You’re looking to optimize your supply chain. What tools can you use to make it happen?

Supply chain tools used for optimization: A. PLANNING: - Demand forecasting - Demand sensing B. SOURCING: - Supplier selection - Supplier segmentation - Supplier performance mgmt - RM planning - Spend analysis C. PRODUCTION: - Capacity and resource planning - Production planning & scheduling - Manufacturing Execution System D. LOGISTICS - Warehouse/DC operations design (Receiving, Putaway, Inventory Mgmt, Order Fulfillment) - Distribution planning - Transportation mgmt - Returns mgmt and reverse logistics Each of the above areas would be supported by systems, software etc. and the corresponding KPIs/performance metrics would be measured, monitored and analyzed through suitable visual management tools.

Supply Chain Optimization is not straightforward using software and getting benefit of this. First we need to find what are the products and/or SKUs you need to use supply chain optimization 1. Are your trying to maximize revnue/profit/ contribution or mimize inventory cost. 2. What other Om parameters that we want to concentrate like Time (laed time, process time), Cost and Flexibility ? 3. Whether it is an integrated supply chain model or model for the whole business 4. On what matrix of Supply Chain management does the company like to optimize. Two tools are important : optimization and forecasting at the data analyctics level At the company needs to deal with decision if dynamic pricing will be an imporatnt decision variable 3.

To optimize a supply chain, you must first discover the biggest problem(s) in your supply chain. As Steve Jobs once said "if you define the problem correctly, you almost have the solution". Supply chain problems often arise due to fast-changing internal (internal processes) and external environments (competition, market, technology, economy). To discover internal problems, transaction data (PO, sales, shipping, ...) and visualization and reporting software (e.g., power BI, SQL, tableau) are necessary. To benchmark your company and discover external problems, you need business databases embedded with powerful visuals (e.g., S&P MSCI, Bloomberg, World-bank, and Supply Chain Data scdata.ai).

One has to first break down Supply Chain Management into associated processes: 1. Demand Forecasting One has to learn tools like Basic Smoothing Methods, Multiple Linear Regression, Time Series Analysis, etc. A software tool like R or Python can come in handy. 2. Supply Planning Withing supply planning, the first part is procurement planning. Identify the product and then develop purchasing strategy Apply Kraljic Framework. Regarding the other part of supply planning, i.e., production planning, one has to get accustomed to Linear Programming for allocating production. 3. Inventory Management: One has to be conversant with probability theory to understand the stochastic nature of demand. Further, knowledge of MILP could also be handy.

Data analysis tools are commonly exploited to discover if there is someting unusual going on within supply chain system. Usually, a dashboard comes very useful to track and trace SC system performance. Dashboard usually made up of several data viz such as graph, chart, etc. This data viz help stakeholders to identify if there is the need of improvement on SC system. Once the issue is revealed, then it will be easier to take action in resolving said issue.

Modeling tools for supply chain optimization - 1. Gurobi,CPlEX ( solvers) and Use Python, Ampl or GAMS for programming or Hybrid one- AIMMS 2. Off the shelf Softwares- Coupa Supply chain guru, AIMMS SC navigator, Anylogistix, Optilogic etc. Each of these software are at different levels of maturity. Depending on Type of problem & complexity involved - Companies can evaluate and use them.

We performed mathematical in python API of CPLEX. With some necessary changes for each use case, we can plan: - amount of raw materials bought from each supplier _ amount of products shipped from production facilities to warehouses, and to customers - amount of products produced in each production lines

One of the best ways of choosing an optimal supplier between a group, is using Multi-criteria decision-making method. It is used to determine the best alternative by considering more than one criterion in the selection process.

This question is somewhat unclear and broad, and the term "tools" is also ambiguous. In general, there are a number of classes of tools that help model supply chain: Enterprise Resource Planning (ERP) systems, Supply Chain Management (SCM) tools, simulation tools, optimization tools, data analytics and business intelligence tools, inventory management tools, transportation management systems, supplier relationship management tools, demand planning tools, and geographic information systems (GIS), and so on. In each category, examples of specific tools can be offered, including bespoke tools and algorithms that can be developed (e.g. python), or these can be commercially off-the-shelf products.

