Monthly Archives: November 2024

We Want to Be a Smart Company — Is That It? Part I

We’ve read the dumb company: how to avoid the fork in the road (part 1 and part 2) and dead company walking: avoiding the graveyard (part 1, part 2, part 3, part 4, part 5, part 6, part 7 and part 8) articles, and the two installments of “we want to be a smart company” (part 1 and part 2), and we truly want to be a smart company, and we are taking the mistakes, and advice, to heart. Is there anything else we can do?

There’s always more you can do! However, there’s not much left to talk about that’s true across the board for all software companies. That being said, we can give you ten final pieces of advice that just may help if money is tight, leads are few, and sales are hard. Today, we’ll give you the first five.

01. Don’t Put Off Improvements / Hard Decisions You Know You Need to Do / Make

Fixing it later always takes longer than you think, and the timeframe multiplies the longer you wait! If you need to rip and replace part of the platform core for scalability, start as soon as you realize that it needs to be done. If your target customers aren’t educated enough to realize why they need your product, start investing in a series of educational content pieces of different forms to get them there. If you need to cut the marketing and sales deadweight, do so ASAP. The longer you wait, the more it hurts you and them. Doing it early allows you to give them a fair notice period and time to help them find a more suitable role.

02. Chop the Dead Wood — especially in Management & the C-Suite

Refocus the dollars on the developers, content creators, and solution-focussed sales people who are actually generating value. the doctor can’t say this enough. You wouldn’t believe how many startups in tech have been dragged into oblivion by an overweighted inappropriate management team (because the investors thought big names would bring success) — but if they aren’t the right people for the job, or the job isn’t even needed to begin with, nothing could be further from the truth … and instead of being the buoyant striders intended to get you across the lake, they are the cement shoes that sink you to the bottom.

03. Tell the Truth, No Matter What

Especially around what your product does today, and especially especially with respect to anything asked by a customer. Any individual with a half a brain knows that no product does everything, and any individual with a brain can be educated as to why no product should and, more importantly, why they don’t want some of the features the Free RFP vendors are promoting (because it’s not feature, it’s function, and, more specifically, the function they need to do).

The reality is that a good customer will value the truth, especially when they hear so little of it these days among the lies, damn lies, statistics, Gen-AI, marketing buzzwords and hogwash. Moreover, they know they probably don’t need everything they ask for and definitely not day one (as it takes time to learn modules and suites and use them to full effect). They also know that most of the “wish list” gathered from across the organization is just stakeholders trying to be useful and they really only want the functionality to do their daily jobs, and, more importantly, the stakeholders will be happy if that core functionality is done well.

So if you’re missing a few things, that’s okay. The customers know there will always be pain (if work was always fun, people would want to work for as little as they could afford to), so as long as you can relieve the majority of, and the most common, pain, those customers will be quite happy to suffer a little aggravation here and there instead of the cluster(f6ck) migraine they currently have on a daily basis.

04. Sales Channel Reconsideration

Look at how you are selling now and think about if that is how, or the only way, you should be selling.

If you are not doing partner/channel sales, maybe you need to do partner/channel sales. If there is a niche consultancy advising clients on a daily basis with problems that your solution solves, maybe you should be training those consultants on how your solution can be used to solve the problems, training those consultants on how to install the solution, and then putting a partnership agreement in place for those consultants to sell the solution for you to their clients for which it is appropriate.

If you are relying mostly on partner/channel sales, and they aren’t coming in fast and furious like you hoped, maybe you need to step up direct sales. In the right circumstances the right partners will do wonders for sales, but if they are consultancies, it will be highly dependent on what customers come to them, since most niche consultancies still have to take what they can get (while the Big X take the lion’s share of projects, even those which they probably shouldn’t because they are already so busy trying to support so many clients with digital transformation projects, because any consultant who turns away any work at a Big X risks getting fired). So even if your consulting/services partner is your greatest champion, you can’t always rely on them to be a consistent source of sales.

