Category Archives: Sourcing Innovation

Optimization: Is it at least time to move beyond logistics and indirect sourcing?

A big focus of this blog is, of course, Strategic Sourcing Decision Optimization (SSDO), one of the few advanced sourcing methodologies guaranteed to save your organization, on average, 12% if correctly applied (as demonstrated in two back-to-back studies by Aberdeen last decade and discovered over and over again by organizations applying it properly) and the doctor‘s specialty.

But it’s not the only place you can apply optimization in Supply Management to save money. Another area, as covered a number of times on SI, is Supply Chain Network Optimization (SCNO). And, of course, some companies just focus on the intersection and do Logistics optimization. But this is not everything that can be done, or should be done, especially in an age where many industries now see The End of Competitive Advantage and don’t actually own physical assets, leasing them as need be to create the products and services desired by their prospective customers.

In this situation, what matters is Asset Optimization, where you optimize a one-time dynamic network to minimize sourcing, network, and logistics costs to minimize the total supply chain costs associated with the product you wish to produce. This is easier said than done. In sourcing, you are mainly considering bids, lanes, and associated costs to compute the optimal TCO (Total Cost of Ownership), and if lifetime costs and metrics are available, or TVG (Total Value Generated) with respect to a fixed situation. In network optimization, you are optimizing the location of owned factories, supplier production centers, warehouses, and retailers to optimize the distribution costs. But in asset network optimization, you have to simultaneously consider the network and associated distribution costs, the sourcing requirements and associated production costs, and the costs of using, or not using, the resources you already have available and contracts you have already negotiated. In addition, you have to consider the risks associated with each potential supplier and location, the sensitivity of the overall asset network to each supplier and location (and is there a single point of failure), and the ability to dynamically alter the network should a failure occur or customer demands change.  And track all of that information.

Plus you have all of the difficulties associated with each type of optimization. With respect to the network, there will be many alternatives for production site, each site will have multiple, and different, asset lines, and each asset will be qualified for a certain operation with respect to a certain product. In addition, some assets will be more efficient and cost effective, and unqualified assets will have a qualification/certification step, which will require limited manpower – a variable that does not need to be modelled in traditional sourcing or SCNO models. It’s a very difficult problem that requires modelling of multiple types of variables and constraints at multiple levels at multiple times. And this last requirement makes the model even more complex.

Plus, in a traditional sourcing model, you don’t really need to consider “time”, as it doesn’t matter how often the trucks deliver your product, just how many trucks are needed to deliver your product as you are billed FTL or LTL by the delivery. And it doesn’t matter what production schedule the supplier(s) use(s) as long as your products are ready on time, so only the total volume need be considered. But when you are dealing with production models, especially when trying to dynamically construct and optimize an asset network, production schedules are significant. If a certain location only has 30% of capacity left available and can only schedule it during a given time-frame, that has to be taken into account. If some of the products have to be delivered before they can complete the first production run, then there has to be a location that is able to do so. And if a continual supply is needed over nine months, the production cycles should more or less line up with minimal overlap as, otherwise, inventory costs would soar.

It’s a complicated problem, but one that is becoming more and more important in fast moving industries such as fashion and consumer electronics — and one that most SSDO providers can’t address. Why?

First of all, they don’t track the necessary data.

Secondly, they don’t have the right underlying optimization platform.

Third, they don’t have the skills to build the right model.

But recently, a few of the bigger players with optimization have started not only tracking all of the direct (material) sourcing requirements, but assets as well.  So the data is there.

Secondly, a few of the optimization platforms have become significantly more powerful and flexible (and now have the necessary computing power under the hood to support them) and could, at the very least, run a series of optimization models (according to different time-spans, which minimizes the need to consider complex timing constraints in a single model) to tackle problems such as this.

Thirdly, there are independent experts with decades of experience who can help design the right model.

So why are none of the big players doing it?  It seems logical, and soon necessary, if an organization wants to continue to identify, and capture, new sources of value year-over-year.

Sourcing Talent Is Rare, Especially Since They Also Have to Manage Risk

Yesterday we told you that Sourcing, like the many facets of Supply Management, is not as easy as it seems as the skills required to go from RFI to award are numerous and compose up a laundry list that is rare to find even in most sourcing teams, yet alone individuals, including:

  • (Cost) Analysis / Market Analysis
  • Logistics
  • Needs Identification
  • Negotiation
  • Project Management
  • Resource Management
  • Supplier Identification
  • Trend Identification
  • … and …
  • Risk Management

The last of which we left off of yesterday’s list because this is a list in itself. You see, in today’s Sourcing landscape, turbulence is not just what you experience in an airplane on your way to a site visit – it’s what you experience trying to manage your supply chains on a daily basis. Just like fluid flows can become highly irregular with the slightest perturbation, so can the flow of goods in today’s ultra-outsourced ulta-global supply chains.

Turbulence is a hidden risk in every supply chain, and one most organizations are never prepared for because, when a risk assessment is done, it is always focussed on easy-to-identify technological, economic, market, financial, organization, environmental and social risks — not random events that can temporarily interrupt your supply chain and cause temporary disruptions with serious financial or brand consequences. Temporary disruptions which, if regular in nature, can put your organization in real jeopardy and temporary disruptions, which, by their very nature cannot be planned for or even identified in an up-front risk assessment.

