Executive Summary
The US inflationary economy, a critical talent shortage, sluggish supply chains, and divisive geopolitical impacts pose existential threats to the Distribution Sector.
Distributors wrestle with external and internal stressors, and some opt to ride out the storm. They think “the new normal” will mean a return to the way things were—a regressive, naive, and self-destructive “strategy.”
But other owners, CEOs, and CFOs see and pursue opportunities that will transform their way of doing business. They see major benefits in finding the right balance between demand and supply. They want to seize and master this strategy to minimize exposure while increasing customer satisfaction.
Visionary distributors must launch new initiatives in Pricing and Inventory. These initiatives require resilience, agility, and the ability to pivot and improvise. Moving forward requires new tools and tactics, strategies and means to master Inventory and Pricing Optimization.
This guide seeks to:
- Acknowledge the challenges facing the Distribution Sector.
- Understand the importance of Pricing Optimization.
- Reimagine a workplace culture of respect, collaboration, autonomy, and accountability.
- Target the crucial balance of service-to-inventory and cost-to-price.
- Understand the importance of Inventory Optimization.
- Find best practices in AI/Machine Learning for Inventory and Pricing processes.
Owners, CEOs, CFOs, and other C-suite decision-makers in Distribution companies will find meaningful data, tech-powered strategies, and non-disruptive solutions to master change and growth.
This analysis offers an approach to balancing critical service-to-inventory and cost-to-price in a dynamic new way, enabled by new technologies to:
- Calculate the optimum service levels,
- Understand inventory turnover ratios and their importance,
- Analyze customer purchasing patterns to optimize inventory levels; and
- Leverage the impact of seasonality on stock control.
A business future is always a work in progress. But successful leaders will lay the groundwork for the changes necessary to thrive through chaotic times.
- Nelson
Nelson Valderrama, CEO, Intuilize
The Inside Story
Where B2B leaders find themselves today
PriceWaterhouseCoopers (2023) surveyed over 4,000 CEOs. Results showed 75 percent of them “face daunting near term challenges” and “declining growth during the year ahead.” The graph in the figure below shows the CEOs’ low revenue expectations through 2023.
"CEOs feel it’s critically important for them to reinvent their businesses for the future."
CEO confidence in revenue over the next 12 months:
Question: How confident are you about your company’s prospects for revenue growth over the next 12 months? (Showing percent change in year-on-year confidence)
"The worst risks—the ones that cause the most harm or loss—often catch us unprepared. Some are unanticipated because they’re unknown, and others are apparent but ignored.” The leading concern finds businesses “already in a weakened state from a prolonged crisis can develop blind spots."
The following analysis explores another risk: looking for “a new normal.” Too many companies tread water. They hope to resume business when the storms abide. The easiest path for them means falling back on legacy methodologies, the “tried and true” accounting methods to manage pricing and inventory. But taking steps backward will shorten their future.
How does your company manage today’s distribution threats?
Today’s distributors feel external, internal, and operational stresses. The COVID-19 pandemic has spawned mutations, rival viruses, and supply-chain misadventures. Continuing inflation has prompted forbidding interest rate hikes. And businesses struggle with recruiting and retaining effective talent.
Distributor CEOs and CFOs must deal with micro- and macro-economic concerns unique to their operation:
- Balancing inventory supply and product demand,
- Strategically offsetting seasonal impact,
- Calculating optimal service levels,
- Forecasting, budgeting, and measuring stock push and pull,
- Minimizing excess inventory; and
- Understanding the importance of inventory turnover ratios.
The following figure illustrates the crucial need for distributors to balance supply pulls and demand pulls. Anticipated profits lie at the center. History, strategy, and a little magic drive most decisions currently.
Central challenge for distributors:
Old school loyalties
Legacy accounting traditions continue to drive the critical balance between Service-to-Inventory and Cost-to-Price in too many businesses. Driven by GAAP Principles and Due Diligence potential, distributors have effectively disabled any potential for adaptability and scalability.
Others have fallen back on systems in place rather than take critical steps into their future. They look at companies of the same size, tradition, and outlook; they effectively seek to be “normal,” just another way of saying “average.”
A Bain & Company (2016) brief noted:
"Many manufacturing firms leave enormous value on the table… They tend to use rough rules of thumb rather than rigorous mathematics, and they lack close coordination among different departments."
The most confident and determined CEOs and CFOs pursue technology and innovation. They embrace challenges as opportunities to improve performance, profits, and customer experience. And more of them have found AI/Machine Learning empowers and engages their employees.
