optimization Archives

Locanis Discusses Its Novel Approach to Warehouse Optimization

Logistics Technologies | By Clint Reiser • 08/31/2022

Earlier this month, I had the opportunity to speak with Bengt Tuner, Group CEO of Locanis. Bengt and I spoke about Locanis and the fact that it offers a fairly unique technology solution that doesn’t fit well into the traditional categories in the market – like WMS, fleet management, or warehouse control systems. Along with the benefits of a novel approach comes the difficulty of succinctly communicating its way of delivering customer value. Our goal was just this – to clearly capture the solution’s means of delivering value. I asked Bengt a few pointed questions that I thought would best communicate who Locanis is, what they do, and how they do it. First, tell us a little about Locanis. And second, what is the product or solution that Locanis provides to the market? Third, and perhaps most importantly, how does your solution deliver value to its users? Fourth, can you provide some specific examples of information captured and utilized, and how Locanis uses this information to improve productivity. And last, what types of warehouses are currently using your solution?

Below are some key points from our discussion as well as the full video interview.

Locanis and Site Optimizer 360⁰

Locanis is a Germany-based company with headquarters in Berlin and offices and representatives in a variety of countries including Turkey, India, Singapore, and the US. Locanis’ product is a software execution platform called Site Optimizer 360⁰. The software solution was designed with the goal of optimizing the entire spectrum of functionalities required on a site – leading to the chosen product branding. Bengt believes that the solution’s task management system contributes greatly to its differentiation from other offerings in the market, such as WMS, fleet management, or warehoues control systems.

How Locanis’ Solution Delivers Value to its Users

The system was initally developed with a focus on optimization. They have a patented AI algorithm, and the back end of that optimizes the entire site – all resources utilized on a site. Not just forklifts, AGVs, pickers, etc., but all resources. The second part is that the software is built on the use of in-motion precision technology. This enables a continuous in-motion view of resource location and what they are doing. This provides proximity insights along with resource capabilities. This information, along with tasks, go into a central pool, where the system’s brain looks at rules, priorities, and objectives, and automatically distributes tasks to resources. Understanding all of this information, along with interdependencies, enables time savings and operational efficiencies. Having one central brain also provides site wide optimization rather than localized, less optimal methods of achieving objectives.

Information Processing Examples

Resource capabilities that the configured in the system include the load carrying capabilities or load capacities of forklifts, AGVs, etc. But the information goes beyond these attributes. For example, the experience level of the workers aligned with the resources are also maintained. Capabilites may include load weight limitations for new forklift drivers or an exclusion from load movements in tight spaces or narrow aisles. In-motion transparency can also be used as a determinant of task execution. For example, a geo-fence can be established and the system can them automatically assign the next task to a given forklift once it enters that zone. Automatic triggers such as this example are one way to this increase the level of automation and productivity from the system.

Types of Warehouses Using Locanis Technology

A customer in fast-moving consumer goods (FMCG) is using Locanis for the movement of cartons and pallets. This customer is using 2D Lidar to track and assign tasks to the forklifts. Another customer manufactures small, medium, and heavy trucks. The specific operation involves a lot of material movements, including small parts. Locanis has another customer that uses its solution in a manufacturing operation with small parts movements. Interestingly, Locanis is in discussions with airports – as the operations have similar attributes and the value proposition is similar to its benefits in a warehouse – material movements that can be optimized with greater intelligence. Another prospect is looking at using Locanis technology for an agricultural operation – the movement of corn or wheat or similar with front loaders moving goods from the field to a transition point and onto the correct stations. etc. Optimal resource utilization for task execution is a common thread for all of these scenarios.

The full video discussion is below:

Supply Chain Planning for Dummies

Supply Chain Planning | By Steve Banker • 02/21/2022

Supply chain management has been in the news. Because of the pandemic and resulting product shortages, the topic has never been more discussed. But the intricacies of supply chain management are beyond the grasp of most. One key solution used to help manage complex supply chains is supply chain planning (SCP).

Supply chain planning is a complex solution. What follows are some key things a newcomer to the supply chain profession, or an interested reader of news headlines, needs to know about SCP technologies, processes, and roles.

Supply chain planning solutions can have a very good payback. The primary payback for demand and supply solutions comes in the form of reducing the amount of raw material, work-in-process, and finished goods inventory a company needs to carry. A network design model figures out where factories and warehouses should be located. Here the savings are based on transportation and facility cost savings.

