Installing a dynamic sales and operations process (S&OP) to stop margin leaks and excessive transport costs

A market leader with 50+ warehouses across the U.S. is using data to drive successful forecasting, increase on time deliveries and maintain price.

The Challenge

The producer had more than doubled in size in the 5 years since acquiring two more plants, opening more terminals and doubling capacity at a key plant location.  Sales, logistics and operations now briefly communicated weekly to update each function on day-to-day targets.  However, the business had reached a size and complexity that had outgrown their current planning process and distribution strategy. This was evidenced by:

• Delayed customer shipments because of low inventories of finished product, despite very high levels of raw material 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 predict problems before they occurred.

• Railcar movement was slowed down significantly from torrential rains in the Midwest. This created another supply bottleneck and underscored the inefficiencies 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 were not accurate (they extend out for 12 months but are not very granular in the nearest month.) This caused last-minute transport expenses to the customer or product stockouts/unavailability when needed.

  
  

Stage I: The Diagnostic

We began 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. The current-state diagnostic had three objectives:

1. Identify areas to leverage

   
   

We wanted to first ensure that we were focused on those parts of the planning and communication chain that have the greatest value potential for the business.  For exmple, we examined forecasting by understanding the variability demand by product by market.

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.

2. Create an “As Is” map

We created an “As Is” map that clearly detailed 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 current process (e.g. unclear/inappropriate roles/structure, insufficient skills, insufficient/inappropriate resources ($ and people), wrong metrics, etc.) and conducted 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.

3. Plan for Future State

Once the “As Is” map was completed 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: Designing the Future State Process

We identified 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. In this project a RACI (Responsible, Accountable, Consulted, Informed) matrix was required.

Metrics Developed

As part of the future process, we inserted process measures, internal measures and customer measures to better establish warning signals ahead of negative impact. For example, we wanted to know 3 months in advance (instead of a day too late) how many tons of WIP a plant will have- as well as finished product. Another tracking metric was related to maintaining railcar loading efficiency.

We also utilized a linear program to predict impacts to the network on different decision scenarios.

The Results

11 months into the implementation, the results were 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 producing to demand (instead of budget) which keeps the organization nimble and reduces reliance on railcar storage. Customer satisfaction scores have increased which reduces customer churn- directly impacting the average selling price in a commodity market.