Category Archives: Spend Analysis

Scalue Wants to Scale Up Your Strategic Procurement with Strategic Spend Analysis

Scalue is a spend analysis company that was founded in 2018 by veterans of Procurement with two decades of experience in DĂĽsseldorf, Germany to help companies identify various, immediate, areas of potential savings, improve their overall purchasing processes, and, most importantly get started quickly (as many large organizations can take between 6 and 24 months just to get their data foundation in order if they take the traditional route and start with a consultancy partner that starts with a data cleansing, classification and enrichment project before building the first cube and starting the opportunity analysis).

Scalue was built to be ready to use the minute that you loaded the starting data set from either

  • a set of flat files or Excel workbook (which are auto-mapped if you use their data model and/or standard field names) or
  • the ERP/MRP (AP) (for which they have a library of pre-built integrations to the majority of the major ERP systems; they may need minor customizations, but those are usually quick to accomplish, and if you need something custom, they do have certified ERP integration partners).

Of course, how ready it is will depend on how good your classification is in the raw data you import. For the majority of companies just starting on their data foundations and/or spend analysis, chances are their data classification is very poor. Fortunately, classification in Scalue is quite easy and can be done by supplier, material group, material, GL coding (if available), invoice (line), or a combination thereof. Scalue typically begins an engagement with a working session to help, and guide the users on creating/updating their categorization and doing the initial spend mappings.

Updates are on your schedule. Most customers prefer monthly (so they can share and do consistent analysis), but they can retrieve updates weekly, daily, or even hourly if you want with a direct (ERP/MRP/AP) system integration or as often as you update an incremental file without an integration.

While Scalue has experimented with multiple AI technologies for classification, they do not use any technologies across the board, and instead use specific instances on specific use cases (for initial classification rule creation, but all AI mapping rules can be deleted or overridden), because they have found, as the doctor knows all too well:

  • classification accuracy in direct, especially when dealing with a multi-national enterprise that sources in different countries that use different SKUs and coded product descriptions for the same product and do so in multiple languages, is poor. Maybe 90%, but you really don’t want 10% of your data misclassified, especially if it relates to high spend transactions
  • classification consistency with the black box is poor, while retraining from corrected classifications will correct some classifications, others that were right are now wrong
  • while it sometimes can produce starting rules, you can’t always trust the confidence and still need to verify all the rules manually
  • a good mapping process will get a spend analysis / data management team to fairly high accuracy (90 to 95% +) in just a couple of days at most (even in large organizations) and the rules are 100% accurate and reliable

When the average direct buyer enters Scalue, the first thing they see is the Cockpit Dashboard in the Management module which helps them understand their spend and drill in to find immediate opportunities. The cockpit, like any good entry dashboard, summarizes spend volumes, suppliers, spend by material group, supplier, and measure for a time period and allows a user to drill in by any (pre)defined dimension or measure in each of these drill downs, and order the drill downs in any order they like. Each drill down brings up a dedicated report screen, where the user can not only drill, but select a dimension/measure subset as well. When a user identifies a high spend (sub) area that they want to address, they can kick off an initiative, which we will discuss later.

Scalue promises a return of 1% on total addressable spend in your first 12 months and has consistently delivered across its customer base for the last few years. This might sound low compared to the numbers quoted by indirect spend analysis providers, but one should remember the following:

  • while the average return on an indirect category that is strategically sourced during non-inflationary times will be 5% to 10% with a modern sourcing solution and good insight,
  • and the average return in the tail spend will be 10% to 15%,
  • the average return in a direct category is usually in the 3% range (as it’s much more carefully evaluated and managed by a company that needs to invest millions in materials and goods to serve its customers)
  • and contracts are usually three (3) to five (5) years.

Thus, if you save 1% a year despite much of the spend being locked in by existing 3+ year contracts, with a average ceiling of 3% on savings on (re)sourcing. that’s actually quite good — and for a company buying 500M in direct, that’s 5M straight to the bottom line in the first year on direct spend alone!

The next step for most users is either the ABC Analysis or the Business Development overview, which is where they will typically go next. The Business Development screen summarizes purchasing volume, regions of origin, and ABC analysis (by country by default) which can also help an analyst dive in to find immediate opportunities (by focussing in on high volume categories where the spend trend is going up, categories that are being sourced from too many regions and present a consolidation opportunity [without increasing risk], and high spend material groups that might be going unmanaged).

The ABC analysis, that the user can drill into from the Business Development tab (or jump straight to), allows the user to see the material group or supplier split by the top 80%, next 15%, and final 5% (or 70/20/10 or however they want to define the A,B,C spend ranges under their interpretation of the Pareto Principle) and just drill into the spend that matters the most.