There are different types of supply chain planning solutions. The key solutions are demand forecasting/inventory optimization, supply planning, and network design.

There is no point in reducing the amount of inventory carried if it results in poor customer service and lost sales. SCP solutions set target service levels, for example 99% for the most important customers and 95% for the rest, and achieves those service level targets at the lowest cost.

Supply chain planning solutions are tradeoff machines. A planner can see how much extra it would cost to take service levels from 95% for all customers to 99%. But it is not just service level and costs that can be traded off. A planner could ask the SCP engine to achieve 95% service, with CO2 emissions under of under a million metric tons at a given factory in the coming month. This would be a three-way tradeoff. Other tradeoffs, to maximize cash on hand or preferred suppliers or customers to do business with, are possible as well.

Supply chain planning takes place at various time horizons. Each time horizon usually has its own model associated with it.

Long-term planning – often called strategic planning – has a time horizon of 1 – 5 years, and the plans may be reviewed when a new strategic decision needs to be made, or on a quarterly or annually basis. Issues addressed at the strategic planning level include investment in production capabilities, new storage and logistical capacity, supplier agreements, new markets and distribution channels, and introduction of new products. The key solution for this is network design.
Medium-term planning – known as tactical planning. This is typically monthly planning are plans are created for the next 12 to 24 months. Only the next month’s plan firm. The key solutions are supply planning, based on a broad model of the supply chain, and demand planning. It is embedded in the integrated business planning (IBP) process. IBP seeks to balance the demand forecast with what can be made in the most profitable manner. Some companies are working to engage in IBP planning on a weekly basis. While difficult to do, there is evidence that does result in better planning.
Short-term planning – known as operational planning. This takes place on a weekly or daily basis, or sometimes even multiple times a day. The IBP plans are sent to the plants with what needs to be manufactured and to distribution on what needs to be shipped to customers. IBP planning is not perfect. No plan is. Customers decide to change or cancel their orders, or machines break down, or ports get backed up. The operational plan seeks to deliver on what has been agreed to in the IBP meeting in the best way possible considering the issues that have arisen. This is typically accomplished with a very detailed supply planning model that models a specific manufacturing plant.

Concurrent planning links execution plans, the plan for what will be made the next few days or weeks, to the longer-term financial plans, often called integrated business plan. As new short-term plans are created, the linkage to the revenue and profitability goals based on the initial IBP plan becomes instantly visible.

Supply and network design models are constraint-based models. In a supply chain you have orders, suppliers, raw materials, machines, customers and other entities as well. Some orders are big, some small. So, order minimums and maximums, are constraints. The orders have products. Different products are made from different raw materials and components. The Bill of Material, how a product is made, and how a product flows through a factory offers a new set of constraints. How long it takes to set up a machine, what the throughput is for different products, introduces other constraints. Customer due dates are a constraint. A big supply model can have over a million constraints on an end-to-end supply chain. There are MANY different types of constraints.

It used to be that these broader supply models used in IBP considered mainly manufacturing and distribution constraints. Now they are considering transportation, supplier contracts, workforce, customer constraints, and many other things in an end-to-end supply chain.

Supply models for operational planning don’t go broad, they go deep. A factory planning solution will model the factory floor set up times by machine for example. The IBP model, the broader network model, might just say on average across all products the throughput is 1,000 units per hour.

Supply chain planning engines “optimize” the schedule. An optimal plan is not a perfect plan. Optimization means that there are so many ways an end-to-end schedule could be set up, that even if the planning engine ran a million years it would still not have looked at all the options. “Optimization” uses clever math to come up with a very, very good solution in a short planning run.

Supply chain planning solutions use a variety of different types of algorithms, as well as machine learning and artificial intelligence, to provide optimal forecasts and plans. Machine learning is one type of solver. It is not the best of all solvers, it is very good for certain types of solves, and not good for other planning problems. SCP vendors need to have a library of algorithms and solver technologies, and then apply the right technology to the right problem.

Even though not all scheduling scenarios are considered, good planning depends upon powerful computers with fast databases. “Scalable” – big fast planning engines – run on specialized databases. In-memory is most common, but graph databases are beginning to be increasingly used.

There are different types of planners. Many companies have demand planners and supply planners. We are starting to see network planners who do both the supply and demand planning. Using network planners does make sense, not just because there are fewer planners, but because there are fewer times a plan must be handed back and forth. Planning is becoming increasingly autonomous. More and more tasks are being taken over by the planning engine, leaving planners the opportunity to run scenarios on only the most important problems.

