Category Archives: Decision Optimization

The Category Sourcing Scorecard – An Essential Tool for Collaborative Category Sourcing

Collaborative Category Sourcing is the foundation for eSourcing 3.0, whatever that happens to be. Why? As pointed out on SI, it is the only way to achieve savings above and beyond the limits of spend analysis and/or decision optimization, which max out at an average of 11% and 12% respectively, and this is especially true when the category has been strategically sourced (repeatedly). And the savings can be substantial. As pointed out in SI’s recent white-paper (sponsored by BravoSolution) on the “Top 10 Technologies for Supply Management Savings Today” (registration required), if the right combination of technologies are applied in the right way, they can often deliver 15%, 20%, 30%, and even 40% savings on hundred-million plus categories which were heavily scrutinized in the past and where little or no savings are expected. That’s why collaborative sourcing — which works best when it’s category focussed — is needed.

But how do you select the right category to start with? It’s certainly not as simple as selecting the category with the largest spend, the category with the least recent sourcing exercise, or the category coming up for renewal in six months. There are a number of internal, market, supplier, buyer, and category-specific factors that need to be taken into account — and this recent post on The Category Sourcing Scorecard over on CPO Rising did a great job of summarizing the vast majority of them.

Internally, the right category is the one with a contract maturing at the right time (which is typically three to nine months in the future, depending on the time it will take to do the sourcing project right), a documented sourcing history, a number of concerned stakeholders — who are willing to be engaged, and an accessible spend history (which, although not clear from the summary, should also contain usage, return, and inventory history).

From a market perspective, there should be enough competition to make an event worthwhile, the availability of one or more substitutes (if the current product has one or more patented, single-source, components), some bargaining power for the buyer, and barriers to market entry for both the product the buyer is producing and the capabilities offered by the suppliers (as, otherwise, new suppliers could set up shop overnight, sell to new buyers at cut-rates to establish business, and hurt your entire supply chain). In addition, the supply/demand (im)balance, which factors into the buyer’s bargaining power, should be known and relatively predictable.

From a supplier perspective, it should require some specialization (that the supplier can use to set itself apart), provide for profit margins, contain value-add components (valuable to the supplier and your customers), and a level of technical excellence. In addition, there should be suppliers who are financially stable, innovative, and willing to work with you to find substitute raw materials, components, designs, or production processes that will take costs down and push quality up.

From a buying perspective, there should be the potential to achieve some supply assurance, minimize production impact, save money, and require a production volume that will be attractive to the suppliers. In addition, there should be some signs that costs and risks can be reduced significantly enough to make the project worthwhile. This could take the form of falling raw material prices, the recent introduction of innovative new manufacturing technologies, or increased market competition.

From a category perspective, impact, complexity, and lead time will definitely be key factors, as noted by the post, but so could organizational importance, sustainability, and C-suite support. This will often be the hardest category to judge and score.

Which brings us to the following question – how do you score the scorecard? Do all the categories have equal weight, or are some more important than others? Making them all equal is certainly a valid starting point, as it will let you quickly eliminate categories that are really bad (with low scores in multiple categories), but may not be enough to let you choose between a category which scores great except for market factors, another which scores great except for supplier factors, and a third which scores great except for category factors.

In reality, the right scoring framework will be dependent upon the ultimate goal. If the ultimate goal is (still) to reduce cost, then the market factors should get the most weight. If supply assurance is the most important goal, then the buying factors should get the most weight. And if innovation is the desired outcome, the supplier factors should likely get the most weight. While it’s hard to make a hard and fast rul, here’s a good starting point for weighting.

To Focus On:	Put a Higher Weight On:
Cost	Market Factors
Supply Assurance / Risk Mitigation	Buying Factors
Innovation / Value-Add	Supplier Factors
Stakeholder Inclusion	Internal Factors
Organizational Strategy	Category-Specific Factors

Informationalization Is Important

Simply put, the more informed you are, the better you are going to be able to source and procure. And this recent article over on the HBR blogs on why you need to integrate data into products, or get left behind just scratches the surface.

As the post notes, virtually every product and service can be made more valuable through informationalization. The GPS example provided is classic. Turn-by-turn directions make the car more valuable as the driver can keep his eyes on the road, get to his destination faster, and, during delivery, avoid left turns that just lead to extended idling at busy intersections. And, as predicted by Stewart Taggert, half of the value in the delivery of a shipping container from halfway around the world would be in the data associated with the container. Good information allows you to calculate in-transit time, and associated costs, loading and unloading costs, storage costs, insurance costs (as you can appropriately determine the chance of accidental loss or theft), etc.

But the best example of the value of informationalization is how it allows you to optimize your sourcing decisions. The more you know about your product options, shipping options, associated costs, and the inherent value of each product versus your other options, the more accurately you can model your options. The more accurately you can model your options, the better chance you have of determining the solution with the lowest cost, the lowest risk, the highest value, and the best value (defined as risk reduction, profit generation capability, etc — whatever makes sense) to cost ratio. And this is how leading Supply Management organizations can save 12%, on average, off-the-top in an optimization-enabled sourcing event — and even more if they collaboratively work with their peers to identify all of the options that may be available and all of the associated tradeoffs. As pointed out in SI’s recent paper on “Top Ten Technologies for Supply Management Savings Today”, integrated, collaborative sourcing can often identify savings opportunities of up to 30% or 40% on categories that were exhaustively combed for savings in the past.