In summary, users usually start with these three Management Dashboards

  • Cockpit – an overall spend overview: volume, cumulative cost variance, top suppliers, top measures, etc.
  • Business Development – looks at price spreads across all of your products to find immediate opportunities (by consolidating to lower cost parts where possible)
  • ABC Analysis – the standard ABC analysis that groups material groups into high spend (A), medium spend (B), low spend (C) using the modified 80/20 rule into 80/15/5 (which can be modified as desired by the customer); this allows an analyst to focus into the high spend / critical material groups first and then see what groups or suppliers are out of control in the tail

Once the initial exploration is done, most analysts will move to the Structure dashboards:

  • Invoice Compliance – how much spend is billed not using contract rates
  • Contract Compliance – how much spend is off-contract that should be on contract
  • Payment Terms Optimization – looks at payment terms and early payment (cash) discounts across suppliers and helps you optimize payment terms and time-frames
  • Delivery Time & Performance – average delivery time, on-time, late, by supplier
  • Terms & Conditions – where they can analyze payment terms and delivery terms across a supplier (cluster) or material (group); keeping on top of this is very important if your suppliers provide early payment (cash) discounts or charge interest for late payments (and hold your critical orders until invoices past due are paid); includes a portfolio view of associated value with each material group-term pairing

From there, they will usually progress into the Control Dashboards:

  • KPI Dashboard – a customizable dashboard that centralizes your KPIs of interest
  • Material Cost Variance – summarizes the cost variances across materials and material groups
  • Report Builder – allows an end user to build a report on set of dimensions and/or measures in the system

Once they have completed their analysis, the users will probably want to set up initiatives (projects) to (re)capture savings and hit the 1% reduction on total spend Scalue can deliver within the first 12 months. To do this, they will move over to the Action Hub:

  • Tracking – the main dashboard that provides an overview of all (open) initiatives including [savings] type, forecast, and captured to date
  • Approvals – the approvals dashboard where an admin can accept, and lock, dates, forecasts, entered amounts (to date), etc.
  • P&L Savings – the savings against the P&L by month for a given time-frame
  • KPIs – allows for a deep cross-initiative analysis that computes averages and statistics across initiatives, categories, manufacturing groups, and other KPIs of interest by time periods of interest
  • Admin – allows for customization of initiative management — the admin can define the phases, the employees who can edit initiatives, the priority classifications, the savings types, the project statuses, and other dimensions upon which the KPIs will be based

As with all spend analysis systems, the end user administrator can setup and maintain the category tree, material groups, supplier, and invoice categorizations completely self serve and inspect it at any time through the module for Data Health:

  • Material Clusters – group material groups by product lines, related uses, or another common denominator you want to be able to do analysis by; see the allocation by spend or volume, and drill into the percentages
  • Supplier Clusters – group suppliers by parent company, region, or another common denominator you want to be able to do analysis by; see the allocation by spend or volume, and drill into the percentages
  • Category Tree – define the category tree and the overarching material groups
  • Material Group Categorization – dive into a material group and map materials
  • Supplier Categorization – classify suppliers by material category
  • Material Categorization – supports product/SKU level mappings
  • Invoice Categorization – define line level overrides where needed

Once a company has mastered the basics and taken full advantage of the standard dashboard and analysis that deliver almost immediate payback, they can do the more advanced portfolio analysis that allows them to analyze portfolios by buying power, supply risk, ESG/CSR, etc. as long as they have the data to do so (and have defined the appropriate supplier/material group clusters). Scalue can pull in the data from the ERP if it exists, and if it doesn’t, it can build (or the end user can build) questionnaires to collect the data from organizational users. This advanced analysis is accomplished in the Strategy Hub:

  • Questionnaire – used to collect baseline data to provide a foundation for portfolio analysis
    (around ESG/CSR, Organizational Buying Power, Supply Risk, etc.)
  • Supplier Portfolio Specifications – used to collect specific data for portfolio segregation by supplier (cluster)
  • Supplier Portfolio Analysis – analyze the spend by the desired portfolio breakdown, dashboard is customizable on implementation
  • Material Portfolio Specifications – used to collect specific data for portfolio segregation by material group (cluster)
  • Material Group Portfolio Analysis – analyze the spend by the desired portfolio breakdown, dashboard is customizable on implementation
  • Combined Portfolio Analysis – see the portfolio analysis by supplier (cluster) and material group (cluster), dashboard is customizable on implementation

Finally, for ERP customers that have multiple years of data in their ERP, they also have a ProcessView module:

  • Dashboard – a summary of the process analysis which focuses on process discovery, lead time analysis, and a breakdown by lead time cluster
  • Statistics – summarizes statistics related to the different steps of your process around average time in the step, which can be broken down by supplier, material (group), user, etc.
  • Query Builder – build queries to answer questions not answered in the dashboard
  • Modeler – adjust the process model as required
  • Impact – The statistics and KPIs are converted into easily understandable process descriptions based on intelligent models to aid the analyst in interpreting key figures and estimating the initial impact.
  • Comparison – The results of a process comparison across suppliers and product (groups) to highlight why one supplier or product is better (or worse) than another.

As with any good platform, you can drill into any data set on any dimension, reorder the dimensions, and drill right down to the individual transactions. Also, since they have a number of implementation partners certified on the major ERP systems (SAP, Microsoft, etc.), you can have it implemented quite quickly, and the partners can work with you to get your data properly classified in a very short time frame as well. You can also export all of your data at any time.

