Freight rates can be volatile, especially in the spot market and, for high-volume buyers of transportation services such as Kimberly-Clark, that creates unpleasantly unpredictable costs. Lately, in order to cope with the ebb and flow of lane volume from its mills to customer-facing distribution centers, the multinational personal care corporation had seen a dramatic increase in its use of transportation classed as “non-award” — not under contract or spot market. That brought a corresponding skyrocketing of total transportation costs.

Peak demand for trucks that couldn’t be satisfied by carriers under contract forced the use of expensive non-award and spot market truckers. Further, stressed sites couldn’t handle the loading and unloading peaks, causing trailers to wait excessive periods for unloading. Again, costs ballooned as a result, especially from carrier detention and labor over-time.  

At the heart of the problem was a conflict of interests between the company’s supply-planning and production-planning processes. Fluctuating-customer demand generates the need for both cycle and safety stocks, and the supply planning system, believing it had “unconstrained distribution” solutions, which generate painful results when it came to deployment – including spot market freight buying.

Kimberly-Clark’s supply chain systems team developed a new system, driven by artificial intelligence (AI), designed to bolt onto the company’s existing planning and ERP systems.

The EARL system:
• Picks up on unconstrained product supply plans from the planning system
• Makes calculations simultaneously for all sites and all transportation origins and destinations.
• Optimizes total cost by taking into account real-world DC labor and transportation capacity constraints.
• Produces a stock deployment plan within those constraints
• Allows for prioritizing replenishment depending on demand
• Produces savings

At the same time, EARL leverages the theory of “postponement” and late-stage differentiation, in order to determine a demand-driven product mix in time to take advantage of earlier tendered Stock Transport Orders (STO’s).

Customer service levels needed to stay front and center, and EARL is designed to proactively position inventory, and offer the flexibility to reduce volume in one lane while pushing additional volume in another, when the need arises. So, although one important goal is to cut  the use of spot carriers, EARL uses extra labor and non-preferred freight when it determines paying a premium is critical to meet a particular customer demand.

EARL’s LevelLoad function determines the number of trucks needed by day and by lane for the next week, based on projected requirements by day for the next month. But at the same time,  it gives priority to making sure customer-facing DC demands are met, while holding back non-critical shipment until they can best be fitted in with distribution capacities. As a result, shipping and receiving sites can stay within their throughput and storage-space capacity, and sites ship and receive only the number of loads that they can reasonably handle.

With a longer horizon of smoothed shipments on each lane, the EARL process is able to reserve trucks further in advance. This early tendering occurs without defining what will travel on each load. Later, just before shipping, and using the latest data from sales orders, the product mix is determined based on the most current demand picture. When defining the contents of these shipments, an optimizing load builder works to not only maximize load fill and prioritize high-priority products, but can meet specific site constraints, such as avoiding mixing product in a single load that is produced in widely separated parts of a production mill’s campus.

Planners now focus on exceptions

Fortunately, the data for EARL existed in current planning, ERP and transportation systems, making EARL relatively low-maintenance, and the daily EARL process runs automatically. LevelLoad creates the daily lane-by-lane network plan for managing the peaks and valleys in the next couple of weeks, creating “placeholder” stock transport orders (STOs) far enough in advance to match carrier planning windows. This frees up freight analyst to work on the (fewer) non-award loads.

Instead of a lane-by-lane approach to planning, with manual overlays for perceived capacity constraints, EARL performs a simultaneous evaluation of all possibilities. Advanced use of lead-times and time-phasing means that EARL is effectively a digital twin for the network. The total network optimization takes less than 20 minutes.

Since bringing all lanes in the Kimberly-Clark network onto EARL in January 2022, the company says the metrics all point to a very positive impact, with significantly reduced volatility in load volumes, improved carrier tender acceptance, better customer service and corresponding savings.