were running to budget and not demand. Another example: Logistics saw the upcoming problem with FG levels but did not “squawk” loud enough to warn sales and ops.
- Forecasts generally are not accurate (they go out for 12 months but are not very granular in the nearest month.)
- Information from plants was closed off to the sales and planning functions
We began with a two-stage process beginning with a brief diagnostic to identify the key leverage points in the current state and confirm the approach required for the following more detailed and rigorous future state design.
In stage I, the current-state diagnostic had three objectives:
- Identify areas to leverage
We wanted to first ensure that we are focused on those parts of the planning and communication chain that have the greatest value potential for the business. For example, we would like to examine forecasting by understanding the variability demand by product by market. This could generate a conceptual matrix as shown below:
We used this type of analysis, and other cuts at the data (e.g. forecast volatility by customer) to identify those areas where the team should focus its effort to improve the process.
- Create an “As Is” map
We created an “As Is” map that will clearly detail the key parts of the current supply chain process. This required interviews with all participants in the supply chain to better understand the information flows around a) how service levels are determined, b) capacity planning, c) cost to serve by product and customer, d) sales planning and e) inventory management.
The team searched for “disconnects” (flaws) in the As Is process (e.g. Unclear/inappropriate roles/structure, insufficient skills, insufficient/inappropriate resources ($ and people), No/wrong metrics, etc.) and conduct root cause analysis for the key problems.
As part of developing the “As Is” map we also needed to fully understand the information flow from raw material sourcing all the way to forecasting and inventory management.
- Plan for Future State
Once the “As Is” map was complete we conducted several reviews of the map and identify those areas of the S&OP process which needed to be fixed and the level of difficulty/intervention required to fix them. This then formed the basis for developing a detailed project plan for the next phase of the work.
Stage II: Design the Future State Process
This would likely center around four developmental areas: 1) information flow, 2) segmentation of products and customers, 3) forecasting, and 4) leadership commitment to modeling the right behaviors.
Included would be the development and agreement of specific accountabilities. This is one type of project in which a real RACI (Responsible, Accountable, Consulted, Informed) matrix was required.
As part of the future process, we inserted process measures, internal measures and customer measures to better establish warning signals ahead of negative customer or cost impact. For example, we wanted to know 3 months ahead how many tons of WIP by category a plant will have as well as finished product. Another metric was related to railcar loading efficiency.
We also utilized a linear program to predict impacts to the network on different decision scenarios.
This phase will incorporate the elements of change management planning such as roll out approach (hard cut vs. phased approach), project management practices, syndication and communication deployment, and mid-course corrections.
Post Implementation Audit
To help ensure that the agreed process is being followed we propose that a detailed audit of the process be conducted. This can only begin once the Project Manager has been recruited/assigned and has had time to begin implementation.
The results (so far)
11 months into the implementation , the results are encouraging. Logistics costs have been reduced by $1.5 million over a period in which the company enjoyed their best sales year in their 50-year history. Plants are making product to demand which keeps the organization nimble and reduces reliance on railcar storage units. Customer satisfaction scores have increased which reduces customer churn- directly impacting the average selling price in a commodity market.