When it comes to user administration, an admin user can grant other organizational users access rights done to the record level (and may only see some modules, dashboards, and menu items as well) and define new measures for report building.

The platform is very useable, but to ensure that all of their users can make the most of it, they have an extensive on-line education library in German and English on their Training Site. These courses go beyond platform basics and even include courses on negotiation, supplier consolidation, strategic category assessment, and so on.

If you’re doing a lot of direct spend and looking for a best of breed spend analysis solution, it’s one to include on the short list.

One of these things is not like the other — it’s the right choice!

Three bids for that spend analytics project from the three leading Big X firms come in at 1 Million. One bid for that spend analytics project from a specialized niche consultancy you pulled out of the hat for bid diversity comes in at 250 Thousand. Which one is right? Those of you who only partially paid attention to the education Sesame Street was trying to impart upon you when you were growing up will simply remember the “one of these things is not like the other” song and think that any of the bids from the Big X firm is right and the niche consultancy is wrong because it’s different, and therefore must be thrown out because it’s too low when, in fact, it’s the three bids from the Big X firms that are wrong and the bid from the niche consultancy that was right.

Those of us who paid attention knew that Sesame Street was trying to show us how to detect underlying similarities so we could properly cluster objects for further analysis. What we should have learned is that the Big X bids were all the same, built on the same assumption, and can be compared equally. And that the outlier bid needed further investigation — a further investigation that can only be undertaken against an appropriately sized set of sample set of bids from other specialized niche consultancies to compare against. And without that sample set of bids, you can’t properly evaluate the lower bid, which, the doctor can tell you, is likely closer to correct than the wildly overpriced Big X bids.

As per our recent post on don’t hire a F6ckw@d from a Big X if you want to get analytics and AI right, most of these guys don’t have the breadth of expertise they claim to have. In the group that sells you, there will be a leader who is a true expert (and worth his or her weight in platinum), a few handpicked lieutenants who are above average and run the projects, and a rafter of turkeys straight out of private college with more training in how to dress, talk, and follow orders than training in actual analytics … and no guarantee they even have any real university level mathematics (and thus a knowledge of what analytics is and isn’t and can and can’t do).

While there was a time big analytics projects were million dollar projects, that was twenty years ago when Spend Analysis 1.0 was still hitting the market; when there were limited tools for data integration, mapping, cleansing, and enrichment; and when there weren’t a lot of statistics on average savings opportunities across internal and external spend categories. Now we have mature Spend Analysis 3.0 technologies (some taking steps towards spend analysis 4.0 technologies); advanced technologies for automatic data integration, mapping, cleansing, and even enrichment; deep databases on projects and results by vertical and industry size; extensive libraries for out-of-the-box analytics across categories and potential opportunities; and a whole toolkit for spend analysis that didn’t exist two decades ago. This new toolkit, built by best of breed vendors used, and sometimes [co-]owned by these best of breed niche consultancies (that don’t try to do everything, and definitely don’t pretend they can), allows modern spend analysis projects to be done ten times as efficiently and effectively, in the hands of a master — a master that isn’t on your project if you hire a Big X. A niche consultancy will have all these tools, and only have masters on the project who do these projects day in and day out. Compared to the Big X, which will have a team of juniors using the manual playbook from the early 2000s, and one lieutenant to guide them. That’s why their project bids are five times as much — and why you should be inviting multiple niche best-of-breed consultancies to bid on your project and be focussing in on their six figure bids for the one that provides the best value, not the seven figure Big X bids.

(This is also the case for implementations. The Big X always have a rafter on the bench to assign to any project you give them, but there’s no guarantee any of them have ever implemented the system you chose before, or if they did, no guarantee they’ve ever connected it to the systems you need to connect to. You need specialists if you want that big new system implmented as cost effectively as possible. Even if you’re paying those specialists 500 or more an hour because getting a system up in 2 months at 40K is considerably better than a small team of turkeys taking 4 months at 250 an hour and a total cost of 100K.)

Remember, where Big X are concerned, All of us is as dumb as One of us! Don’t fall for the Big X Collectivism MindF6ck! the doctor does NOT want to do say it again, but since a month still is not going by where he’s hearing about niche consultancies being thrown out for “being too cheap” (which means the enterprise throwing them out is too uninformed and not recognizing that the Big X bids are the outliers because they aren’t inviting enough expert consultancies to the table), apparently he has to keep writing (and screaming) this truth. (the doctor isn’t saying that you can’t get a million dollars of value from some of these consultancies, just that you won’t by giving them these types of projects which they are not suited for and don’t have the expertise in. Remember, most of these firms got big in management, or accounting and tax, or marketing and sales consulting, not technology consulting. The only reason these big consultancies are offering these services is because of the amount of money flowing into technology, money which they want, but while the best of the best of the best in more traditional accounting, management, and marketing fields flocked to them, the best of the best in technology flocked to startups and c00l big tech firms. So they just don’t have the talent in tech.)


 

Did you ever try eating a mitten? the doctor bets they did! (He feels you’re not all there if you think glorified reporting projects still cost One Million Dollars and might actually try to eat your mittens!)