05. Rethink Partnerships

Regardless if it is part of your strategy or not and what partners you do, or don’t, have today. It’s rare for a company to get it right out of the gate, or for the strategy that is right out of the gate to be the best one down the road as markets change, directions change, plans change, etc. If things are going well, you follow the if it ain’t broke, don’t fix it. If things aren’t going well, you evaluate and rethink it. Your strategy/partners could still be the right strategy/partners, and it just needs more time for the strategy/relationships to take off, or it might be that you need a new strategy/relationship.

No consultancies or complementary offerings selling your solution? Why? We’ve mentioned time and time again that no solution is everything to everyone, and there’s always a complementary solution or service that can add value, even if it takes a bit of work to identify it. So if you don’t have a services / implementation partner trained and certified to sell for you, why not? And if you don’t have relationships with one or more complementary solutions with companies with a complementary culture and value, why not? Even if it is only the odd referral, it could help … and if you’re going up against a suite, and your solution is not, it could definitely help. (After all, most customers who need a “suite” really only need a few key modules, at least for the first few years.)

Alternatively, if you only have partners who filled your ears with sweet nothings until you agreed to be a partner and then gave you sweet nothing once the deal was inked, they are NOT partners, especially if right after the deal was inked they decided to partner with another solution provider with a bigger offering and price tag and sell that instead. Those partners should be dropped faster than a radioactive potato and replaced with a new one.

Stay tuned for Part 2!

The Complete AI in Procurement, Sourcing, and Supplier Management: No Gen-AI Needed Series Indexed

The Complete AI in X (No Gen-AI) Series, 2018/2019 and 2024!

CLASSIC (SM Content Hub)

AI In Procurement

AI in Procurement Today Part I
AI in Procurement Today Part II

AI in Procurement Tomorrow Part I
AI in Procurement Tomorrow Part II
AI in Procurement Tomorrow Part III

AI in Procurement The Day After Tomorrow

AI in Sourcing

AI in Sourcing Today

AI in Sourcing Tomorrow Part I
AI in Sourcing Tomorrow Part II

AI in Sourcing The Day After Tomorrow

AI in Supplier Discovery

AI in Supplier Discovery Today

AI in Supplier Discovery Tomorrow

AI in Supplier Discovery The Day After Tomorrow

AI in Supplier Management

AI in Supplier Management Today Part I
AI in Supplier Management Today Part II

AI in Supplier Management Tomorrow Part I
AI in Supplier Management Tomorrow Part II

AI in Supplier Management The Day After Tomorrow

AI in Optimization

AI In Sourcing Optimization Today

AI In Sourcing Optimization Tomorrow

AI In Sourcing Optimization The Day After Tomorrow Part I
AI In Sourcing Optimization The Day After Tomorrow Part II

CURRENT (Your SI!)

AI In Procurement

Advanced Procurement Yesterday: No Gen-AI Needed

Advanced Procurement Today: No Gen-AI Needed

Advanced Procurement Tomorrow: No Gen-AI Needed

AI in Sourcing

Advanced Sourcing Yesterday: No Gen-AI Needed

Advanced Sourcing Today: No Gen-AI Needed

Advanced Sourcing Tomorrow: No Gen-AI Needed

AI in Supplier Discovery

Advanced Supplier Discovery Yesterday: No Gen-AI Needed

Advanced Supplier Discovery Today: No Gen-AI Needed

Advanced Supplier Discovery Tomorrow: No Gen-AI Needed

AI in Supplier Management

Advanced Supplier Management Yesterday: No Gen-AI Needed

Advanced Supplier Management Today: No Gen-AI Needed

Advanced Supplier Management Tomorrow: No Gen-AI Needed

You Should Never Build Your Own ProcureTech Solution! Ever!

Integrate your own custom suite to suit your processes, maybe, but never build from scratch. (And we should not have to be talking about this again after just publishing on the subject two weeks ago, but too many conversations are indicating that we still need to shout this loud and clear!)

For some reason, this comes up every decade, usually after a hype cycle has peaked, marketers have switched from focussing on solutions to sound bites from a suite of providers who have released products that don’t meet customer needs, the implementation failure rate has edged back up to the 80%+ range, and customers have gotten absolutely positively fed up with the whole situation.

