Daily Archives: May 21, 2012

From Strategic Spend to Strategic Value-Add, Part IV

Today’s guest post is from Ayush Sharma, a Strategic Sourcing Consultant with Trade Extensions in the Americas. His particular speciality is the application of optimization to Retail Sourcing, Dedicated Transportation, 3PL Logistics Sourcing, and Direct and Indirect Materials Sourcing. Ayush has a Masters degree in Supply Chain Management from the University of Texas at Dallas, certifications in Lean Six Sigma and Supply Chain Management, and has served as a Technical Director for a local branch of the Institute for Supply Management (ISM).

We started the series off by discussing the importance of supply and demand chain integration, with respect to the organizational strategic plan, as the key to an efficient, profitable and fluid business and the importance of a good Strategic Sourcing process, built on combinatorial bidding and optimization, in the execution of supply and demand chain integration. Then we discussed the characteristics of a strong and measurable sourcing process which can be utilized to increase Supply Management throughput and turn the organization’s Strategic Spend into a Strategic Value-Add for the corporation as a whole. In our last post we presented the first of two examples, inspired by real-world events, that demonstrate the impact of including combinatorial bidding and optimization in a sourcing project that follows a process similar to the one outlined in our last post. Today, we present our second example.

Let’s consider the case of Retailer X that wants to source several cases of fresh fruit juice. Three varieties are being sourced in this project — Apple, Blueberry and Cranberry Juice. The retailer has three DCs in Austin, Baton Rouge and Columbus and wants to determine if it’s more cost effective for the supplier to transport items to the DCs versus the retailer’s trucks picking them up. Finally, let’s consider the three suppliers placing bids on these items are Company A, Company B and Company C.

Retailer X has the following forecast for FY 2012:

Item Name Distribution Center
  Austin, TX Baton Rouge, LA Columbus, OH
Apple Juice 10,000 cases 10,000 cases 10,000 cases
Blueberry Juice 20,000 cases 30,000 cases 10,000 cases
Cranberry Juice 30,000 cases 10,000 cases 10,000 cases

The team wants to perform some creative analyses. To this end, suppliers are allowed to provide the following information:

  • Delivered Duty Paid (DDP) ‘Cost per Case’
    (this includes the cost of transportation from the supplier location to a DC)
  • Collect ‘Cost per Case’ excluding transportation
    (in this case, the retailer handles transportation)
  • Item and location-specific capacities
    (e.g., the supplier can only provide 30,000 cases of Apple Juice from their Florida location)
  • Discounts on dynamic bundles of items
    (e.g., If awarded the entire forecast of Apple Juice and Blueberry Juice, the supplier offers to provide a discount of 5%)
  • Information about the locations that suppliers will be shipping from

The retailer has been strictly monitoring data from the last two years and is using the implemented costs from FY 2011 as a baseline for this project. Based on the data collected over the last two years, the retailer was also able to find a direct correlation between the suppliers’ qualitative metrics (let’s call this an Index Score) and their ability to match the expected price without unexpected cost increases over the financial year. Based on this information, the retailer wants to penalize suppliers with a low Index Score to ensure they’re able to maintain supply quality.

It’s possible to get a sense of analysis possibilities just from looking at the supplier data collected. The retailer obtains a ‘Transportation Cost’ (Cost per Case) from their internal transportation team using the suppliers’ location information. This Transportation Cost is used to calculate a ‘Landed Cost per Case’ if the retailer handled transportation. The Landed Cost thus obtained is then compared against the ‘DDP Cost per Case’ and the best cost is chosen. The retailer also takes into account supplier capacities to calculate how much of the demand volume gets fulfilled from each location. Also, each supplier has offered certain discounts if they’re awarded certain volumes. This is weighed against the capacity information to determine the best overall fit.

The optimization and analysis process typically spans several steps:

  1. Low Cost Scenario: This scenario simply calculates an award to each Item-DC combination using the lowest cost per case (among the Landed Cost and the DDP Cost) without considering capacities or discounts
  2. Low Cost with Capacities: This scenario again uses the lowest cost per case but now considers supplier capacities and discounts while calculating individual awards
  3. Limiting Winners: Typically, there are some supplier specific constraints that need to be applied (e.g., only 1 supplier gets the Austin DC); We build upon the solution in #2 by applying these constraints
  4. Supplier Mix: This set of constraints ensures product availability while maintaining the desired supplier mix (e.g., award at least 10% of each DC to a new supplier)
  5. Applying Penalties: In this case, we build the solution further by incorporating some penalties using the suppliers’ Index Scores
  6. Additional Constraints: Each category has its own unique set of requirements which determines the constraints that are applied; An example of this would be penalizing suppliers that are located far away from a DC if the product is time-sensitive

The process for this project spans across multiple rounds. The retailer participates in face-to-face negotiations between the two rounds to discuss the suppliers’ quote with each supplier and to explore any additional ways they could add value. The retailer also decides to share some feedback with suppliers in the second round based on their analyses. In most cases, increased transparency encourages suppliers to provide better quotes.

The example above was very simple with just three items being sourced. But you’re immediately able to get a sense of the possibilities where an increased number of Item-DC combinations can be sourced in the same project. Potentially, the retailer could also look for multiple commodities that could be fulfilled by the same set of suppliers and group these into a single project. Having this level of scalability ensures the advantage of better supplier quotes while maintaining the desired supplier-product mix in the analysis stage.

The retailer identifies relevant KPIs that allow them to effectively monitor the category over time. Examples of such metrics include the ratio of product to shipping costs (per DC and overall), suppliers’ on-time delivery performance (this must be applied to the overall index score), Expected vs. Implemented Costs (costing changes due to supply shortages, natural disasters, etc.), the cost of maintaining the supplier mix (aligned with sourcing strategy), etc.

Over a multi-year supply cycle, this process effectively drives savings while maintaining a strict hold on metrics that are important to the category and aligned with the retailer’s overall strategy.

When you combine this example with the example in the last post, it’s easy to see how optimization, when used in conjunction with combinatorial bidding, can add tremendous value to any strategic sourcing initiative. The advantage of being able to compare different possibilities within a short duration of time while following stringent sourcing methodology means your organization has a repeatable and result-oriented process on the right track to sourcing success.

Thanks, Ayush!