Sievo is Still Constantly Sieving Your Data For New Opportunities

While the doctor has never covered Sievo on Sourcing Innovation, he did cover them in depth at Spend Matters, and, if you have appropriate Content Hub access, you can find the last 3-Part in-depth Vendor analysis here in Part I, Part II, and Part III.

A Quick Recap

Sievo was founded twenty years ago in 2003 in Helsinki, Finland and is probably the oldest remaining standalone spend analytics player, as a significant portion of all of the M&A that occurred in the 2010s was on leading spend analytics players. (The next oldest standalone vendor is likely Rosslyn Analytics founded in 2007.) While it started as a standard service-based spend analytics offering, by the time of the doctor‘s in-depth update on Spend Matters in 2020, Sievo had moved well beyond basic spend analysis and centered much of its value proposition on driving savings and improvement (as Sievo is not just limited to spend data and can also process compliance, risk, and ESG/GHG/Carbon data, for example) program identification (through automated insights), measurement and management across the full spectrum of spend, where they do the data management services, the foundations for savings and improvement program management, and even the meta-data management to power advanced organizational initiatives.

With regards to data management, it handles all of the system integrations; refreshes on your schedule; does all the classification (working with your team to build the classification you want); cleanses, normalizes, and enriches your data; and rolls out (customized) market and category intelligence to your organization.

With regards to classification, they use a mix of (black-box) AI and human (re-)classification and review, guaranteeing 94% accuracy, and usually achieving 98% to 99% accuracy. They use multiple techniques including customized in-house LLMs, DeBERTa Large v3, BERT, evolutionary algorithms, and NER models, with and without feedback. (And while the doctor knows most of you won’t understand any part of that sentence, it means their AI/ML team actually knows what they are doing and they are not just a bunch of college drop outs randomly feeding open models with random data and assuming anything that appears to give them 90% accuracy is good. Which happens quite a bit in “data science” consulting shops. See the doctor‘s post about how to do Analytics and AI Right.) Mappings, especially for low confidence, high spend, and new categories/customers are also manually reviewed on a regular basis, corrected, and the models retrained. In addition, a user can request a reclassification at any time, and once a customer admin approves, the data is reclassified (in an override) and the model retrained.

With regards to integration/refresh, they’ve integrated with over 100 ERP/AP/data systems and dozens upon dozens of market, risk, and ESG feeds that they can use to enhance your data and typically refreshes massive data sets daily (but can do faster or slower depending on the desires of the organization).

With regards to traditional analytics, the platform has the standard set of modern drill down dashboards, support for user-defined calculated/derived dimensions, and incredibly powerful filters that support functions that can be used on any dimension (base or derived) which collectively allow a user to break out, and drill into, any subset of data of interest.

With regards to out-of-the-box reporting and analytics, Sievo has over two-dozen out-of-the-box dashboards that provide deep insight into spend by category, PO, invoice, supplier, emission, cycle time, tail, geography, etc. There’s also pre-built insight dashboards for savings opportunities on price (variance), payment terms, currency exchange, and efficiency/cycle-time improvement.

With regards to savings tracking and management, Sievo includes a realized savings dashboard that can automatically compute the spend difference period-over-period, currency fluctuations, and changes due to changes in market indices (where a contract price is tied to a market index). It provides insight into spend that results from non-performance and allows Procurement to pinpoint the exact reason for deviation from a plan. Note that savings tracking is very extensive in the platform and users can define budgets, demands, expected trends, milestones, approvals, etc. and track each initiative as a separate project in the platform.

It also supports forecasting/budgeting, contract metadata management, and price benchmarking based on over $1 Trillion of customer spend (and their customers now represent 1% of global GDP) mapped to a common internal schema and with their deep knowledge of direct material pricing (from indexes, etc.), they can help customers benchmark down to the material level in a BoM.

So What’s New? (Since 2020.)

Since 2020, Sievo has made a number of platform improvements and included a number of new offerings in the spend analysis platform. The most notable of which are the following:

Integrated Actions

In addition to supporting (savings) action plan, some associated actions can now be initiated within the system (with more being added with every release). Email contacts relative to supplier performance / sustainability can be initiated with the system, automated negotiations through Pactum can be kicked off within the system, and Ecovadis requests (detailed below) can be kicked off within the system. A user can also access the Sievo Academy, Sievo Support, reclassification requests, associated contracts (not just metadata), and roadmap suggestions/product feedback directly through the platform.

Integrated Sustainability and Third Party Dashboards

When we last covered Sievo, it had an Ecovadis integration and could pull in select data for enrichment dimensions or measures, but, if you have the subscription, they now maintain all Ecovadis data natively in their platform and have a new Sustainability dashboard where a user can dive into the different Ecovadis scores (Global, Labor, Ethics, Environment, and Sustainable Procurement) by category, material, supplier, etc. and see the global score, assessed suppliers (as not all suppliers will be assessed by Ecovadis), assessed suppliers, score by top 100, score by category, score by organization, global score heatmap, etc. A user can drill down to an individual supplier (by category and material) and see all of the Ecovadis details natively in Sievo. If a supplier has not been evaluated and the organization would like it evaluated, they can even initiate a request directly in the Sievo platform. (Alternatively, if you have other sustainability/risk data feed licences, including [but not limited to] SupplierIO, RiskMethods, D&B, etc.] all of that data can now be brought in and maintained natively as well.)