Distributors need tactical tools to balance Price Optimization (The Selling Side) with Inventory Optimization (The Buying Side). They require deep data to design and deliver their futures strategically.
Owners, suppliers, and customers all watch their distributor’s performance, expecting 100% product availability. And they do not want to hear about overstock or stock-outs.
Excess inventory and inventory shortfalls are unhealthy. Distributors feel constant pressure to find the “magic,” that balance point where they have enough inventory—but not too much. After all, their inventory health affects profit margins and the overall customer experience.
Bain recommends a broader holistic approach to S&OP (Sales & Operations Planning). At the center sits a sales and operations partnership, a mutual respect for dynamic supply and demand realities.
The figure below emphasizes the critical, dynamic, operational synergy between supply planning for demand planning.
A broader holistic approach to S&OP:
What “hanging in there” really means
Many B2B businesses have opted to stay the course. They figure the practices that have worked for generations should get them through these challenging times.
However, this coping practice proves regressive. This adherence to the norm shows no readiness for “the new normal.” It craves a return to “the way things used to be.”
Hanging in there lacks the agility and resilience needed to pivot, to reimagine its process and purpose when necessary.
McKinsey & Company (2019) warned:
"The distributors at the greatest risk may therefore be those operating in large segments with high margins, limited technical expertise, low value-added services, low customer purchasing power, and easy-to-ship products."
To optimize pricing and inventory management, a company must recognize internal threats:
- Business infrastructures drive decision-making into too many hands resulting in redundant, dissimilar, and often contradictory processes.
- Sales and Inventory teams need clear direction, goal setting, and decision-making autonomy.
- Vital data remains to be available or unevenly distributed, leaving pricing and inventory controls unfounded.
- GMROI procedures carry pricing biases forward, offering inaccurate and weighted information.
- Sales and Inventory control teams are left to repair customer relationships instead of getting to the root causes of the failures.
McKinsey also observes that “digital advances are game changers in the distribution industry. Those organizations with resilient structures and guidance regularly outperform those without the agility, courage, and willingness to pivot and proceed. The figure below shows the performance gap between resilient and non-resilient publicly-traded distributors over the course of “The Great Recession.”
Resilient distributors significantly outperformed their peers:
A subset of ‘resilient’ distributors significantly outperformed peers through the 2007 recession and recovery.
How future-driven are your operations?
Despite the insistence that they are all for emerging advanced technologies, too many distributors still employ antiquated systems for strategic planning and forecasting.
Sales Teams and Inventory Managers take pride in their spreadsheets. They consider the history, ready to roll out and extrapolate, nudging prices up reluctantly to account for increased material costs and inflation. But otherwise, sticking to past pricing.
They rely on educated guesses, training new hires in old ways. At the same time, demand planning uses heritage forecasting, guessing, and estimating methodologies.
But more and more CEOs and CFOs have looked to opportunities in the current chaos. They have not found energy or engagement in traditional approaches. Instead, they want the tools and strategies delivering short- and long-term gains in mastering their inventory and pricing response.
Strategic ways to balance supply and demand
Every business must balance its supply and demand. Balance ensures the company can meet customer needs and still avoid incurring unnecessary costs or inventory expenses. Here are some strategies for balancing supply and demand:
Best practices for minimizing excess inventory
Inventory data must be fit for its purpose. It must be:
- Trusted as quantitative and free of bias,
- Valued for its accuracy in description and prediction,
- Operationally sound, easily processed, and applicable to other organizational functions, and
- Managed with metrics reporting accuracy, consistency, and integrity.
AI/ML initiatives will deliver reliable results if—and only if—quality data informs quality algorithms. Incorrect or aged data will lead to inaccurate predictive models, resulting in costly mistakes or ineffective strategies.
5 tips for collecting quality data:
- Identify data points that measure what you want and need to measure now.
- Develop a staff that understands that bad inputs guarantee poor results.
- Use data sets to learn from them how they might help decision-making across the business’s functional silos.
- Design and monitor data input practices to avoid duplication.
- Create a sense of change sensitivity in data gathering and execution.
AL/ML also requires data labeling. The overall data set generated at any business remains unlabeled, but effective algorithms must “know” what to learn.
End-users must collaborate and partner on what they want out of such technologies. Then they will tag those relevant data subsets. Businesses should shop for providers who guarantee the least disruptive data analysis and labeling approach. The resulting model should start small, trusting in just a few labels to see how it works.