The supply chain planning software market grew at a double-digit rate over the last year. It is unusual for a mature, multibillion dollar market to grow so fast. Clearly, the pandemic has been a driver. The pandemic has highlighted the need for companies to be agile and resilient. SCP is one key technology for achieving these goals.

Achieving Energy Savings through a Digital Transformation: The Vopak Case Study

Logistics Service Providers | By Steve Banker • 02/18/2019

Royal Vopak is a leading independent tank storage company founded over 400 years ago. It specializes in bulk storage of liquids including oil, chemicals, vegetable oils, biofuels, and gas. The Rotterdam based company had revenues of 1.3 billion Euros at the end of their last fiscal year and operates 66 global terminals, mostly near ports. They are a public company and thus publish an annual report. The annual report states that they are “making substantial investments to deliver the full benefits of the digital transformation.” These include investments in Big Data, dashboarding analytics, optimization technologies, new sensors at their terminals, mobile technologies, and smart robots and drones.

Their terminal in Savannah Georgia serves as one of their test bed sites for developing and implementing new digital technologies. Savannah is where Vopak piloted a project with Atomiton called the digital terminal to reduce their energy usage and help meet their sustainability goals. A week and a half ago, at ARC’s Driving Digital Transformation forum in Orlando, Diana Salguero – the IT Director for the Americas at Vopak – and Jane Ren – the CEO at Atomiton – spoke about this project.

Vopak’s Savannah Terminal Undergoes a Digital Transformation

Ms. Salguero said “technology leadership is a strategic pillar” for Vopak. And technology leadership requires innovation. Based on business challenges, proof of concepts (POC) are reviewed and approved. Some of these PoCs move to a pilot, and if successful Vopak then scales the technology across the remaining terminals that have a need for the solution and are capable of using it.

The company has enabled a group of bright people across operations and IT to work together with startups or leading innovative technology companies through a variety of proof of concepts and pilots. These vendors can include suppliers that would have historically been considered too young or too small for this conservative 400-year-old firm to work with. These vendors help Vopak understand what might be possible. The innovation team has enough funds to work on number of proof of concepts. In deciding which POCs to tackle, they create user stories to share with the business to gauge the interest and potential value of a project. If a POC is deemed successful, they create videos to share across the business. The goal of these videos is to create excitement, and to get other terminals with similar problems to pony up the money for a pilot.

A Digital Transformation at the Savannah Terminal

In this case, the challenge involved getting visibility of electricity usage in real time. Peak time energy costs are higher, but these hours were also when Vopak’s operational activities were at its peak. The goal of the project was to reduce peak energy usage and energy costs, and shift activities like heating (and some cooling) to non-peak hours while reducing energy usage. To do this, they needed real-time energy consumption across different machines and activities. The equipment included legacy meters that were not digitally enabled, but the company did not want to spend millions of dollars to swap them out.

This is where the Atomiton solution came in. The Atomiton platform allows for communication with sensors, actuators, devices and machines and then provides a programming language to help make this data intelligible and usable. Then the software uses analytical predictions to recommend optimal scheduling to reduce peak energy.

From a material flow perspective, multiple trucks come to the terminal each day. As customers scheduled their daily or weekly activities for product movement, the planner at Vopak now had enhanced visibility through the Atomiton platform. The planner was able to schedule the daily activities so that more work could be done outside peak electricity usage.

At the terminals, some products in the tanks required heating to the right temperature to make them viscous enough to travel through the pipes. The pipelines and the pumping stations are also big users of electricity. In the proof of concept, the goal was for the planners to see the energy usage, and then intelligently shift operations off peak hours to lower electricity costs. Historically, because there was not visibility to both heating operations and the slot booking schedule, the tanks were heated on an ongoing basis to keep them in the required temperature range.

The project started as a proof of concept at just one substation at the terminal. This POC did not generate the savings Vopak was looking for. “But a proof of concept never fails,” Ms. Salguero said, “you just learn from it.” The issue with this POC was that it was limited to one area of the terminal, which limited the ability of planners to shift heating operations off peak hours. This was because shifting energy usage for this specific substation was not productive unless all the substations were also added to the platform.