CO2 Analytics

There’s also a new set of CO2 Scope 3 Emission management dashboards that can, at a category, material, and/or supplier level, summarize total CO2(-equivalent) emissions, related spend, intensity, carbon price amount, with breakdowns by category, GHG protocol category, organization, region, supplier country, share of emissions per higher level category/supplier, etc. A user can dive in and see the Unit of Measure (UoM) conversion rates, inflation rates, mapping quality, and types of estimates used at the meta-leval and the mapping source, emissions factor, validity dates, and associated backup (attachments, etc.) at the mapping level.

One unique feature of the Sievo platform is that the CO2 can be used as a second currency alongside spend and allow users to see the spend/carbon trade off of different decisions. (For example, switching to supplier S for Product P can save D Dollars but cost X emissions.)

Curated Data, including Science-Based Target Initiative (SBTI), Support

Sievo has not only started tracking supplier ESG and net-zero status data, but also correlates an organization’s spend against SBTI records and summarizes percentage of spend with SBTI suppliers (who have set a target), percentage of spend with SBTI suppliers with near-term targets, total spend with net-zero targets, and percentage of de-listed suppliers as well as spend by top 100 suppliers and net-zero status, spend by category and net-zero status, and spend by organization and net-zero status. It’s a great way to see SBTI spend coverage at a glance.

At a finer grained level, for each supplier, it summarizes the supplier’s general status, number of commitments, number of targets, net zero status & target year if committed, near term target status (targets set or not), long term target status, company temperature alignment, spend, sector, org type, region, location(s), etc. and all data can be accessed by a drill in to the supplier level.

AI-Extended Supplier Profiles with Feedback and Human Validation Available

In addition to spend benchmarks powered by community intelligence on well over One Trillion Dollars of spend mapped to a common, centralized, taxonomy, they have also been building a common supplier database which currently consists of over 6 M suppliers (with the goal of 10M by EoY 2024) with enhanced supplier profile data around geography, categories and materials, CO2/GHG and sustainability, SBTI, and other relevant data from a spend analysis perspective. In addition, they have been augmenting it using customized AI/NLP/LLM that uses web-based data (from the supplier’s web site, Wikipedia, supplier intelligence platforms, etc.) to provide a more complete picture in order to provide clients with deep embedded supplier insights, which will eventually support deep discovery in a future release.

Sievo manually validates a subset of high-spend suppliers for every client and every user can provide feedback on supplier data they feel is incorrect and request validation on a supplier if desired. Suppliers whose profiles have been manually validated are marked as verified (and data is not changed without human verification if such data comes from a data feed where the provider, such as Ecovadis, has manually verified the data).

Extended Supplier 360 Dashboard and CraftBoards

The dashboard summarizes total spend (for the given time range), it’s spend rank, PO coverage, avg invoice, spend share by category, by organization, by ERP supplier (instance/subsidiary), by payment terms, invoice-to-due days, due-to-pay days, [local] price [consolidation] opportunities, price opportunities vs. best price from a different supplier, top insights (from the new insights dashboard, discussed below), and, if desired, key metrics from the new SBTI dashboard (summarized above).

Since our last update, Sievo has also added CraftBoards, which allows users to build their own dashboards on any data subsets they like. Sievo can process and display any type of data, and the user can build cubes and dashboards on any data source they like.

Adaptive Insight Recommendation Engine

Sievo has doubled down on identifying all types of savings opportunities across price (variance), payment terms, process optimization, risk and compliance, and the (supplier) tail. Sievo has also doubled down on a new insights overview dashboard that currently summarizes system-identified opportunities across almost twenty (20) types of analysis across those five categories (with more coming in future releases). With regards to price optimization, it will summarize price (variance) opportunity and high price impact. With regards to payment terms, the outlier opportunity, the early payment impact, and overall consolidation opportunity as well as (interest/penalty) losses due to critically late spend. With regards to process, it summarizes order consolidation spend, hidden tail spend, non-PO outlier spend, and after-the-fact spend that is likely ripe with opportunity. With regards to risk and compliance, FX exposed spend, outlier sustainability spend, single sourced material spend, and hidden locally sourced high risk spend. Finally, with respect to the tail, they break it down by category, material, new supplier, new supplier non-PO, and supplier count increase which is super rife with opportunity. It’s super easy to drill into each dashboard metric, see the breakdown by supplier, category, or location, and quickly identify the top opportunities for savings. And, of course, dive into the suppliers with the highest opportunities as the Sievo platform was designed to automatically bubble up insights from the data in a manner that is relevant and actionable.