Inventory Optimization
Economics, polarization, and inflation have hurt distributors everywhere. Volatile pressures have caught many business leaders off guard. And their instinct has opted for a coping mode. Too many are just happy to survive.
However, they must take advantage of the opportunity to adopt and adapt. Their future depends on their ability to find value and utility in the new technologies promising Inventory Optimization.
Inventory stratification or inventory segmentation: Which works best?
The ASCM (Association for Supply Chain Management) defines inventory stratification as “the process of classifying items based on predetermined factors related to a company’s business environment and goals.”
Creating a successful inventory stratification strategy involves balancing item velocity, popularity, and profitability to maintain efficient cash flow and working capital. This method organizes the inventory stock-keeping units (SKUs) and ranks them by profitability and how likely they will move. That means some fast-selling units may outrank the most profitable ones.
Inventory segmentation, however, categorizes SKUs by their importance in terms of a specific metric, such as revenue, cost, or volume. Or it may divide the inventory into classes identified by metric variability.
The figure below illustrates a strategic approach to Inventory Optimization that combines the two strategies.
Inventory stratification/segmentation:
Real-time visibility
With more and more businesses promising same-day or next-day delivery, originators must be able to see what the customer sees. Order something from Amazon, and they will send buyers a photo of the package at the resident’s front door. Customers can track their orders, ensuring that things are on their way.
Any business wanting to maintain optimal stock levels, minimize costs, and satisfy customers must have effective inventory planning. Companies must have a clear understanding of demand signals, those signals indicating customer demand for their products or services.
But in today’s fast-paced environment, business leaders will find it difficult to predict and monitor demand signals. They need real-time visibility into demand signals to make effective inventory planning possible.
Real-time visibility into demand signals requires access to up-to-date information on customer demand, inventory levels, and supply chain data. Decision makers will use this information to make data-driven decisions about inventory planning, including when to order new stock, how much to order, and when to adjust prices. By enabling real-time visibility into demand signals, businesses can minimize the risk of stockouts, reduce inventory carrying costs, and improve customer satisfaction by ensuring that products are always available when customers need them.
Demand signals
Demand signals refer to the information a business uses to determine customer demand for its products or services. These signals can include a variety of data points, such as sales trends, quotes, customer feedback, market research, and online analytics. Businesses can then make informed decisions about inventory planning, pricing, and product development by analyzing these demand signals.
Demand signals can be both internal and external to the business.
Effective inventory planning depends on the accuracy and timeliness of demand signals. Tracking and analyzing demand signals in real-time helps businesses adjust their inventory levels and production schedules. They have the better data to meet customer demand without incurring excess inventory costs of stockouts.
With accurate and timely demand signals in hand, business leaders can share information across functional silos. The sales team, for example, might share customer feedback with purchasing, operations, and shipping.
In summary, demand signals are key indicators businesses use to understand and respond to customer demand. Effective inventory planning and business success depend on real-time visibility of these signals.
Supply signals
Supply signals refer to the various indicators and data points that provide insight into the current state of goods and services.
Leading supply signals include inventory levels, production capacity, and lead times. Tracking these metrics helps businesses assess the state of their operations. With these real-time updates, they can assess the state of operations, making necessary adjustments to meet demand.
Supply signals must be accurate and timely if business leaders and investors expect to make informed and effective decisions.
Healthy vs. unhealthy inventory
A healthy inventory remains fit to purpose if it has the correct percentage of each SKU based on historical and evolving demand levels. A healthy inventory indicates that decision-makers have done their homework on current stock and future probabilities.
Keeping the inventory healthy depends on the proactive involvement of inventory managers. Unhealthy inventory consumes company capital and resources. Unless the inventory moves, the business incurs overhead and labor burden and fails to produce products fulfilling the customer experience.
5 Steps to Optimize Inventory
Strategic planning starts with securing accurate data. Planning needs analytics to identify reorder points and anticipate customer demand. No matter their seniority or experience, individuals cannot dig deep into the data sets to produce reliable information. So, they rely on seniority and experience to hedge their guesses. Some of them can prove close to the mark, but analytics can do the job faster and more accurately.
4 benefits of inventory optimization
- Inventory optimization creates a more efficient supply chain for wholesale distributors. It improves human decision-making and minimizes human error.
- Inventory optimization introduces automation to the inventory management process. It dramatically increases awareness and accelerates cost-effective decisions.
- Inventory optimization leverages technology to advance forecasting, manage seasonal demand, and share real-time visibility.
