Reducing Costs Post COVID-19

Scott Setrakian
Making AI Make Money
9 min readJun 16, 2020

Why exiting the crisis will offer a once-in-a-generation opportunity to drive down the cost of goods and services, and how to achieve the benefits

Abstract

  • The prices of goods and services are set by economic properties driven by the relationship between supply and demand
  • The current COVID-19 crisis has caused a significant dislocation in the supply/demand balance: demand has shriveled to nearly nothing in many industries
  • When the economic recovery begins, there will be a once-in-a-generation opportunity for businesses to realize significant price reductions from suppliers
  • To achieve the benefits, buyers will need to contact multiple potential suppliers for each purchase, qualify them efficiently, and drive multiple bidding rounds to obtain the most attractive outcomes
  • Artificial Intelligence (AI) tools can be used to drive this process across a wide swath of goods and services purchases, which will enable businesses to achieve the full benefits of this opportunity in an efficient manner
  • The economic efficiency caused by this process will help fuel economic resurgence, to the benefit of all buyers, sellers and customers

Introduction

As the world economy reels under the impact of the coronavirus pandemic, executive teams are appropriately focused on making the necessary moves to stay in business.

COVID-19 has brought many of the relationships between businesses and their suppliers to a standstill, as customer demand has dropped to historically low levels in nearly every corner of the economy. This has caused a significant negative demand shock, with the pressure of plummeting customer demand moving down the chain to impact the suppliers of business goods and services.

We know academically that the economic pain of the moment will not destroy us, even though it’s hard to focus on anything beyond the immediate term. However, as the crisis evolves, businesses will need to begin to chart the course back to normalcy.

As the economy reawakens and companies ramp up, there will be a significant mismatch between supply and demand, as willing and ready supply will be available in volumes much greater than demand. This will provide a unique opportunity for companies to reset the costs of the goods and services they buy to run their businesses, setting the stage for a faster and more profitable recovery.

Realizing these opportunities will require significant work for buyers, who will have to employ disciplined best practices across the entire universe of purchased goods and services in a compressed time period.The time required for each purchase would typically outstrip the capacity of most organizations, forcing a prioritization of only the largest ticket items. However, new Artificial Intelligence (AI) software can automate major portions of this effort and provide buyers with significant leverage to maximize profitability across their full spectrum of purchases.

Pricing

We’ve all learned that the pricing for goods and services is set by the relationship between supply and demand. This bromide is undeniable, but it hardly explains how a particular price is set. While we can clearly see that demand has fallen considerably in the current climate, the measure of which that will impact prices now and in the future is less obvious.

Helpful guidance can be drawn from the world of commodity markets, where information about supply and demand is widely available, buyers and sellers are fully informed professional traders, and daily pricing moves fluidly as the supply/demand balance fluctuates. Pricing is set equal to the production cost of the least efficient (i.e., highest cost) producer whose supply is required to meet the last unit of demand.

Figure 1 is a highly simplified version of this phenomenon, demonstrating conceptually what was driving the price of a selected commodity — crude oil — on January 1, 2020. With demand at 100 million barrels per day, the price was set by the “other non-OPEC unconventional players” — the highest cost producers whose supply was required to meet the last barrel of demand.

Figure 1: Global Oil Market — January 1, 2020

Much happened to both supply and demand in the following 90 days.

  • First, demand softened from about 100 MM barrels per day to less than 90 MM, caused by decreases in worldwide consumption due to the coronavirus.
  • Second, Saudi Arabia, Russia and other OPEC members increased daily production, as their previous output control agreement expired without a renewal agreement.

This led to the relationships we see in simplified form in Figure 2 as of March 31, 2020: lower demand and higher supply combined to slam price from $70/bbl to $20/bbl.

Figure 2: Global Oil Market — March 31, 2020

Of course, the international commodity markets operate very differently on a day-to-day basis than markets for the diverse expanse of commercial goods and services.

In the huge majority of the business world, buyers and sellers have little idea of the relative economics of the various suppliers for any given good, and how collective demand stacks up against the range of costs from potential suppliers. That said, over time, and adjusted as appropriate for differences in quality, prices will ultimately settle roughly equal to the cost of the least efficient supplier whose capacity is required to meet demand.

However, short term pricing chaos can erupt when supply and demand balances are disrupted as they are right now with the collapse in demand. As the economy heals and demand regains momentum, buyers will have a unique opportunity to exploit the disruption and achieve significantly lower cost levels.

Winning When Supply/Demand Balance Favors the Buyer

As the economy gains strength on the back end of the COVID-19 shock, there will be an unprecedented “buyers’ market” for business goods and services. With the consumer awakening, green shoots of demand will permeate through the business ecosystem.