Modeling tools may vary depending on the problem and its class. If the dimension of the problem and the number of its limitations allow you to build a mathematical formulation of the optimization problem with the setting of the target function, variables and constraints, then it will certainly be effective to solve it in this way using high-performance solvers. For supply chains, these can be the tasks of planning daily deliveries, storage in warehouses and other medium-term planning tasks. Simulation methods are effective for operational planning tasks, for example, for logistics tasks.

Hands of the wheel supply and demand management solutions are needed in order to respond market changes rapidly and optimize supply planning. It requires - ML science for demand forecasting, - optimization science for profit maximization, - what-if business scenario planning with simulation science, - decision engines for high judgement items with prescriptive science, - decision engines for low judgement items with artificial intelligence, and - integration with complex adaptive service systems science. As a summary, organizations need multi- and trans-disciplinary science perspectives.

I am giving some examples of optimization techniques to solve(optimize) supply chain problems: 1- Streamlining transportation costs through the application of linear programming. 2- Finding the most efficient batch sizes in manufacturing using integer programming. 3- simulation modeling to explore diverse demand scenarios and enhance inventory strategies. 4- resource allocation efficiency by optimizing the structure of the supply chain network. 5- queuing theory to address waiting times and enhance resource utilization. 6- demand prediction accuracy by employing time series analysis and forecasting models. 7- Tackling intricate optimization challenges, like routing or scheduling, through genetic algorithms and heuristic methods.

Powerful production scheduling software can efficiently align (synchronize) production operations and material procurement activities with customer demand such that lead times quoted for customer orders become shorter, on-time delivery is high and WIP and material waiting times on shop floor are controlled. The software also helps predict bottleneck formations and mitigate them proactively. MRP production scheduling module was intended to do the same but it failed miserably. I say this based on my experience with order-driven production in complex job shops. Until now, I did not find an application of a mathematical optimization solution in any job shop for this purpose. Such scheduling in real factories is beyond mathematical treatment.

It is possible to solve mathematical models resulting from decision-making problems at all levels and segments of supply chain in a reasonable time using these tools.

There are a lot of optimization tools to improve your supply chain. Ranging from classical optimization like a transportation problem - where you should allocate vehicles to customers to minimize your cost or to fit within a time window - to a Machine Learn approach where you can forecast the demand for your SKUs given their historical data and another features, allowing you to plan your production, inventory and operations.

Most often, optimization tasks and what-if analysis help to improve the quality of solutions. The implementation of such solutions can improve the cost-effectiveness of processes by 10-20%. It is very important to correctly integrate these solutions into the process and be guided by the strategies that offer the most effective solutions.

Tools such as decision trees, sensitivity analysis, scenario analysis, and multi-criteria analysis enable the comparison and evaluation of various alternatives. They help assess the uncertainty and robustness of solutions while weighing the pros and cons of each option. By leveraging decision support tools, businesses can make informed and rational decisions that align with their goals and preferences, enhancing the overall decision-making process.

Tools were listed are all relevant. The choice of any one or a combination of the them is case specific and is dependent on the size and the business nature.

Ici, le challenge, quand bien même on a des outils d'aide à la décision est le choix des seuls ou paramètres critiques. Pour un arbre de décision par exemple comment et pourquoi choisir 70% pour ce chemin et 30% pour l'autre? Comment déterminer que l'hypothèse nulle est réalisée à x% ou que le risque d'accepter une hypothèse alors qu'elle est fausse est de telle valeur. Il faut avoir une masse de données pour déterminer ces seuils, qui peuvent l'être de fa?on empirique avec des personnes expérimentées qui ont travaillé sur ces questions. Encore ici IA et ML peuvent permettre d'aider à déterminer ces seuils que j'ai indiqués avec un maximum d'approximation.

Diversas s?o as ferramentas matemáticas de apoio à tomada de decis?es. Entre elas destacam-se as ferramentas de simula??o de fluxo de processos, que têm como objetivo representar um fluxo de processo real (ex. sistemas ferroviários, intralogística industrial, fluxo de pacientes em uma emergência médica, etc) em um fluxo de processo virtual (modelo de simula??o). Com estes modelos é possível testar cenários diversos, avaliando impactos de mudan?as, encontraando gargalos, propondo melhorias, entre outras vatantagens.