Customers, fed up with the valueless hype, marketing sound bites, high failure rate, and utter lack of solutions from the vendors targeting them on a daily basis, start to think that the right solution is to build their own.

Sourcing Innovation tackled this subject in depth back in 2015 when it wrote a 4-part series on why you should NOT build your own e-Sourcing solution, followed by an explanation of why you should not build your own Contract Management and e-Procurement platform. (links here)

That’s why we are both repeating and elaborating on last Friday’s Rant on why A Company Should Never Build It’s Own Enterprise Software Systems.

Not only do we have the situation where:

  • the company is not an expert in building software products
  • the company is not an expert in best practices across all of its processes
  • by the time a custom solution is developed, it’s out of date
  • it’s not about the product, it’s about the process you should be working toward and, most importantly,
  • it’s about the data that drives the process!

But we have the situation where, as highlighted in THE REVELATOR‘s article:

1. Developing your own is NOT being an early adopter! (Which is what many companies considering build-your-own think they are.)

Early adopter means someone who adopts leading edge technology from a third party, not someone trying to fast track their digitization effort with custom built tech. This is just high risk with little chance of reward for all the reasons mentioned in all of our prior articles.

2. They think Gen-AI will fix their data problem and allow them to develop their own!

If you’re read anything on Gen-AI on this blog you know that’s the last thing it will do. For Gen-AI to have any chance of working at all, it needs a huge amount of good, clean, data. Otherwise, it’s garbage in, hazardous waste out. No technology has ever needed such large amounts of near-perfect data to have even an abysmal chance of working, and the fact that the marketing madness has convince many CPOs that Gen-AI can fix a data problem is downright terrifying!

3. They obviously think that the initial quote will be close to the final cost.

No where are cost overruns more extreme than in custom development by a non-software organization that contracts a Big X with poor specifications that look easy, and that, due to lack of manpower, sends The C-Team (if you are lucky) because it’s just another instance of system X (when it’s not).

To be honest, in this situation, if the costs ends up being only 3X to get something usable (but still not what you wanted), given the high technology failure rates, that would be amazing.

We know it’s hard to find appropriate solutions given all the noise out there, and the overabundance of vendors that all look, sound, and go all in on useless Gen-AI the same, as it just takes one glance at the Mega Map to figure that out, but that doesn’t mean there aren’t vendors out there appropriate for you. Vendors that put solutions, not tech first, that built affordable tech that works (and didn’t take too much money from investors who then insisted on quadrupling the price), and that will work in an ecosystem with out vendors to solve your problems.

You just have to look hard. Real hard. Probably harder than you’ve ever had to look before. (Expect to eliminate 6 out of every vendors you look at for short list consideration and probably go through 20 to find 3.) But trust us, when you find the right vendor, it will be worth it. The solution will work, will configure to your liking, will be extremely usable for the problems your team faces every day, and will be one where the provider will grow with you for the decade to come.

Good things come to those who wait to find the right vendor. (Even if they have to crawl through multiple pig sties to do so.)

Advanced Supplier Management TOMORROW — No Gen-AI Needed!

Back in late 2018 and early 2019, before the GENizah Artificial Idiocy craze began, the doctor did a sequence of AI Series (totalling 22 articles) on Spend Matters on AI in X Today, Tomorrow, and The Day After Tomorrow for Procurement, Sourcing, Sourcing Optimization, Supplier Discovery, and Supplier Management. All of which was implemented, about to be implemented, capable of being implemented, and most definitely not doable with, Gen-AI.

To make it abundantly clear that you don’t need Gen-AI for any advanced back-office (fin)tech, and that, in fact, you should never even consider it for advanced tech in these categories (because it cannot reason, cannot guarantee consistency, and confidence on the quality of its outputs can’t even measured), we’re going to talk about all the advanced features enabled by Assisted and Augmented Intelligence that are (or soon will be) in development (now) and you will see in leading best of breed platforms over the next few years.

Unlike prior series, we’re identifying the sound, ML/AI technologies that are, or can, be used to implement the advanced capabilities that are currently emerging, and will soon be found, in Source to Pay technologies that are truly AI-enhanced. (Which, FYI, may not match one-to-one with what the doctor chronicled five years ago because, like time, tech marches on.)