For example, when buying product components from China, an experienced risk team is going to identify:

  • Supplier Risk
    Are they financially stable? Will they adequately protect your IP? etc.
  • Factory Risk
    Is the quality acceptable? Are there workplace or safety hazards that could shut it down?
  • Port Risk
    Will the product be safe? Is there any danger of strike or overcapacity? On both sides …
  • Export and Import Risk
    Are all regulations adhered to? RoHS? WEEE? Has all the paperwork been completed and submitted on time?
  • Technology Risk
    Is the real-time product tracking and distribution system reliable? Backed Up? Integrated properly with all parties?
  • Environmental
    Is the product being made or stored in areas subject to regular natural disasters such as hurricanes, typhoons, earthquakes, etc.?
  • Social Responsibility
    Is the product conflict / slave labour free? Are all employees of all partners treated equitably? Is the product, and its production, environmentally friendly or at least environmentally safe? Can the product be safely disposed of?
  • Market
    Will the market still want your product when it is available? Is a competitor going to beat you to the market?
  • Economic
    Will the economy maintain or improve? Or will it worsen, leading to reduced demand across the board? What is the job forecast looking like in target markets – job loss in those areas can weaken consumer demand.

and a few dozen other common risks from the risk identification and management playbook.

But it’s not going to identify one-time random events such as:

  • Unlikely Terrorist Attack by a random civilian who goes postal and, when trying to go postal, thanks to a gas leak, accidentally blows up a building due near the docks and causes the port to become unaccessible for 3 days
  • Delayed Delivery due to Paperwork Mix-Up
    One truck is scheduled for delivery of your product to your distribution warehouse, another for mid-term storage at a competitors warehouse on the other side of the continent. And because the small carrier you’re using doesn’t have real-time inventory tracking, and your product is scheduled for JIT delivery, the mix-up isn’t detected until the expected delivery date when your product is half-way across the country.
  • False Stock-Out due to Inventory Mis-Key
    The clerk enters 8,000 units instead of 80,000 into the system, stores exactly 8,000 in the proper location in the ware-house, and puts the other 72,000 units of your hottest selling product at the back of the warehouse reserved for discontinued inventory.

Each of these events can happen, and each can cause a real, unexpected, and unpredictable turbulent impact to your supply chain. Are you ready for it? Can you sourcing team react and adapt when it does?

Can You Even Identify Savings to Realize?

A few week ago we sort of put the cart before the horse when we noted that Realizing Those Savings is No Easy Feat because many organizations will undertake sourcing events, cut contracts, but then fail to realize 30% to 40% or more of the expected savings (and this has been the case since AMR’s classic studies on savings realization over a decade ago, well before they were bought and absorbed by Gartner).

So even though it’s sort of putting the cart before the horse to put an infrastructure in place to capture savings, without such an infrastructure, identified savings won’t realize. So it’s really not a bad idea to start with Procurement platforms that capture savings, because you need them.

However, today we’re going to assume you have such an infrastructure in place, and ask the question, even if you do, can you identify real savings? It’s a lot harder than you think. It’s not the lowest cost. Or the lowest landed cost. It’s the lowest total cost of ownership … over the product lifetime, which could be for years if you offer a warranty. Because not only is their warranty costs, there are return logistics costs as well!

But it’s not easy to capture all of the relevant costs in an RFI, nor is it easy to build the models that can accurately model total lifetime cost of ownership in Excel. That’s why the doctor has been promoting optimization-backed sourcing platforms for years — only those platforms can accurately compute lifetime costs and allow for the right fact-based negotiations and award decisions.

But it’s not just cost that needs to be considered, it’s value and service levels. You need customers to want your products, and you need delivery times you can depend on. But value and service guarantees cost money, and in inflationary markets, that means costs just go up and up.

If market prices are increasing, and you need to improve service levels and add more value-based features to appease customers, can you even identify savings?

The answer is, without the right platforms that allow you to look at your costs holistically and find ways to minimize them beyond just a price-based bid, is that you can’t … at least not after the first time you’ve “strategically sourced” a product. Additional savings will come from better category definition and alignment, smarter network design, better inventory management and aligned inventory levels, and up-sell opportunities from more appropriate, sustainable, sourcing selections.

And that will require the right upstream technology that will include the following:

  • supplier discovery to identify the right suppliers
  • optimization backed sourcing to make the right value-based decisions
  • supplier management to make sure the relationship and performance can be managed
  • risk management to identify, monitor, and mitigate potential disruption risks
  • analytics to analyze past, current, and ongoing price and KPI performance
  • CLM to manage the contract, obligations, and identify the time for renewal, renegotiation, or termination

And that’s why you see a proliferation towards Strategic Procurement Technology Suites and why the doctor has teamed up with Spend Matters to analyze them. Platforms are becoming key to identifying real, sustainable, savings — but only if they are the right ones for the customer base they are installed in.