Plus, good information allows your organization to:

constantly improve products and services by way of the fact that you are able to
collect more relevant, timely, accurate, detailed, and integrated data.

And when you have relevant, timely, accurate, detailed, and integrated data, you can take out your best-of-breed data analysis tool, use the tips and tricks SI outlined in it’s free e-book (co-authored by Bernard Gunther of Lexington Analytics, now a division of Opera Solutions) on Spend Visibility: An Implementation Guide, and extract even more value for the organization by optimizing not just Supply Management spend, but utilization, service, warranties, Marketing & Legal spend, and every other product and service activity that burns capital and/or creates organizational value.

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:

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
Low Cost with Capacities: This scenario again uses the lowest cost per case but now considers supplier capacities and discounts while calculating individual awards
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
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)
Applying Penalties: In this case, we build the solution further by incorporating some penalties using the suppliers’ Index Scores
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!

From Strategic Spend to Strategic Value-Add, Part III

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, in our last post, 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. Today, we are going to present our 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.

We start with a logistics sourcing project run by ‘Transport Corp.’, a 3PL (third-party logistics) company that wants to source three routes — Route A, Route B and Route C. Each route has a certain volume (number of truckloads) that needs to be fulfilled. Transport Corp. wants to utilize the combinatorial bidding and optimization process and invites three freight carriers to bid in this project — ‘Carrier A’, ‘Carrier B’ and ‘Carrier C’.

Transport Corp. has the following volume information.

Route	Origin	Destination	Volume	Mileage
Route A	Atlanta, GA	Akron, OH	1000 loads	600 miles
Route B	Bakersfield, CA	Buffalo, NY	2000 loads	2500 miles
Route C	Chicago, IL	Cincinnati, OH	3000 loads	300 miles

Transport Corp. wants the carriers to provide the following inputs:

A ‘Rate per Mile’ for each lane
An estimated ‘Transit Period’ (number of days from the origin to the destination)
A ‘Capacity Commitment’ (number of loads each carrier can fulfill)
Any ‘Lane Bundles’ that would entail a lower rate across the bundle

The freight carriers each quote the following:

Carrier A
Route	Origin	Destination	Rate per Mile	Transit Period	Capacity Commitment
Route A	Atlanta, GA	Akron, OH	$1.00	1.0 days	200 loads
Route B	Bakersfield, CA	Buffalo, NY	$0.75	2.5 days	1000 loads
Route C	Chicago, IL	Cincinnati, OH	$1.00	0.5 days	2000 loads

Carrier A doesn’t have any additional bundle discounts to provide.

Carrier B
Route	Origin	Destination	Rate per Mile	Transit Period	Capacity Commitment
Route A	Atlanta, GA	Akron, OH	$1.00	1.0 days	800 loads
Route B	Bakersfield, CA	Buffalo, NY	$0.75	2.0 days	1000 loads
Route C	Chicago, IL	Cincinnati, OH	$1.20	0.5 days	1000 loads

In addition, Carrier B says that if given all the volume in Route A and Route B, they’ll provide an additional discount of 5%.

Carrier C
Route	Origin	Destination	Rate per Mile	Transit Period	Capacity Commitment
Route A	Atlanta, GA	Akron, OH	$1.25	1.0 days	1000 loads
Route B	Bakersfield, CA	Buffalo, NY	$1.00	2.0 days	2000 loads
Route C	Chicago, IL	Cincinnati, OH	$1.50	0.5 days	3000 loads

Carrier C doesn’t have any additional bundle discounts to provide.

Initial Results (Low Cost without Capacities or Discounts)

With the carrier Lane Rates (cost for shipping all the loads on each lane), it’s possible to get a ‘Total Cost’ comparison. Looking at the numbers simplistically (i.e. without considering any capacities or discounts), we can infer the lowest cost carrier easily. In this case, looking at the table below, it’s easy to identify the winner overall would be Carrier A if one was just looking at the carrier prices

Route	Carrier A (Full Volume)	Carrier B (Full Volume)	Carrier C (Full Volume)	Winner
Route A	$600,000	$600,000	$750,000	Carrier A OR Carrier B
Route B	$3,750,000	$3,750,000	$5,000,000	Carrier A OR Carrier B
Route C	$900,000	$1,080,000	$1,350,000	Carrier A
Full Business Total Cost	$5,250,000	$5,430,000	$7,100,000	Optimal: $5,250,000

Low Cost Considering Discounts

However, we know that Carrier B has offered a 5% discount on Route A and Route B if awarded both these lanes. Let’s consider this possibility in the table below. It’s apparent that after applying the discounts, Carrier B becomes more favourable not only on Route A and Route B but also overall (see the last row showing the total cost for awarding all routes to a single carrier).