- Inventory optimization ensures that distributors stock the right products at the right price at the right time with timely analysis and costing of current inventory and predictive analysis.
One CEO remarked that it also had company cultural effects. He found his sales team excited about the new information, and the data created opportunities for collaborative conversations.
Best practices in inventory optimization
Artificial Intelligence and Machine Learning have moved into everyday life. AI/ML drive online and Big Box retailers’ sales success. The customer data feeds them “likes” to customize inventory selection and sales appeal. But advanced technologies can also level the playing field for traditional distributors.
AI/ML technologies drive the robots and systems manufacturing goods. They enable Alexa, Siri, and chatbots everywhere. Now, they can change how businesses manage their inventories.
For one thing, advanced technologies explode legacy bookkeeping disciplines with predictive analytics to:
- Forecast necessary inventory levels despite fluctuating seasonal demand.
- Reduce inventory shrinkage by identifying risk factors like theft, breakage, or logistics.
- Improve customer experience by ensuring stock is on hand for current and future demand, even for emergencies.
- Identify best-selling SKUs and crucial marketing, sales, and cost information.
- Improve decision-making, converting data into valuable information on pricing strategy, supply chain management, and response across the company’s functional silos.
Different AI solutions are available
Artificial Intelligence mimics human intelligence. Depending on how it is constructed and fed information, AI can perceive, learn, reason, and decide on action.
Machine Learning is a subset of AI. It refers to the technology that enables computers and machines to learn from their performance —without human help. ML absorbs unstructured data, text, video, and images, so it can alter a machine’s settings, correct typing errors, and provide analytics for decision-making. As it continues in use, it corrects itself at users’ direction.
Businesses have introduced AI/ML solutions specific to their needs and as part of Information Technology packages.
- Data Mining Software solutions comb through data for patterns of consequence, the strategic information useful to decision makers. But these off-the-shelf packages lack AI’s speed and ability to dive deep.
- Real-time analytics software has been used by distributors to monitor accurate inventory levels. Real-time analytics provides insights to key decision makers almost immediately on request. Artificial Intelligence can stand alone or integrate with existing Real-time Analytics to offer the advantage of predictive analytics.
- Automated decision-making (ADM) requires AI power. ADM has been likened AI on steroids. It consumes massive amounts of data in multiple forms to mimic decision making at pain points. It may be a larger process than distributors want. It requires customizing AI functions to serve different business models better.
- Supply chain optimization has traditionally been managed by hand. But mid- and large-sized distributors process thousands of SKUs, so many that management worries about its KPIs. AI/ML can explore large databases to produce 5-star quantity and quality. Distributors are only one location on a supply chain that runs from Manufacturer through Distributor to the Customer.
AI algorithms can ensure profitability and sustain the supply chain by learning from positive traffic and disruptive process errors. As IBM explains:
"High-performing supply chains enable 'business efficiency and responsiveness, so customers get what they want, when and where they want it.'"
10 hot tips for managing unforeseen circumstances
Yossi Sheffi, MIT/Sloan Professor and director of the MIT Center for Transportation and Logistics, points out that the COVID-19 pandemic affected the supply chain and customer demand. He labeled this economic condition a “black swan” effect, “a rare, unpredictable disruption that causes lasting damage.”
So, what can businesses do?
Demand forecasting and inventory optimization technologies that employ artificial intelligence, machine learning, and advanced analytics will augment the talents and skills of those specialized professionals. The analytics will provide the footprint for solid planning and communication strategies. AI capabilities allow businesses to be further ahead in inventory management than ever in supply chain history. Tech-savvy CEOs and CFOs see the advantage right away.
Pricing Optimization
Businesses use market and end-user data to pinpoint the optimal price that provides value and profit. The better the data, the more accurate and meaningful the price point.
With thousands of SKUs in their catalogs, distributors must identify the price that will prompt customer purchase and actualize the business’s financial goals for each item.
Distributors generate masses of data annually due to the buying and selling of products and services, yet they of ten lack efficient tactics for tapping into this information. As a result, many rely merely on their intuition or collective wisdom when making decisions. They must establish a pricing model and adopt emerging technologies to do their work. The task is daunting, and relying on legacy accounting practices does not help.
Establishing a pricing model
The price at which the customer sees value is one goal. But realizing a profit at that value point drives business growth, investor values, and employee satisfaction. To hit that target price, businesses should follow a pricing model that meets their expectations best.