This new environment will be dramatically different than just prior to the meltdown. Many goods and services were being provided by suppliers that were operating generally at or near capacity, with sufficient demand for everyone and pricing supported by the highest cost player in the supply envelope (Figure 3). But in the new world, demand is emerging from a very low level (Figure 4).

Figure 3: Legacy Demand for Goods and Services

Figure 4: Post COVID-19 ‘Buyer’s Market’ for Goods and Services

In this new world, every supplier has an incentive to bid down to its unit cost to win business, and most suppliers will be satisfied with pricing today that is much lower than it was just prior to the crisis.

To realize the benefits, business must:

  1. Act quickly, across the full sweep of their purchases. As demand grows, the opportunity to significantly reduce costs will recede;
  2. Contact multiple suppliers for every purchase. The low-cost supplier will not simply present itself, and relative cost levels among suppliers are not openly apparent;
  3. Think from the perspective of the suppliers and offer deals that best align with their needs as demand revives. This will be a time when filling capacity and securing continuity will be most valuable to the supplier and lower pricing in exchange can be most easily negotiated. This can mean offering longer term and broader-scoped deals;
  4. Run multi-bid processes. It often takes competition between vendors to incentives each player to offer its best price.

These recommendations are commonsensical but following them can make a big difference in ultimate price realization. We can see theory confirmed by fact through Supplier.ai research of over a thousand purchase events in relatively normal supply/demand periods. Bringing multiple suppliers into
consideration results in significant tangible price decreases (Figure 5) and running multiple bidding rounds reduces price even more (Figure 6).

Figure 5: Additional Suppliers Drive Down Final Price

Figure 6: Multiple Bidding Rounds Drive Price Down Further

Making It Happen

The challenge of realizing the benefits of this opportunity is one of scale.

All of a company’s goods and services relationships need to be reset as quickly as possible, because as demand re-emerges and grows over time, the cost reduction opportunity will narrow. However, most companies’ purchasing departments are not geared to simultaneous procurement across the entire scope of current and potential suppliers.

To meet this need, advances in AI are enabling purchase support tools that can supplement human teams and automate many of the more complex and time-consuming steps in a best-practice purchasing process. A combination of natural language processing, machine learning, machine vision, and large-scale multi-source data integration is being deployed to power tools that address the challenges of identifying more suppliers and managing the negotiation process.

To realize the benefits, the software must:

  1. Ingest a purchase request with specifications (including text and images)
  2. Automatically identify a set of relevant suppliers
  3. Generate automated, intelligently worded, digital requests for interest and qualifications
  4. Accept and reply to statements of interest with a formal request for proposal
  5. Collect and prioritize incoming bids for management review
  6. Respond as appropriate with a negotiating request for best and final bid
  7. Provide management with a curated set of final bids for selection
  8. Recommend the supplier to be awarded the business

The process works with minimum human interaction and can be executed in a controllable number of days at nearly zero incremental cost. It is an essential capability to successfully achieve a step change reduction of costs as the recovery proceeds, and the cost/benefit of re-engaging suppliers and contractors becomes increasingly evident.

Conclusion

The recovery from the COVID-19 induced recession will be led by consumers. They will emerge from the downturn as they return to earning sufficient wages to pay for new things — from a meal at a casual-dining restaurant to a new apartment rental unit. In all cases, this consumer will be very price conscious.

Earning their business will require companies to provide competitive pricing, which will require an efficient cost structure. The companies that recover most successfully will be those that position themselves to meet customer needs most efficiently by resetting their input costs across the board.

These companies will need to simultaneously address the full range of purchased goods and services to significantly decrease costs in this new supply/demand environment. This mandate requires a level of activity for corporate purchasing departments that is well beyond “business-as-usual,” but the challenge can be met with the application of AI-enabled software. Such software can scale up the effort by automating many valuable but time-consuming steps and empowering the purchasing department to drive competitive advantage and propel their companies to superior performance.

About Supplier.ai

Supplier.ai helps large, multi-location businesses reduce costs by applying artificial intelligence and process automation when purchasing goods and services. Our software leverages the most effective purchasing strategies and manages each step of the buying process — including vendor identification, qualification, outreach, and negotiation — with minimal human interaction.

About the Author

Scott Setrakian — Vice-Chairman, Foundry.ai

Scott leads Foundry.ai’s San Francisco office. Prior to joining Foundry.ai, Scott was co-founder and Managing Director of Applied Predictive Technologies. Previously, Scott sat on the Board of Directors of Mercer Management Consulting and ran the firm’s global Oil, Gas, Chemicals, Pharmaceuticals and Process Industries Consulting Group. Scott received an MBA and an A.B. in Human Biology from Stanford University. He sits on the Board of Directors of the Buena Vista Funds, the William Saroyan Foundation and the San Francisco Zoo.

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Scott Setrakian
Making AI Make Money

Vice Chairman, Foundry.ai - Formerly co-founder and Managing Director of Applied Predictive Technologies - Stanford alum.