Today we continue with AI-Enhanced Supplier Management that is in development “today” (and expected to be in development by now when the first series was penned five years ago) and will soon be a staple in best of breed platforms. (This article sort of corresponds with AI in Supplier Management The Day After Tomorrow that was published in May, 2019 on Spend Matters.)

TOMORROW

Supplier Future State Predictions

Supplier management platforms of today can integrate market intelligence with community intelligence, internal data, and external data sources and give you a great insight into a supplier’s current state from a holistic perspective.

Along each dimension, future states can be predicted based on trends. But single trends don’t tell the whole story. Now that we have decades of data on a huge number of companies available on the internet across financial, sustainability, workforce, production, and other dimensions which can be analyzed overtime and cross-correlated, we can do more, and know more.

Based on this correlated data, machine learning can be used to build functions by industry and company size that can predict future state with high confidence based upon the presence of a sufficient number of sufficiently accurate data points for a company in question. Now that these platforms can monitor enough internal, community, and market data and pull in a plethora of data feeds, they can accurately compute metrics with high confidence along a host of dimension, and this in turn allows them to compute the metrics that are needed to predict future state if the vendor’s platform has enough historical data on enough companies to define trends and define predictor functions using machine learning.

Not only can you enter a relationship based on a current risk profile, but on a likely future risk profile based on what the company could look like at the end of the desired contract term. If you want a five year relationship, maybe taking advantage of that great deal due to a temporary blip in supplier or market performance may not be a good idea if suppliers historically in this situation typically went into a downward spiral after accepting a big contract they ultimately weren’t prepared to deliver on.

Category Based Supplier Rebalancing

We could actually do this today, as a few vendors are now offering this capability, but it’s not yet part of supplier management platforms and the newly emergent offerings are often limited to a few categories today. But tomorrow’s platforms will continually analyze your categories holistically (along the most relevant dimensions, which could include cost, supply assurance, environmental friendliness, etc.) to determine if the supply mix you are currently using is the best one, let you know if there could be a better one, and suggest changes to orders (as long as it doesn’t jeopardize contracts where that jeopardy could come with a financial or legal penalty).

It’s just a matter of re-running an optimization model on, say, a monthly basis with updated data on price, supply assurance, and environmental friendliness (using the appropriate data for each, such as market quotes, current supplier risk, carbon per unit, etc), and comparing the optimal result to the current allocation plan. If it’s within tolerance, stay on track; if it’s slightly out of tolerance, notify a human to conduct and review a thorough analysis to see if something might need to change; if it’s way off of tolerance, recommend a change with the data that supports the change.

Supply Base Rebalancing

Once you have a platform that is continually reanalyzing categories and supplier-based assignment, you can start looking across the supply base and identify suppliers which are hardly used (and an overall drain on your company when you consider the costs of maintaining a relationship and even maintaining the supplier profile) and supplier that are potentially overused (and pose a risk to your business simply based on the level of supply [as even the biggest company can stumble, fall, and crash to the ground on a single unexpected event, such as the unexpected installation of a spreadsheet driven Master of Business Annihilation as CEO who has no clue what the business does or how to run it effectively and, thus, causes a major stumble, as summarized in Jason Premo’s article).

And, more importantly, identify new suppliers who have been performing great with slowly increasing product / service loads and should be awarded more of the business over older suppliers that are becoming less innovative and more risky to the operation at large. Now, this will just be from a supply perspective, and not a supply chain perspective (as these programs focus on suppliers and not logistics or warehousing or overall global supply issues), but this will be very valuable information for Sourcing and New Product Development who want to always find the best suppliers for a new product or service requirement.

Real-Time Order Rebalancing

Since tomorrow’s platforms will be able to recommend category rebalancing across suppliers, they will also be able to quickly recommend real-time order rebalancing strategies if a primary supplier is predicted to be late in a delivery (or a human indicates an ETA for a shipment has been delayed by 60 days). This is because they will be integrated with current contracts, e-procurement systems, and have a bevy of data on projected availability and real historical performance. Thus, it will be relatively simple to recommend the best alternatives by simply re-running the machine learning and optimization models with the problematic supplier taken out of the picture.