For each area, there’s also an insight dashboard that summarizes the total number of insights identified, and the breakdown by open, assigned, completed, and removed. One major difference in their platform is that a human user can identify when an insight is immaterial or non-actionable and the system will not only hide it, but learn from the feedback, and not show the insight again. For example, a payment term rationalization across a supplier’s location will not be possible if the average payment term is longer than legally allowed in France or the UK (as a result of recent laws designed to ensure [small] suppliers are paid timely). Note that insights can be assigned for action to a user and, once assigned, generates an action plan in the Sievo system.

The individual opportunity dashboards are incredibly detailed, and you can see prior coverage or a Sievo demo for details. For example, the payment terms consolidation dashboard, which allows you to dive into a category (as most contracts are category based), allows you to pick a supplier, see the supplier spend and summary, spend by payment terms, spend development opportunities by payment terms, totals by ERP supplier instance, best payment terms, overall working capital opportunity by ERP supplier, etc. etc. etc.

New Widget-Based Insights-Oriented Home Page

The main dashboard can summarize the top insights (as per the insights discussed above) across each area analyzed by the platform and provide quick links into each insight area and bubble up insight while also giving users quick access to:

  • their current/open initiatives
  • their bookmarked dashboards
  • their current/open reclassification requests
  • recent bulletins
  • quick links to relevant courses in the Sievo Academy
  • top CO2 emitters
  • other widgets the user wants on their home page

Extended Do-it-Yourself Self Service Report Creation

Not really new, but as they have added dimensions and measures, they have all be included in the self-service dashboard (and organized by measure type such as spend, payment term, supplier, invoice, PO, material, category, etc. for quick location). Similarly, as new chart types have become available, they have been included as well. (And you are not restricted to traditional bar, line, pie, and table charts — full featured pivot charts, scatter plots, and mekko charts are also available in self-service).

Near-Term Roadmap

Community Data

Sievo users will be able to benchmark their data against anonymized data from other Sievo companies’. For example, a company will be able to gain insight into its payment term competitiveness with respect to market average. A variety of pilots are ongoing to determine the most useful data, but these insights will extend the value Sievo can bring to its users.

Index-based Price Forecasting

In the materials forecasting module, more functionalities are being integrated to ensure a deep understanding of future budgeting and spending and simplify usability. Soon (in Q2 2024), forecasting based on indexes will become available, making the process even smoother and accurate for Sievo users.

Supplier Portal

ESG emissions in the supply chain are largely related to those from suppliers, and thus collaboration with suppliers is a must for emission reduction. A new supplier questionnaire and supplier portal enables users to ask suppliers for information related to their emissions, sustainability initiatives, and more.

In conclusion, if you are looking for a Best-of-Breed spend analysis solution, Sievo continues to be among the best in the space and should be on your RFP short-list.

Mercanis: Men with a Mission to bring Modern Volkswagen Efficiency with BMW Style to Source-to-Contract! Part 2

As discussed in Part I, Mercanis is a new Source-to-Contract mini-suite provider based in Berlin, Germany that is bringing a powerful, affordable, and easy to use solution to the mid-market that not only has core capabilities in sourcing, supplier management, analytics, and contract management, but also has core capabilities around risk assessment AND intake, which is not something we have traditionally seen in mid-market Source-to-Contract, and even enterprise Source-to-Contract and Source-to-Pay suites.

Logging into Mercanis takes the end user, who could be a buyer, an AP clerk, or an average employee who needs to go out to market for a product or service to do their job, to their customized dashboard (according to their role) where they can see an overview of their events/requests, contracts, suppliers (including individual supplier overviews) they manage or have access to, organizational spend they oversee, and other relevant information depending on the selected widgets.

Yesterday we overviewed Sourcing, Supplier Management, and Risk. Today we are going to overview Contracts, Spend Analysis, and Platform Administration.

CONTRACTS

Contract Management in Mercanis is straightforward contract document management with a sprinkle of contract creation capability. It stores all of the contracts and associated metadata, including the supplier, active term, value, type, and status (which is draft, pending, active, inactive, and archived by default). It’s easy to search, filter, retrieve, and view a contract at any time. Viewing takes the buyer to the summary screen, from which the user can drill into more detailed screens on payment, linked documents and contracts, stakeholders, relevant clauses, and other (custom) information screens as appropriate to the contract type. The system also supports the definition of tags and contracts can be tagged to categories or conditions of interest, such as sensitive of personal data, auto-renewing, special initiatives, and so on.

Uploading a contract in the Mercanis platform is easy. You drag and drop the document and it auto-extracts most of the key meta data elements that are described in the platform using OCR and advanced NLP. It’s not perfect (no system is, no matter how much fancy AI the systems claim), but it’s easy for the user to override any extract data that is not quite what they want, or not found, and index into the relevant part of the contract.

Finally, contract queries can be search and filter on metadata or Natural Language chat, which will learn from repeated use and adapt to the user’s natural language queries over time.