Machine Learning and Optimization Enable a Digital Transformation at the Savannah Terminal

Nevertheless, the company decided to move forward with the pilot. They believed they had learned enough to predict that if the visibility extended across the entire terminal, there would be significant savings. During the pilot stage they also employed machine learning to discover just how long it takes for a tank with a particular product stored in it to cool. The Atomiton platform’s machine learning also factors in external conditions like ambient temperature and humidity. The machine learning feedback loop is still in place. The platform continues to improve its ability to predict how fast materials will cool.

One of the goals of this project was to develop of an application that helps with the optimization of the operational activities. The problem was complex enough – material/tank cooling properties, a week-long slot booking schedule, the weather forecast, and a set of linked tank heating, pipeline movement, and pumping activities – that manual planning was not going to lead to optimal savings.

They were right to proceed! The pilot achieved a 25% reduction in peak energy, 20% in overall energy savings, and a 5% increase in labor productivity. Vopak will soon finish the evaluation and decide whether to scale this across multiple terminals. While Ms. Salguero did not definitively say they would proceed further as this decision will be made by the Global Operational Department, the project was so successful it certainly seems likely. For Vopak, the phrase ‘digital transformation’ is not empty rhetoric.

Price Optimization Finally Becomes a Tool for Integrated Business Planning

Business Intelligence | By Steve Banker • 03/06/2017

In the year 2000, supply chain optimization software companies were hot. Optimization was the new Messiah that would lead supply chain practitioners out of the desert. Wall Street agreed. They put a high valuation on i2 Technologies, the leader in supply chain planning and optimization. In that year, i2’s leading competitor, Manugistics, acquired Talus, a price optimization solution. Price optimization based on price elasticity models is optimization on steroids. Price elasticity shows the responsiveness, or elasticity, of the quantity demanded of a good or service to a change in its price. Mathematically, it shows the percentage change in the quantity that is demanded in response to a one percent change in price.

At that time, advanced statistics, not optimization, were being used for demand forecasting. Price elasticity promised to help move industry beyond forecasting demand to profitably shaping demand. And the core supply chain process, Integrated Business Planning, seeks to profitably balance demand and supply. Price optimization promised to greatly improve that process.

In that time period, revenue management tools were being used by the Airline and Hotel industries. But its use had not spread beyond those two industries.

At the time, the Manugistics acquisition of Talus seemed like a great move. And for a few years, I pestered Manugistics to allow me to talk to customers that had implemented this solution in other industries. Manugistics could never provide a good reference. And over time, the discussion of price optimization in supply chain management dwindled. I always wondered why.

Andres Reiner, CEO of PROS

In a discussion with Andres Reiner and Craig Zawada, the CEO and Chief Visionary Officer respectively at PROS, I learned why. PROS describes themselves as a “revenue and profit realization solutions” provider. In mathematical terms, they describe what they provide as not being price elasticity software, but rather a “win rate elasticity” solution. And PROS has managed to successfully implement its software to more than 40 industries.

Craig Zawada, Chief Visionary Officer at PROS

Mr. Zawada explained that when companies that sell products or services to other businesses – a business to business (B2B) environment – those companies don’t have enough variation in their pricing for a traditional price elasticity calculation. Further, business to consumer markets have much more price elasticity than B2B. For a core commodity, a company is going to buy the product; it is not a buy/not buy situation like in B2C. And while pre-buying for volume discounts is possible, that does not much affect the total volume bought over time.

PROS uses a variety of transactional, customer and product attributes to create micro-price segments. As one example of the kind of logic used, a customer buying a commodity that represents a very small portion of their total spend would not be as sensitive as if were a significant percentage. In the food industry, food prices are affected by commodity prices, so PROS seeks to forecast commodity prices, and use the relationship of the commodity price to the finished product price to help forecast how likely a customer is to buy a food product at a certain price. PROS also recommends which finished products will optimize revenue. For instance, for milk producers, it may be better to produce skim milk or yogurt instead of whole milk or cheese. Or for a beef producer, depending on the season, it may be smarter to cut roasts rather than steaks to maximize revenue.

Transactional data for electronic devices can include pricing not just the finished product, but looking at things like how much of a demand surge comes from models, how truly “new” those models are, and whether customers always bought a computer with the maximum amount of disc space or the fastest speed. This means that the PROS solution needs not just the product master data, but the bill of material and buying history as well. And obviously, the solution needs customer master data. One attribute is particularly critical: PROS always seeks to store the relationship of what a particular customer paid to the average price paid in the market. In short, this is a Big Data solution.

