Lean Analysis: Using Historical Data to Interpret Future Cost Structure

“Prediction is very difficult, especially about the future.” - Unknown

One of the obligations of any on-site consultant working for LeanCor is not just to work within the tactical realm of our customer’s supply chain, but to improve upon the structural foundation that produces best practices and standard work in any segment of the organization. Breaking the traditional silo mentality through the evaluation of data in one aspect of the business to see how that information can be applied in a useful manner in another can yield unexpected benefits. However, many companies either lack the discipline to ensure that data is being shared effectively, or unknowingly promote an information “hoarding” mentality in the perceived benefit of employee job security. Fortunately for LeanCor employees in the consultant position, most of the customers that find us have expressed a desire to break traditional role boundaries. In doing so, they have committed to embracing a lean culture where change is embraced, not feared.

Such is the way of thinking at the manufacturer for which I work on-site in coastal South Carolina.

As recent as several years ago, employees of this organization were responsible for meeting expectations for their own functional departments, not necessarily for the company as a whole. A prime example of this was in the sales function. While sales knew what the going market rate for the raw product was, they were only topically aware of other costs they were passing on to their customer. As such, they (nor anyone else in the company) did not know what costs were reasonable to pass on – and in turn how to measure and attempt to reduce these costs. This came to light at a C-level meeting where the focus was high transportation costs, what was driving these costs, and how to reduce these costs.

Fortunately, this is the same dilemma that was facing another one of my colleagues, an on-site for another LeanCor customer in Houston, TX. After pitching our proposal to assist in isolating and projecting these transportation costs, we decided to bring in my colleague to assist in creating a replication of the model he produced for his client - tailored to the dynamic of my customer using LeanCor provided historical data.

After a follow-up discussion with our customer about the current state, we agreed upon the following goals for the project:

  • Use historical transportation cost data to understand shipping volumes, frequencies and costs
  • Project future costs for shipments to defined geographic regions, broken down into static weight buckets
  • Create an easy to use reference sheet for sales personnel to reference when breaking down freight costs (to be included as separate line item for invoicing)
  • Track actual costs and compare to projections, use this analysis to update projections on a quarterly basis

Using 6 months of data (appropriate to capture current pricing trends), we set about creating our objectives. Special care was taken to remove any irrelevant data, such as transportation LeanCor administered that wasn’t customer dependent (interplant moves, scrap resourcing, etc). Our first major breakthrough was uncovering the cost trends associated with different weight breaks of our shipments, which allowed us to extrapolate where we were spending the most money as a result of underutilized trailers. Secondly, we derived the data that highlighted the geographic regions where our customer had a relatively stable cost point per delivery weight.

Example of a regional cost map

At this point, we had all of the data required to understand our cost structure, but only if the forward-looking sales expectations reflected the same levels of activity in the same weight categories for shipments going to similar regions. While we were repeatedly assured that volumes expected for 2013 would be similar to those in 2012, we knew that we had to take into account fluctuations in the cost of fuel, seasonal capacity issues, etc. We did make some assumptions as to what macro circumstances for which we would have to compensate. But in the end, we agreed upon a strict PDCA cycle to ensure that goals, transportation costs, and sales volume changes were reflected in several annual updates that will be distributed to our customer’s sales team.

As of this publication, the time spent on extrapolating future costs from an existing data set has proven to be a successful exercise in providing visibility to a cost category that has been obscured in the past.

These guides have been in use now for several weeks. Through weekly reporting, we have shown that our estimates for what we expected our finished good transportation costs to be have been accurate to within 3%. Most importantly, our customer can now better understand what costs are necessary to allocate to the transportation portion of their customer invoices, and the recipients of the product expect a predictable expense for the movement of freight to their receiving dock.

To a point, refined historical data can give you a certain amount of visibility to your future costs, but regular vigilance is necessary to ensure that your model is updated to reflect current market conditions. Expectations that are met need to be reviewed regularly for consistency, but if your adherence to expected tolerances are maintained, you can rely on developed tools as a trusted part of your business planning.

Written by John Szoke, Manager of Lean Supply Chain Operations at LeanCor

Posted by LeanCor Supply Chain Group

LeanCor Supply Chain Group is a trusted supply chain partner that specializes in lean principles to deliver operational improvement. LeanCor’s three integrated divisions – LeanCor Training and Education, LeanCor Consulting, and LeanCor Logistics – help organizations eliminate waste, drive down costs, and build a culture of continuous improvement.

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