SPEND ANALYSIS

Basic Spend Analysis is integrated into the core and allows the user to select filterable widgets and dashboards that show spend by category, cost center, supplier, and other major identifier in the system (contract, sourcing event, etc.). It is instantiated with AP data on system implementation, which the system auto-maps to your pre-defined category taxonomy using (auto-generated) mapping rules consisting of suppliers and keywords/phrases/abbreviations/tags in the line item descriptions (identified by AI and curated by humans) and provides sourcing professionals insights from the date of go-live.

As with every other modern platform, it’s easy to drill into the categories (and sub-categories), suppliers, cost centers/business units, and contracts and see the associated transactions. Filters will also allow limiting to date ranges or other record values of interest. And it’s very easy to pop-up a supplier profile from a spend analytics widget or screen or a contract as the analytics, while basic compared to best-of-breed spend analysis tools, are fully integrated.

ADMIN

When it comes to platform administration, it is highly configurable by the organizational administrators. This administration includes the ability to configure approval paths, role groups, individual users, and workspaces (which roles can be limited to) as well as the company information your suppliers see about you. (It’s such a simple concept, but even many SRM platforms don’t make it easy for a supplier to access the customer information about you that they need as a supplier.) There can be different approval paths for every workflow including, but not limited to, supplier onboarding, sourcing (intake) request approval, sourcing awards, and contract approvals, including conditional/branching approvals based on arbitrary fields (such as amounts over or under 50K, product/service category, etc.). These flows can be built using a visual approval workflow builder that can support all standard Boolean logic and if/then/case conditionals.

Let’s dive into workspace configuration, as this is one of the most unique capabilities. The platform supports the definition of as many workspaces as you want, where each workspace can have its own dashboard, its own subset of modules, restricted/no admin access, approval workflows, and templates. Most importantly, a role can be associated with a workspace and when a user is associated with role, that is the workspace, and the only workspace, they will see when they log in. If necessary, the platform can support hyper-personalization natively.

In addition to the platform administration capabilities outlined above, the organization can define business units, manage its category tree (for sourcing and the built in spend analysis), define it’s default meta data requirements by contract type, visually manage all platform workflows (across all modules), manage its currency exchange rates, define its (supplier/RFQ) ratings, and define and manage the data collection templates for every module in the system including supplier data collection forms, pricing sheets, RFP questionnaires, and contract/document templates.

When it comes to workflows, just like the platform can support as many workspaces as you like, it can support as many workflows as you like for each process supported by the module. For example, you can not only have a different sourcing workflow for each category, but you can have multiple workflows based on expected spend. You can have different supplier onboarding workflows depending on category, geography, or a combination thereof (for example), different contract / document creation and management workflows (in addition to approval), and so on. And each can be linked to the associated module in the associated workspace. Highly configurable.

Workflow definition is enabled by the rule builder which is very flexible, and just like approval workflows, is completely visual, supports all Boolean logic, and allows rules to be easily defined in a rule chain that defines the category/ies, role group(s), workspace(s), discriminator (such as budget amount), and action (which can itself kick off another workflow).

The pricing sheets are very flexible and essentially act as mini-spreadsheets embedded in the sourcing tool. Allows for detailed cost break downs and calculations in both sourcing events, and analytic comparisons. The templates can have any number of elements and support all standard HTML components.

IMPLEMENTATION

The system can be implemented and configured for go-live in as little as two weeks, as long as the relevant supplier dataset and spend history can be provided day one and is complete enough that their processes can sufficiently classify the AP data on the first pass to the point that they can complete the processing with manual intervention within the timeframe. Note that the buying organization can choose to load all suppliers, all suppliers used within the last x months or years, or just currently active suppliers that will be used in sourcing events.

Mercanis is a great new entry to the mid-market Source-to-Contract space, especially considering all of the acquisitions and roll-ups of the last 5 years or so that took a lot of companies out of the mid-market and into the enterprise suite game. If you’re looking for a new S2C solution, and especially if you are based in Europe, Mercanis will make a great addition to your shortlist. It’s come a long way in a short time and the doctor has no reason to believe that they won’t continue to make significant progress, and add significant value, over the next few years while maintaining a price-point the mid-market can afford.

Mercanis: Men with a Mission to bring Modern Volkswagen Efficiency with BMW Style to Source-to-Contract! Part 1

Mercanis a new Source-to-Contract mini-suite provider based in Berlin, Germany that is bringing a powerful, affordable, and easy to use solution to the mid-market that not only has core capabilities in sourcing, supplier management, analytics, and contract management, but also has core capabilities around risk assessment AND intake, which is not something we have traditionally seen in mid-market Source-to-Contract, and even enterprise Source-to-Contract and Source-to-Pay suites.

Logging into Mercanis takes the end user, who could be a buyer, an AP clerk, or an average employee who needs to go out to market for a product or service to do their job, to their customized dashboard (according to their role) where they can see an overview of their events/requests, contracts, suppliers (including individual supplier overviews) they manage or have access to, organizational spend they oversee, and other relevant information depending on the selected widgets.

Today we’re going to discuss Sourcing, Supplier Management, and Risk.

SOURCING

Creating a sourcing event in Mercanis for new or previously sourced articles can be accomplished in just a few minutes as the platform was designed for high efficiency. With integrated intake, the system will either guide an organizational user to a self-serve sourcing event for articles (products/components/fixed services) in acceptable categories under a certain amount or funnel to the appropriate sourcing team, as appropriate.