Carbon-Based Rebalancing

Similarly, with the rise of carbon-calculators and third-party public sources on average carbon production per plant, and even unit of a product, it will be relatively easy for these supplier management platforms to build up carbon profiles per supplier, the amount of that carbon the company is responsible for, how those profiles compare to other profiles, and what the primary reasons for the differentiation are.

The company can then focus on suppliers using, or moving to, more environmentally friendly production methods, optimize logistics networks, and proactive rebalancing of awards among supplier plants to make sure the plants producing a product are the ones closest to where the product will be shipped and consumed. It’s simply a carbon focussed model vs. a price focussed one.

SUMMARY

Now, we realize some of these descriptions are dense, but that’s because our primary goal is to demonstrate that one can use the more advanced ML technologies that already exist, harmonized with market and corporate data, to create even smarter Supplier Management applications than most people (and last generation suites) realize, without any need (or use) for Gen-AI. More importantly, the organization will be able to rely on these applications to reduce time, tactical data processing, spend, and risk while increasing overall organizational and supplier performance 100% of the time, as the platform will never take an action or make a recommendation that doesn’t conform to the parameters and restrictions placed upon it. It just requires smart vendors who hire very smart people who use their human intelligence (HI!) to full potential to create brilliant Supplier Management applications that buyers can rely on with confidence no matter what category or organization size, always knowing that the application will know when a human has to be involved, and why!

ketteQ: An Adaptive Supply Chain Planning Solution Founded in the Modern Age

As per yesterday’s post, any supply chain planning solution developed before 2010 isn’t necessarily built on a modern multi-tenant cloud-ready SaaS stack (as such a stack didn’t exist, and it would have had to be partially to fully re-platformed to be modern multi-tenant cloud-ready SaaS). Any solution built after was much more likely to be built on a modern multi-tenant cloud-ready SaaS stack. Not guaranteed, but more likely.

KetteQ‘s Adaptive Supply Chain Planning Solution is one of these solutions that was built in the modern age on a fully modern multi-tenant cloud-native SaaS stack, and one that has some advantages you won’t find in most of the competition. I was able to get an early view of the latest product which was released last week. Founded in 2018, ketteQ was built from the ground up to embody all of the lessons learned from the founders’ 100+ successful supply chain planning solution implementations across industries and systems, and the wisdom gained from building two prior supply chain companies, with the goal of addressing all of the issues they encountered with previous generation solutions. The modern architecture was purpose built to fully utilize the transformational power of optimization and machine learning. It was a tall feat, and while still a work in progress (as they admit they currently only have three mature core modules on par with their peers in depth and breadth [although all inherit the advantages of their modern stack and solver architecture]), but one they have pulled off as they can also address a number of other areas with their other, newer modules, and integration to third party systems (particularly for order management, production scheduling, and transportation management) and address End-to-End (E2E) supply chain planning, with native Integrated Business Planning (IBP) across demand, inventory, and supply — which are their core modules, along with a module for Service Parts Planning and S&OP Planning.

In addition to this solid IBP core, they also have capabilities across cost & price management, asset management, fulfillment & allocation, work order management, and service parts delivery. And all of this can be accessed and controlled through a central control tower.

And most importantly, the entire solution is cloud native, designed to leverage horizontal scalability and connectivity, and built for scale. The solution is enabled by a single data model that can be stored in an easily accessible open SQL database, in a contemporary architecture that supports all solutions. The solution is extendable to support scalability, multiple models, multiple scenarios per model, and a new, highly scalable solver that can perform thousands of heuristic tests and apply a genetic algorithm with machine learning to find a solution by testing all demand ranges against all supply options to find a solution that minimizes cost / maximizes margin against potential demand changes and fill rates.

Of course, the ketteQ platform comes with a whole repertoire of applied Optimization/ML/Genetic/Heuristic models for
demand planning, inventory planning, and supply planning, as well as S&OP. In addition, because of its extensible architecture, instead of manually running single scenarios at a time, it can run up tothousands of scenarios for multiple models simultaneously, and present the results that best meet the goal or the best trade-off between multiple goals.