The cost-plus pricing model
Manufacturers will adopt a Cost-plus Pricing Model. They calculate the cost of materials, research/development, facilities, marketing, and other expenditures. Accounting practices help apportion the costs to different products. They add the desired profit margin to set the preferred price.
Distributors carry the manufacturers’ pricing forward. They accept the price paid and add all their business costs for shipping and receiving, sales and marketing, employee compensation and benefits, facility maintenance, and more. Then, they add their anticipated profit margin.
The Cost-plus Pricing Model works simply; when a manufacturer makes only one item, say a valve, and the company produces no other products or variations on the valve. The manufacturer will raise the price or sacrifice part of the profit margin in a fluctuating or inflationary situation. Another manufacturer produces valves in various sizes, materials, and designs. It also handles valve-related products, like piping, calibrators, and more.
The manufacturer makes an agreement with a distributor to sell and deliver products. The price paid by the distributor becomes its cost. The distributor will add expenses for labor, maintenance, storage, and delivery. But it takes a sophisticated system to price thousands and tens of thousands of units to meet the market and sustain the desired profit margin.
Distributors must leverage their data to produce consistent, sound, and profitable pricing. This is where distributors see their future growth and scalability in technologies like AI/ML—advanced technologies that relieve the stress felt by CEOs and CFOs in mining and optimizing the inventory and price of each SKU.
The price-on-demand model
A Price-on-Demand model focuses on nailing the preferred price customers are willing to pay—based on the value they perceive in the product.
This approach starts with a study of customers’ responsiveness to various price levels. The prices have been compared with competitors and aligned with the current market pricing. Then researchers study the customer response.
However, consumer behavior varies over time, sometimes radically. The changes reflect any number of customer whims, but inflation will reduce the willingness to pay. This demand elasticity creates inconsistent pricing and makes predicting market behavior difficult.
Demand-based pricing may lead to lower prices. But it will also reduce supply and may discourage the production of better goods.
The competitor-based pricing model
The Competitor-based model pursues successful pricing by matching the competitor’s prices. Or, it might set prices slightly below or above the public information on the competition. It has little to do with value and works best where providers have saturated the market.
Competitor pricing is a simple process, but some areas need improvement. Competitor-based pricing risks duplicating the competitor’s pricing errors, It also ignores quality and discourages opportunities for quality improvement. With little price differentiation in the market, other factors will motivate the buyer.
Optimizing prices for maximum return
Price optimization analyzes market data to identify the optimal price point that will satisfy customers, attract more buyers, maximize sales, and increase profit margins. The figure below compares price optimization with standard approaches to pricing.
An illustration of approaches to pricing:
Finding the price that balances customer appeal, value, and profit takes effort. Pricing decisions examine extensive data sources:
- Customer surveys
- Historic sales
- Current contracts (VMI, blankets, etc.)
- Current Inventory
- Operating costs
- Labor overhead
- Quality reports
- Demographics
These market forces function dynamically and unpredictably. Staying ahead of pricing in fluid relationships that link and satisfy provider, price, and end-user proves challenging. But an era of economic volatility and continuing inflation will increase the effort. The figure below shows how price optimization can find the optimal price, satisfying the vendor and customer.
Factors influencing price optimization:
Price optimization is time- and labor-intensive. It depends on information-rich and accurate data sets. This pricing practice must always consider value, avoid guessing, and offer discounts prudently.
Only AI/ML technology can reduce the price optimization process because It consumes and metabolizes massive databases, forecasting high-impact trends in product costs, overhead, and customer behavior.
AI/ML responds to dynamic markets, offering high visibility on real-time efforts and results. It integrates seamlessly with commonly used ERP systems but operates well beyond their scope. And the best-in-breed AI/ML coordinates pricing optimization with inventory optimization.
5 strategies for setting discounts and promotions
Discounts and sales promotions always catch a buyer’s eye. But businesses should use them strategically.
Best practices in pricing optimization
Companies must remain data-driven to survive this volatile and confusing environment. It is not enough to cope or wait it out. The future of work will be new indeed, but it will not be a return to normal.
Distributors must seize the opportunities between the lines of the ill-defined present. It will take daring CEOs, CFOs, and other key decision-makers to leverage their data and master scalable, flexible, and profitable results.
Some decision-makers worry about disruption or believe their in-house IT team can fit all the pieces together. Those teams look backward, searching for sources, repairing systems, and bringing technology current.
But AI/ML can be minimally invasive and non-disruptive. It serves multiple “masters” across all functional silos. It remains fluid, dynamic, and visible. And it designs, invents, and delivers.