Don’t Underestimate The Importance of Workflow …

One of the big reasons that, even four years after the doctor told you about the Procurement Damnation of Project Management most vendors haven’t done anything as per my post last week (read here), is that they have little or no workflow management, a capability that we told you is vital to the modern platform in our post three weeks ago where we were Digging into the S2P Tech Foundations.

Workflow is more than just a platform’s ability to guide a buyer through the application to complete a specific task, workflow is the ability of the platform to be adapted, and adapt, to the processes an organization needs to support, and the ability of the platform to support management of those processes and the projects that create them.

This is something a large majority of software application developers don’t get, and, as a result, something a large majority of applications don’t have. And it’s something that needs to be baked in at the foundations of an application, or the application will never have good workflow capability.

Why is there so little? Because classic application design philosophy, inspired by the waterfall model of software development, has been:

  • identify a problem
  • define the problem
  • translate into requirements
  • detail into functional specifications
  • build a software solution that implements the functional specifications
  • iteratively test and debug until stable enough for release

And the agile philosophy didn’t change much. The only difference is that instead of attacking the full extent of the problem and translating the full problem into requirements, you focused on a core piece of the problem, translated it into requirements, fleshed out, built, and then went back and extended the core, extracted the new requirements, fleshed those out, built new pieces and integrated into the existing solution, and so on.

No thought was given as to how to create a set of self contained units that could be strung together in a workflow to solve bigger problems, which is key to providing a platform that would allow the workflow to be extended and altered and allow the organization to change over time.

And if you’ve invested five to ten years in a platform that has been profitable, do you really want to go back to square one and build it up from foundations the proper way? Especially if you think you can still make money on what you have? Probably not.

And looking to the bigger picture, that’s the state of Procurement 2.0 and why we need new, evolutionary, platforms if we are every going to realize the extent of Procurement 3.0. But that’s another post.

AI Won’t Solve Your Talent Problem!

Talent is Still the Biggest Issue Facing Procurement Today … so what are you doing about it? (Besides still cutting the training budget as soon as cashflow gets tight and delaying necessary system purchases because you can’t take a long term view.)

As SI has repeatedly said, Procurement Pros need to be jacks of all trades and (almost masters of all but in reality) masters of one (Procurement) (Trend #17), and that’s no easy feat when the skills and knowledge a Procurement pro needs to do her job effectively increases every year.

And new AI / Cognitive technology doesn’t decrease the skill sets and knowledge required, despite what one may think. In fact, it only increases it Why? First of all, do you have assisted intelligence, augmented intelligence, or a cognitive system that is as close to true AI (artificial intelligence) as one can get with today’s technology? And, more importantly, does your Procurement Pro understand what you have, what the differences are, and what the respective limitations are.

If the solution is just assisted intelligence, then it’s an automation solution (RPA) with some expert knowledge encoded to handle typical situations with certain assumptions. If the assumptions are invalid, will the software detect them? If the situation goes beyond the realm of typical, will the software detect it? And even if the software does, will it be able to do anything without expect guidance? An example of assisted intelligence is an automated auction where the platform automates the sourcing of an item or service designated for auction among pre-approved bidders and goes from demand specification to final award without human input. But will it detect if the bids are complete? Within expectations? That bidders are bidding on the right product or service? Maybe the buyer assumes shipping included, but the bidders aren’t including shipping, and since the system only has a ceiling, it doesn’t know that the bids are way too low, and awards to the lowest bidder, that is actually the highest as the bidder is the furthest away and has the highest transportation cost.

Same goes for augmented intelligence. However, with augmented intelligence, the software goes beyond simple RPA with fixed expert rules — it is able to analyze a lot of parameters and pick the closest matching scenario and associated workflow. For example, an opportunity analyzer that takes into account current market pricing, supply availability, bidder responsiveness, current market trends (upward and downward), projected demand, etc. and advises the buyer on the type and timing of the sourcing event as well as the best workflow. But what if the market pricing is a week out of date and the market price just jumped up 20% (due to a fire in a major supplier’s plant) and reversed the trend? That changes everything, but the solution may not detect it and instead advise the worse sourcing event.

Cognitive platforms that continually monitor the situation are better, and if they learn from the actions the expert users take over time, better still, but they still can’t cope with an exception al situation they haven’t been coded for, or trained for. For instance, even if they detected that last minute spike in pricing that reversed the pricing trend and, thus, changed the optimal sourcing strategy, will they understand why the spike happened and the best alternate strategy? Or will they default back to the recommending the default strategy in a situation where costs are increasing … e.g. switching from auction to multi-stage RFI with optimization-backed analysis? Neither is right in this situation. In this situation, its extend the current contracts with your non-affected suppliers, increase the number of units, and lock in supply early, even if cost is higher.

In all these situations, only a knowledgeable, experienced, and sometimes expert Procurement Pro is going to be able to make the right decisions … and a novice relying on the systems is going to make the worst, and most costly, decision imaginable.

There’s no true AI, no all knowing software, and no replacement for a real expert.

The reality is that, at the end of the day, these systems make your experts more efficient — and multiply their productivity — they don’t replace their expertise.