Route	Carrier A (Full Volume)	Carrier B (Full Volume)	Carrier C (Full Volume)	Winner
Route A	$600,000	$570,000	$750,000	Carrier B
Route B	$3,750,000	$3,562,500	$5,000,000	Carrier B
Route C	$900,000	$1,080,000	$1,350,000	Carrier A
Full Business Total Cost	$5,250,000	$5,212,500	$7,100,000	Optimal: $5,032,500

Optimal Payment Considering Discounts, Capacities and Business Constraints

In the same problem, we now analyze the possibility of honouring carriers’ ‘Capacity Commitment’ numbers. In addition, Transport Corp wants to mitigate risk and therefore wants to award each route to at least two carriers. We now see that a simple problem with three lanes and three carriers quickly becomes hard to solve. This is where the power of optimization comes into play, allowing us to quickly compute the best solution. Here’s a look at the solution if we want 2 winners per route and also want to honour capacity commitments. Carrier B’s discount doesn’t materialize in this scenario as no carrier gets a full lane award.

Route

Winner 1

Winner 2

Route A

Route B

Route C

Volume Awarded	Payment	Winner
200	$120,000	Carrier A
1000	$1,875,000	Carrier A
2000	$600,000	Carrier A

Volume Awarded	Payment	Winner
800	$480,000	Carrier B
1000	$1,875,000	Carrier B
1000	$360,000	Carrier B

Optimal Total Payment $5,310,000

Taking this one step further, it’s possible to visualize cases where Transport Corp wants to incorporate some penalties for carriers with higher ‘Transit Periods’ to arrive at another solution that has a better overall lead time. In this manner, several what-if scenarios can be run in a short span of time. These types of creative analyses can be performed while simultaneously allowing carriers to submit all the information they have. However, a process also needs to be instituted where the awarded scenario is closely evaluated against previously implemented rates. It is also useful to do some sensitivity analyses to understand how the award alignment changes if the payment is relaxed by a certain percentage. Monitoring these in addition to carrier performance and quality metrics allows Transport Corp to arrive at an optimal decision that considers all factors and is right for their business.

Thanks, Ayush.

From Strategic Spend to Strategic Value-Add, Part II

Yesterday’s post discussed the importance of supply and demand chain integration as the key to an efficient, profitable, and fluid business. These processes, which should be integrated with the strategic plan, should utilize Strategic Sourcing and it’s ability to drive ‘savings’ year after year. And the best Strategic Sourcing processes are those that combine combinatorial bidding and optimization, which allows an analyst to run several what-if scenarios in minutes and generate reports that show exactly how the overall spend distribution changes as newer business processes are taken into account. In today’s post, we will discuss the requirements for a strong and measurable sourcing process.

A strong and measurable sourcing process normally exhibits the following characteristics:

Integrated Processes
Using a rolling window of a few years (usually two to three years), strategic sourcing projects should be strictly monitored to understand expected vs. implemented costs and ensure implemented costs are in line with the strategic plan. The suggested time window works best as it allows room to respond to market dynamics while maintaining a medium to fairly long-term focus.
Creative Analyses
Technologies like combinatorial bidding and optimization can be used creatively. Today’s tools offer extreme flexibility in terms of the types of data that need to be captured and the limitless possibilities for analysis. Businesses must look at ways of incorporating qualitative information and ongoing metrics (e.g., favouring suppliers who have been able to maintain the price for the duration of the contract) in the analysis process.
Collaboration
Supplier collaboration is key to insure the success of any Strategic Sourcing process. In this era of real-time communication, it’s vital to collaborate with suppliers on an ongoing basis while providing them dynamic feedback to improve the overall quality of the sourcing process.
Economies of Scale
Businesses must look to increase the scale of projects. Several bidding tools allow ridiculously large numbers of bids to be captured and analyzed at an extremely granular level. Increasing the size of projects not only means increased productivity and cycle time (and thereby cost) efficiencies for the organization. It also allows you to take advantage of economies of scale in the bidding process while maintaining the appropriate level of detail during analysis.
Benchmarking
Sourcing projects should be used to create financial benchmarks allowing organizations to understand industry trends and the impact of specific changes in methodology. When the market fluctuates, effective benchmarking techniques help understand the immediate and long-term effects and allow organizations to mitigate risk.

The easiest way is to just get started using the guidelines above and then steadily build and refine the process. No one solution will work for all organizations, but taking the lather-rinse-condition-repeat approach with special focus on ‘conditioning’ will ensure maximum optimality. Supplier collaboration should take center-stage, especially when the focus is on long-term profitability. Utilizing the latest technology to capture all of their requirements ensures a wholesome process and a meaningful relationship with suppliers. As the process gets more streamlined, productivity benefits (along with specific measurable savings) will mean increased throughput in the entire sourcing process without losing track of strategic goals. This is how your Supply Management organization turns the organization’s Strategic Spend into a Strategic Value-Add for the corporation as a whole.

In the posts that follow, we will illustrate this with a couple of 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 above.