New solutions to evolving problems
- Improve supply chain visibility. Prices change constantly. Inflation occurs every time that supply cannot meet demand. It escalates when too much capital in the hands of consumers accelerates demand. So, inflation makes it difficult for companies to negotiate deals with suppliers.
However, because AI/ML makes the supply chain visible, distributors have ready, accurate, and predictive data at hand to share with providers and customers. - Recruit and retain talent: COVID-19 locked down workplaces sending employees to work remotely. Many felt disenfranchised and sought work with other employers. Others spent time considering the purpose of their work and its value to them personally. Many of them sought new work in different fields. Moreover, unemployment is at an all-time low, so there is no evidence that people have simply dropped of the labor force.
The resulting resignations and career changes do reveal that businesses must reimagine how they function with people. Distributors need to recruit talents with passionate interest in technology. Jobs should be structured to promote collaboration, and management must respond quickly and empathetically to identified worker pain points.
It is also time for distributors to reveal the “tribal knowledge.” Structure mentorships that pair matured talent with the new. Let the mature employee take the new hire through the known and remembered history. But let the new talent mentor the old in reverse, helping to familiarize with the technology. - Support salespeople: The less they know, the more salespeople feel like runners. They usually have their head and hands around basic information: names, phone numbers, addresses, and the like.
However, they often feel they do not have a voice in processes and pricing. AI/ML provides high-volume information, much of which they have a need-to-know. Being able to track product, purchasing, and delivery would ease their work. With expanded data, they should never find themselves caught with guessing a price based on limited information. - Improve working capital (WC): Improving WC is cheaper and less risky than soliciting new bank loans, debt instruments, or equity. WC also provides visibility on how efficiently the company utilizes its invested capital.
Companies that use the latest data processing tools and analytical insights have unprecedented opportunities to understand their accounts better. Analyzing numerous transactions over time, advanced analytics can identify patterns in receivables and payables with pinpoint accuracy. - Price for profitability. In the past, some businesses left pricing to the CFO. The CFO knew all the numbers and controlled most of the business data. Given that position and authority, CFOs would price for profitability. That is, prices would be set to ensure an identified profit margin.
The days when CFOs could rely on their ability to affect cost are long gone. With more complex products and fast er-changing markets, CFOs must unlock prices to stay competitive and maximize profits through innovation.
The supply and demand pulls on most products are out of balance today. To truly understand “dynamic pricing” (profitability at the customer, product, and volume levels), visionary CEOs and CFOs need robust data about how much each decision will contribute to their overall profitability.
Five Key Takeaways
This paper has addressed the CEOs, CFOs, and other C-suite decision-makers tasked with reimagining the future of their business.
Business leaders can master inventory and pricing optimization, building a resilient, agile, scalable, and profitable framework only if they employ advanced technologies. Readers should take away these key points:
.... visionary CEOs and CFOs need robust data about how much each decision will contribute to their overall profitability.market and end-user data to pinpoint the optimal price that provides value and profit. The better the data, the more accurate and meaningful the price point.
What To Look For
CEOs and CFOs must master Inventory and Pricing Optimization if they want to transform their business to succeed and grow despite threats and hurdles.
Here are 10 benefits and features of the best AI/ML powered price and inventory optimization technologies.
1. Strategic thought leadership, helping decision-makers with industry expertise and market insights, including trends/patterns, alerts, and competition benchmarks—to plan for uncertain days.
2. A SaaS application that runs Inventory Optimization and Pricing Optimization simultaneously and works seamlessly with all ERP systems.
3. A provider partnership proactively involved with businesses of any size.
4. A track record of performance with well-known organizations, able to provide enthusiastic testimonials.
5. On-going expert advice and guidance in processes and best practices tailored to the distributor’s individual needs.
6. Machine Learning algorithms developed with years of tribal knowledge to generate meaningful prescribed actions.
7. Ability to integrate rapidly, doing the heavy lifting while the resident IT Team can move things forward quickly.
8. Ready to identify healthy and unhealthy inventories, recommending goal-oriented actions to resolve the issues.
9. A track record showing how AI/ML helped companies achieve high service levels, better inventory turns, and improved return for the investment in inventory through price optimization
10. A commitment to help CEOs and CFOs master cross-functional sharing and collaboration on inventory and pricing issues.
Distributors face uncertain futures unless they recognize opportunities in the current threats to their way of doing business. CEOs and CFOs can master the storm and reimagine their future using AI/ML technologies to achieve inventory and pricing optimization.