When an organizational user wants something, they define their event name, a unique departmental project reference, category, budget, RFX due date, relevant organizational tags, affected business unit[s], preferred NDA (from those associated with the category), and then the system will either notify the requester that this needs to be a (strategic) sourcing event and direct it to the sourcing team or take the buyer to their (selected) workspace where they can set it up on their own.

In either situation, the next step is to select suppliers. Suppliers are auto-suggested by the system and it’s one click to select them (and the user can search for other known suppliers or even invite a new supplier for onboarding if they want to). After that, they select an appropriate pricing sheet (from those associated) which is automatically pulled in, and then they select appropriate RFP surveys that they want filled out (which are also auto-suggested based on the article). They can then launch the event immediately, or specify a later date, and at any time they can (come back and) add stakeholders.

For a single article, since everything is auto-suggested, they can literally select the core suppliers, price sheet, and surveys with a few clicks and launch a small event in a minute. Most events on an article or category can be reasonably defined in five to fifteen minutes (vs. the 15 hours for some first, and even second, generation suites).

In the Sourcing projects can be multi-round if necessary. Once the results come back, the buyer can kick off another event based off of that project and link it to the existing one to create a multi-round event.

Also, once response come in, as many stakeholders as desired can score it, the scores can be weighted, and once an award is decided upon, it can be sent to the contract module. Survey responses for each survey can be compared side-by-side for easy comparison against peers. And when the individual responses are scored, the buyer can see the assessment criteria scores graphically in spider graphs, including a calculated score based on total relative pricing. When it comes to price sheets, which can include embedded formulas, the buyer can select the prices of interest for side-by-side comparison as well. And to make the comparisons pop, the buyer can even shift to dark mode. While not always the best for data entry, it does make certain visual comparisons pop.

The entry point to sourcing is the dashboard which will summarize the requests, events by category, upcoming, and current sourcing events that need to be reviewed, managed, or awarded.

An organizational buyer can also two-click a new sourcing event by going to the article summary screen, locating the article of interest, clicking on it, defining an event name, selecting one of the associated sourcing workflows (defaulted if just one), selecting one of the associated pricing sheets (defaulted if just one), and confirming the event creation.

SUPPLIER MANAGEMENT

The Supplier Management module revolves around the Supplier Repository which organizes all supplier related information in the system with each supplier maintained by the system. It’s easy to search suppliers by name, category, location, associated transaction cost centers, and other information. Upon implementation, Mercanis can import all of your suppliers from your ERP, just a subset you mark as active, or only those suppliers used in the past x years.

On implementation, they will pull in as much information as they have, fill in gaps with any information they have in their system, and augment with a 360-degree profile they auto-generate using their AI tools that scrapes supplier websites and pulls in data from third party sites, Compliance Catalyst, Dun & Bradstreet and/or other third party supplier data providers you have a subscription to. This profile will include a short description, any known (reference) customers, categories the supplier (can) supply in your taxonomy, any known contacts, owners, known business units, primary / head office location, website and Linkedin URLs, and even known similar suppliers in your database. It will also contain direct links to any third party profiles you have access to, and can even pull all of that information into the platform for you.

This is in addition to the basic corporate information (and contacts) maintained by the system (which includes legal identifiers, basic accounting information, and location data), supplier states (which can be buyer organization defined), tiers (as the organization can track tier 2 suppliers or suppliers typically used by your suppliers, third party ratings (from the ERP or a data partner) and data that can be pulled in (which can be visually displayed in spider graphs), specific information collected during onboarding, and appropriate risk data (including cached data from any third party data feeds you have a license too). Note that suppliers can also be evaluated using organizational surveys that can be associated with them, and multiple evaluators can be associated with these surveys.

The SRM system also centralizes and maintains a record of all system activity, including sourcing events, contracts, risk profiles, and associated supplier analytics. It also tracks all associated tasks from across the system in one location, all associated (onboarding/sourcing/contract) requests, and any notes the buying organization wants to add.

New supplier creation is easy. It can be as easy as defining a name and email to kick-off the onboarding process, which will send a request to the buyer to provide the requested information. (Note that if you provide an appropriate legal identifier or URL and the supplier is in the Mercanis database, base information will automatically be populated to simplify the onboarding process for the supplier.)

Search can be customized to work on any given supplier identifier.

RISK

The risk module, primarily used in supplier pre-qualification, tracks country and industry risk across the globe and can instantly associate the relevant country and industry risks with an existing, or new, supplier based on its address and NAICS code. The platform uses over 40 different data sources to analyze country and industry risk in accordance with the German Supply Chain Act and computes a score for every country-industry risk correlation.

In addition, it can integrate with third party data from providers like IntegrityNext and Ecovadis and, for any supplier, pull in all the relevant data if the customer has the data feed licenses and automatically compute advanced risk measures using their data (from public sources) and third party data.

Come back tomorrow for Contracts, Spend Analysis, and Administration.