Impressed by Confluence's impact on enhancing collaboration in the supply chain! ???? This versatile platform facilitates seamless communication, document sharing, and project tracking, fostering a unified approach in complex supply chain environments. Confluence's real-time collaboration features enable cross-functional teams to collaborate efficiently, ensuring everyone stays on the same page. Its structured documentation and integration capabilities streamline processes, improving visibility and decision-making. Embracing Confluence has proven instrumental in optimizing communication and driving collective success in the dynamic landscape of supply chain management. ???? #Confluence #SupplyChainCollaboration #Efficiency

Collaboration tools are essential to communications throughout the supply chain. But there must be a continual connection from start to finish. I think this is the challenge that is being faced right now. There is an acknowledgement of the need to be connected, but there is an aversion to adopting the necessary processes to make those tools work. With the inflation experienced over the past three years, it will be essential that industries adopt these tools to reduce costs, improve efficiency, and meet the expectations of the customer and consumer. I believe email should be removed from the equation when talking about collaboration. It will be essential to reduce the number of touchpoints possible to make these processes most effective.

At the moment, many large companies, especially in the field of supply chains, have their own staff of operations research experts. This is an important department that not only improves the processes of implementing solutions, but also instills a culture of optimization thinking.

Learning tools are helpful to gain pieces of knowledge that can help you, but what you learn is only theory until it is applied to real-world scenarios. In the end, it doesn't matter how many tools are in your toolbox unless you can adjust those tools to real-world scenarios (especially the most complex). If learning tools are part of your strategy, it's important to focus on tutorials and examples that align with situations you are likely to encounter. If you cannot find any, take the time to also convert the concepts learned to a real-world scenario and test it out!

It is great to learn from all the above mentioned abstract ideas modifications but in my experience, the best learning tools are review and analysis of real life scenarios of other innovative approaches used by other operations managers.

In optimizing supply chains, the human aspect is inevitable. Even with advanced tools and methods, success hinges on the systematic discipline of the workforce. It's about nurturing a learning culture, open communication, and a safe, inclusive environment. Empowering employees with training and fostering inter and cross departmental collaboration ignites innovation. This human-centric approach enhances productivity, reduces turnover, and builds resilience. Strong partnerships with like-minded suppliers ensure an ethical, efficient network. While tools and techniques are vital, the real strength lies in the skilled hands of the people who manage and operate. Their engagement and well-being are key to a sustainable, adaptable future.

All the above mentioned tools should have clear mandate to or help towards attaining it (such as):simplify supply chain management process leading to improved efficiency and reduce costs. And this are ways to go about it: - Streamline communication between suppliers and manufacturers. - Optimize inventory levels to minimize stockouts and excess inventory. - Implement real-time tracking and monitoring systems. - Collaborate with partners to improve forecasting accuracy. - Automate repetitive tasks to save time and resources.

COTS: Planning: Demand Forecasting: SAP & Oracle Demand Sensing: JDA Demand Sourcing: Supplier Selection: Jaggaer Direct Supplier Segmentation: Ariba Network & Ivalua Supplier Performance Management: SAP Ariba & Zycus Raw Material Planning: Infor M3 & Epicor ERP Spend Analysis: Basware Analytics & SpendHQ Production: Capacity and Resource Planning: MS Dynamics 365 & NetSuite ERP Production Planning & Scheduling: Preactor APS (Siemens) Manufacturing Execution System (MES): Rockwell Automation FactoryTalk Logistics: Warehouse Operations: Manhattan Assoc Warehouse Mgmt Distribution Planning: HighJump Warehouse Advantage Transportation Management: Oracle & SAP Transportation Management Returns and Reverse Logistics: ReverseLogix

One of the most important capabilities to optimise supply chains will be to increase the visibility / the automated exchange along the supply chain. This will have implications for all other capabilities and provides a solid foundation.

A knowledge graph is essentially a highly interconnected map of all the entities and relationships involved in your operations. Unlike a traditional map that simply shows locations, a knowledge graph delves deeper to reveal 1. Entities: This includes everyone and everything involved in your supply chain 2.Relationships: This is where the magic happens. The knowledge graph maps the connections between these entities, capturing critical information Think of it as a powerful "brain" for your supply chain, constantly learning and evolving as new data is added. This empowers you to make informed decisions, adapt to changing conditions, and build a more resilient and efficient operation.