A specialty chemical company generated enormous amounts of excess product every year. As this inventory expired, the supply chain had to regularly write off and dispose of the expired product. Making it worse, these chemicals were considered hazardous waste, so the business paid to transport the inventory across the country to special landfills or incinerators.
No Coordination Between Functions
The business was a segment of a multinational industrial company, and separate groups distinctly managed the functions of demand planning, supply planning, and waste management. There was virtually no coordination between these functions, and these teams even operated from offices in different geographies.
Because of this lack of coordination, no one fully understood the true cause of the excess inventory. The supply chain blamed the business for poor forecasts, while the business blamed the supply chain for always producing the wrong product.
My Approach: Audit Entire Process
Inventory is always the consequence of a chain of events. To understand what generated excess inventory, you need to analyze the entire process that led to the creation of the inventory plan.
I began my analysis with the sales pipeline, as this is a key input to the planning cycle. I then worked with the demand planner to see how they incorporated the pipeline data in their forecast model. Lastly, I examined the supply plan (or the ‘production plan’) to see how if it was aligned with the demand plan and existing inventory levels. Here’s what I found:
1. Sales Pipeline
The sales team provided key inputs into the demand plan at this business. Each sales associate populated a sales pipeline each month, and the demand planner used this pipeline data to estimate sales from new customers in future periods.
I compared the sales pipeline from prior periods with actual invoiced sales. I found that in nearly all cases, actual invoiced sales for new client wins was far below the sales level predicted in the sales pipeline.
Finding #1: Exaggerated Sales Pipeline
The sales team was highly motivated to report new sales wins. In fact, they habitually exaggerated the impact of new wins to give the appearance of a robust sales pipeline. Because the demand planner incorporated the sales pipeline into their forecast, the demand plan also overstated the impact of new customer wins.
2. Demand Planner Role
I spent time with the demand planner to better understand their process for developing the demand plan each month. We walked through the process of extracting historical data each month, and how they incorporated the sales pipeline and other data into the demand forecast.
Finding #2: Poorly-Defined Demand Planning Function
The designated demand planner had other job functions, and was not dedicated solely to the task of creating a demand plan.
They had no statistical expertise, and had only minimal background with spreadsheets and data consolidation. As we worked together, I helped her identify a number of spreadsheet errors in the demand forecast model. Each of these errors was contributing to either overproduction or underproduction of various SKUs.
3. Supply (Production) Plan
Armed with knowledge of how the demand plan was built, I now reviewed the production plan put together by the supply planner at the manufacturing site. The function of the supply planner is to create a production plan that builds enough inventory to satisfy the demand plan.
A well-built production plan incorporates both the demand plan and existing inventory. If the business already has excess inventory a certain product, the plant should not produce more inventory until that product is sold.
Finding #3: Mysterious Errors in Production Plan
I compared the demand plan and existing inventory levels to create my own estimate of needed production of the top SKUs over the next twelve months. I then compared this to the actual production plan.
I found that the actual production plan showed an especially high production for five specific SKUs over the next twelve months. The business already had excessive amounts of inventory of these SKUs, so it made no sense that the production plan called for even more production of these same SKUs.
Upon examination, we learned there was an outdated script in the legacy planning system at the plant. No one at the plant knew how to maintain the planning system, so the application was not updated to reflect the attributes of the current product portfolio.
My Impact: $2 Million Reduction in Inventory Waste. Every Year.
By looking at the entire planning cycle, I identified three key issues that each contributed to excess inventory:
- Exaggerated Sales Pipeline
- Errors in Demand Forecast
- Production Plan Errors
These issues result in a total forecast accuracy of 25%. Said differently, this is an error rate of 75%. To illustrate the impact of this: If the business needed 100 units of a specific product in a given month, the plant was on average producing as little as 25 units or as much 175 units.
These issues contributed to massive amounts of excess inventory, all of which needed to be disposed of as hazardous waste.
I separately addressed the issues with the sales pipeline and the demand planning process, and we engaged software engineers to fix the problem with the planning system. All in, we eliminated nearly $2 million of waste on a recurring basis. This is about two railcars of product each year; or twenty railcars over a ten year period.