While PROS most commonly sells their solution to a sales executive, pricing leader, or someone in finance, they do have customers that are using their solution in their Integrated Business Planning processes. In short, price optimization has finally become a supply chain solution.

The Internet of Things is Nothing New

Business Intelligence | By Steve Banker • 02/01/2016

I’ve made the argument before that the Internet of Things is nothing new. In the logistics space, for example, we’ve been taking RF scans and using that data to improve warehouse processes since at least 1975 when McHugh Freeman, an early warehouse management system supplier, began business. Internet protocols make it easier to communicate that sensor data to applications. This is a big advance, but I don’t think we are seeing fundamentally new types of applications; just better and cheaper ones.

The process manufacturing industry has been at this for a long time as well. There was an interesting article written in Forbes.com by Peter Zornio, the Chief Strategic Officer of Emerson Process Management. Emerson Process Management is a leader in in helping businesses automate their production, processing and distribution processes in several process industries. Process industries make their products by using formulas and manufacturing recipes. Process manufacturing include industries like Chemicals and Oil & Gas.

Process Manufacturing is Complex
(Photographer: Andrey Rudakov/Bloomberg via Getty Images)

I’d like to review some of the points made in this article because I think they provide insight to not just how IoT could evolve in process industries, but also broader insights that also apply to logistics professionals.

According to Mr. Zorino, in the process industries, IoT has been used for the past 25 years – “ever since the development of microprocessors and network-based instruments… Many of these enterprises work with products and materials that can be readily measured as they flow through pipes.”

Monitoring the performance of individual pieces of equipment with sensors and wireless communications is both relatively simple and can provide good ROI based on better machine uptime. The payback is only getting better. “Driving these changes are increasingly inexpensive sensors, the maturation of the Internet, and the beginnings of enhanced analytics. Sensors are now so cheap and easy to install – no drilling, no screws, practically “lick ‘n’ stick” in many cases – that we now refer to ‘pervasive sensing,’ and use them especially in locations described within our industries as the four ‘d’s’ – dull, dangerous, dirty and distant.”

In the process industries, these changes are leading to an expansion of IoT’s role. “Until recently, only process control and safety functions were monitored and connected. Now, with costs plummeting, areas like plant and equipment reliability, energy management, personnel safety and environmental compliance are increasingly being addressed.”

But the next step forward – monitoring an entire process or operation – is much bigger than a focus on individual assets.

Big assets like industrial plants are a lot like human beings: They’re complicated, mercurial, and different – every single one. In any given plant, the equipment keeps changing as it wears or gets replaced. The supervisor who ran the operations yesterday is off today and has been replaced by one who runs things differently.

Even the weather has an impact; when a warm front blows in, performance changes.

As a result, modeling most complex processes or operations requires subject matter experts with a really deep and comprehensive understanding of how everything works, separately and together. Analyzing the resulting data is no easy task either. It’s often both science and art – not unlike a doctor’s interpretation of a patient’s chart and own words. These kinds of interpretive skills do not grow on trees – and certainly not within most companies.

The upshot: Unless they’re willing to outsource the modeling of their operations as well as the collection and interpretation of their data, many industries will be limited in what they can derive from the IoT by their own in-house skills – at least until applications can be made more sophisticated.

The most complex models we have in logistics are probably supply chain network models, provided by software companies like LlamaSoft and JDA, which help us understand where are warehouses and factories should be located and how we should engage in transportation routing.

My process automation colleagues argue that models in the process industries are far more real time, which adds additional layers of complexity. These models have to be adopting to changing conditions on an ongoing basis. For example, the models change set points across a variety of machines and instruments so that when “a warm front blows in” yields can still be optimized. Or if one set point drifts outside of a specified control limit, other set points change to compensate for that deviation.

One conclusion, is that to fully leverage IoT across extended supply chains we may find ourselves building far more real time models than we have ever seen before. As one small example, during last year’s Long Beach labor strikes, shippers shifted volume to other ports. They might book on a ship headed to Vancouver only to find out a slew of other shippers had done the same. Delays by port were changing on a day by day basis. What if models of port throughput were used and combined with AIS Vessel tracking data? If the model understood how many ships were sitting outside a port, how many were headed to that port, the relative sizes of those ships, and the throughput capacity of the port, shippers could have made much better port scheduling decisions.