KetteQ does all of this in a platform that is, compared to older generation solutions:

  • fast(er) to deploy — the engine was built for configuration, their scalable data model and data architecture make it easy to transform and integrate data, and they can customize the UX quickly as well
  • easy to use — every screen is configured precisely to efficiently support the task at hand, and the UX can be deployed standalone or as a Salesforce front end
  • cost-effective — since the platform was built from the ground up to be a true multi-tenant solution using a centralized, extensible, data architecture, each instance can spin off multiple models, which can spin off multiple scenarios, each of which only requires the additional processing requirement for that scenario instance and only the data required by that scenario; and as more computing power is required, it supports automatic horizontal scaling in the cloud.
  • better performing — since it can run more scenarios in more models using modern multi-pass algorithms that combine traditional machine learning with genetic algorithms and multi-pass heuristics that go broad and deep at the same time to find solutions that can withstand perturbations while maximizing the defined goals using whatever weighting the customer desires (cost, delivery time, carbon footprint, etc.)
  • more insightful — the package includes a full suite of analytics built on Python that are easily configured, extended, and integrated with AI engines (including Gen-AI if you so desire), which allows data scientists to add their own favorite forecasting, optimization, analytics, and AI algorithms; in addition, it can easily be configured to run and display best-fit forecasts at any level of hierarchy and automatically pull in and correlate external indicators as well
  • more automated — the platform can be configured to automatically run through thousands of scenarios up and down the demand, supply, and inventory forecasts on demand as data changes, so the platform always has the best recommendation on the most recent data; these scenarios can include multiple sourcing, logistics, and even bills of material; and they can be consolidated meta-scenarios for end-to-end integrated S&OP across demand, supply, and inventory
  • seamless Salesforce integration — takes you from customer demand all the way down to supply chain availability; seamless collaboration workflow with Salesforce forecast, pipeline, and order objects in the Salesforce front end
  • AWS nativity — for full leverage of horizontal scalability and serverless computing, multi-tenant optimization and analytics, and single-tenant customer data. Moreover, the solution is also available on the AWS marketplace.

In this coverage, we are going to primarily focus on demand and supply (planning) as that is the most relevant from a sourcing perspective. Both of these heavily depend on the platform’s forecasting ability. So we’ll start there.

Forecasting

In the ketteQ platform, forecasts, which power demand and supply planning,

  • can be by day, week, month, or other time period of interest
  • can be global, regional, local, at any level of the (geo) hierarchy you want
  • can be category, product line, and individual product
  • can be business unit, customer, channel
  • can be computed using sales data/forecasts, finance data, marketing data/forecasts, baselines, and consensus
  • can use a plethora of models (including, but not limited to Arima[Multivariate], Average, Croston, DES, ExtraTrees, Lasso[variants], etc.), as well as user defined models in Python
  • can be configured to select the best fit algorithm automatically based on historical data, based on just POS data, POS data augmented with economic indicators, external data (where insufficient POS data), etc.

These models, like all models in the platform, can be set up using a very flexible and responsive hierarchy approach, with each model automatically pulling in the model above it and then altering it as necessary (simply by modifying constraints, goals, data [sources], etc.). In the creation of models, restore points can be defined at any level before new data or new scenarios are run so the analyst can backtrack at any time.

Demand Planning

The demand planning module in ketteQ can compute demand plans that take into account:

  • market intelligence input to refine the forecast (which can include thousands of indicators across 196 countries from Trading Economics as well as your own data feeds) (and which can include, or not, correlation factors for correlation analysis)
  • demand sensing across business units, channels, customers, and any other data sources that are available to be integrated into the platform
  • priorities across channels, customers, divisions, and departments
  • multiple “what if” scenarios (simultaneously), as defined by the user
  • consensus demand forecasts across multiple forecasts and accepted what-ifs

The module can then display demand (plans) in units or value across actuals, sales forecasts, finance forecasts, marketing forecasts, baseline(s), and consensus.