A key theme at the 20th Annual ARC Industry Forum, taking place next week in Orlando, will be the Internet of Things. I will be moderating a panel called Building a Supply Chain Control Tower for IoT Visibility. Our panelists have built very advanced and interesting visibility solutions that leverage IoT. The panelists are Jeff Tazelaar, Global Leader – Auto ID, RFID, GPS and Telemetry Expertise Center at Dow Chemical; Jan Theissen, Director Strategy and Methods, Global Purchasing and Materials Management at AGCO; and Tom Moroney, VP Wells and Facilities Technologies at Shell. Let me know if you will be at our forum and would like to meet up with me (sbanker@arcweb.com).

Revisiting Supply Chain Planning

Inventory Management | By Clint Reiser • 09/30/2015

I chose “Revisiting Supply Chain Planning” as the title for this blog post because I believe the phrase captures both the current focus of my research agenda (what I am doing) and the heightened priority SCP has taken on the agenda of many supply chain executives (what practitioners are doing). Earlier this week, I began updating ARC’s Global Supply Chain Planning (SCP) market study, last published in 2013. It’s worth noting that the SCP study was also the first one I developed as an ARC analyst back in 2006. Shortly after I completed the SCP study in 2006, a colleague of mine implied that the SCP market would not change much, because most vendors now offered optimization (known as APS back then) and they all had access to the same algorithms. I couldn’t disagree more. A lot has changed over the last 10 years – including the operating environment (supply chains), planning processes (the depth, breadth, and frequency of analysis) and SCP software (speed, capacity, scope, ease of use, and breadth of functionality).

The Changing Operating Environment
Global Network Modifications
There is evidence that a tectonic shift in global production and distribution processes is currently in progress. This trend is nearshoring – where many North American and European-based manufacturing companies are relocating production from historically low-cost but geographically distant locations such as China, to locations closer to the point of consumption. In fact, David Simchi-Levi recently wrote an HBR blog titled You Can’t Understand China’s Slowdown Without Understanding Supply Chains that suggests China’s recent slowdown may be a result of this global nearshoring trend. According to a study by AlixPartners that I recently referenced on LogisticsViewpoints, rising labor costs in China are hindering that region’s cost advantages. At the same time, other important inputs, such as natural gas prices and the premium placed on supply chain agility, are encouraging practitioners to reexamine their supply chain networks. I am particularly interested in learning during my research process about the ways in which strategic network design applications are currently being utilized to model the nearshoring options available to these firms and the degree to which this trend is impacting the SCP software and consulting markets.

Structural Shifts in Order Fulfillment
The growth of e-commerce and the demand for next-day delivery are changing the status quo in e-fulfillment from large, rural distribution centers to a greater number of smaller DCs proximate to population centers. This is another structural shift in supply chains that requires network redesign, scenario analysis, and updated fulfillment planning models. Similarly, retailers with stores are evaluating the merits of omni-channel flows to determine the potential benefits and costs of these options. As an example, an executive from one large retailer informed me that they are engaging in a smart fulfillment sourcing initiative to determine the merits of more widespread ship-from-store operations. Including store locations as sources of fulfillment and reverse logistics creates a step change in network complexity that will benefit from robust cost-benefit analysis.

Planning Process Sophistication
Planning processes have become much more sophisticated over the last ten years. Practitioners are currently engaging in planning optimization runs more frequently. At the same time, planning processes are more integrated than they were in the past – considering a broader range of inputs, factors, and constraints. The increased adoption of sales and operations planning (S&OP) is a prime example of more holistic planning that enables robust scenario analysis considering cross-functional factors. Also, data has become more readily available and more granular, leading to greater insights. Finally, all of this has been supported by cost-effective, high-speed in-memory computing capabilities.

SCP Software Performance Improvements
I do agree with the essence of my colleague’s statement in 2006 that vendors have access to the same algorithms. However, I also believe that the application of the data to the optimization algorithms, the degree to which the objective function represents reality, and the extent of variables and constraints built into the models varies by vendor and specific end-user. Some examples of distinctions include multi-echelon inventory optimization as opposed to single level optimization, use of POS data to adjust replenishment plans (demand sensing applications), replenishment planning that includes warehouse constraints (integration of planning with execution), and the ability to distinguish product sell-through from sell-in.

Conclusion
As I update ARC’s study of the global SCP market, I would like to hear about interesting SCP processes and novel applications of supply chain planning software. Please reach out to me at creiser@arcweb.com if you are a practitioner with an interesting story or a SCP software supplier with a novel solution.

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