In addition to this demand planning capability and all of the standard capabilities you would expect from a demand planning solution, the platform also allows you to:

  • Prioritize demand for planning and fulfillment
  • Track demand plan metrics
  • Consolidate market demand plans
  • Handle NPI & transition planning
  • Define user-specific workflows

Supply Planning

The reciprocal of the demand planning module, the supply planning module in ketteQ leverages what they call the PolymatiQ solver. (See their latest whitepaper at this link.)

Their capabilities for product and material planning includes the ability to:

  • compute plans by the day, week, month, or any other time frame of interest
  • do so globally, regionally, locally, or at any level of the hierarchy you want
  • and do so for all regional, local, or any other subset of suppliers of interest, as well as view by customer-focused dimensions such as channel, business unit and customer
  • use the current demand forecast, modifications, and taking into account current and projected supply availability, safety stock, inventory levels, forecasted consumption rates, expected defect rates, rotatable pools, and current supplier commitments, among other variables
  • run scenarios that optimize for cost and service
  • coordinate raw and pack material requirements for each facility
  • support collaboration with suppliers and manufacturing
  • manage sourcing options and alternates (source/routes) for make, buy, repair and transfers

Moreover, supply plans, like demand plans, can be plotted over time based on any factors or factor pair of interest, such as supply by time frame, sourcing cost vs fill rate, etc.

In addition, the supply planning module for distribution requirements can:

  • develop daily deployment plans
  • develop time-phased fulfillment and allocation plans
  • manage exceptions and risks
  • conduct what-if scenario analysis
  • execute short-term plans
  • track obsolescence and perform aging analysis/tracking

Inventory Planning

We did not see or review the inventory planning module in depth, even though it is one of their three core modules, so all we can tell you is that it has most of the standard functionality one would expect, and given the founder’s heritage in the service parts planning world, you know it can handle complex multi-echelon / multi-item planning. Capabilities include:

  • manage raw, pack and finished goods inventory
  • set and manage dynamic safety stock, EOQ, ROP levels and policies
  • ensure inventory balance and execution and support for ASL (authorized stocking list), time-phased, and trigger planning by segment
  • support parametric optimization for cost and service balancing
  • the ability to minimize supply chain losses through better inventory management
  • the ability to optimize service levels relative to goals

Salesforce: IBP

As we noted, the ketteQ platform supports native Salesforce integration, and you can do full IBP through the custom front-end built in Salesforce CRM, which allows you to seamlessly jump back and forth between your CRM and SCM, following the funnel from customer order to factory supply and back again.

The Salesforce front-end, which is very extensive, supports the typical seven-step IBP process:

  1. Demand Plan
  2. Demand Review
  3. Supply Plan
  4. Pre IBP Review
  5. Executive IBP Review
  6. Operational Plan
  7. Finalization

… and allows it to be done easily in Salesforce design style, with walk-through tab-based processes and sub-tabs to go from summary to detail to related information. Moreover, the UI can be configured to only include relevant widgets, etc.

In addition, users can easily select an IBP Cycle; drill into orders and track order status; define custom alerts; subscribe to plans, updates, and related reports; follow sales processes including the identification and tracking of opportunities; jump into their purchase orders (on the supply side); track assets; manage programs; and access control tower functionality.

As a result of the integration with Salesforce objects, including Pipeline and Orders, the solution helps bridge the gap between sales and supply chain organizations, enabling executive-driven process change. As an advanced supply chain solution on the Salesforce Appexchange, it enables the broad base of Salesforce customers on the manufacturing cloud a slew of unique integration possibilities.
And, of course, if you don’t have Salesforce, you still have all this functionality (and more) in the ketteQ front-end.

Finally, the platform can do much more as it also has modules, as we noted, for service parts planning, service parts delivery, sales and operations planning, cost and price management, fulfillment & allocation, asset management, clinical demand management, and a control tower. It is a fundamentally modern approach to planning that is worth exploring for companies that are challenged to adapt in today’s disruptive supply chain environment. For a deeper dive into these modules and capabilities, check out their website or reach out to them for a demo. This is a recommendation for ANY mid-sized or larger manufacturing (related) organization looking for a truly modern supply chain planning solution.