Supply Chain Planning Software Designed to Meet Your Changing Market Demands

Supply chain software solutions give businesses the ability to profitably manage business disruptions, and transform their supply chains into a competitive advantage. These companies create an agile, lean manufacturing environment that supports real-time decision-making instead of ‘after-the-fact’ firefighting activities.

Creating The Optimal Supply Plan with The Arkieva Supply Planner

The Arkieva Supply Planner is a comprehensive optimization tool for keeping supply aligned with corporate objectives in the face of change. Creating an optimal plan to meet demand is integral to managing your supply chain effectively. Our tool can generate a sustainable reduction in inventories up to 20% across the network through less costly overtime, increased throughput, and fewer setups.

Arkieva models the internal and external supply chain across the global enterprise; multi-site, multi-user, and enterprise-wide. Our tool synchronizes material and capacity allocation with production and planned purchases, optimizing operations with true customer demand. The ability to quickly re-plan provides increased flexibility in manufacturing.

The Supply Planner allows creation, of multiple what-if scenarios, varying factors such as demand and capacity, anticipating potential external threats like strikes, etc.

Arkieva Supply Planner also provides desktop capabilities for operations managers to promptly handle constantly evolving situations and still preserve both margins and customer service. The familiar Excel-like interface makes it easy to enter or review capacity, costs, and other required data used for optimization. It can easily generate supply/demand reports, including side-by-side, visual comparison of what-if scenarios.

Manage Change Profitably
Flexibility and rapid turnaround are essential in a changing marketplace, whether evaluating new growth opportunities or managing costs. Arkieva Supply Planner offers the ability to quickly create profit-driven supply scenarios for contingency planning, in a sandbox environment, before committing resources.

The Arkieva Supply Planner helps answer such questions as:

  • Will we be able to cover demand if a shutdown continues?
  • How soon should we begin building inventory to support an upcoming promotion?
  • What is the most cost-effective way to distribute our product?
  • Can we accept a significant additional order without disrupting customer service?
  • How should we respond to a spike in raw material costs?

See it in action for yourself and find out firsthand how you can get the most from your assets. Schedule a demo from the link below or ask us about Arkieva’s Project Self-Funding.

Multiple Alternate BOMs

Summary
A company must make decisions each month on the best way to satisfy demand when it has many products manufactured with different BOMs. An optimization model can be a valuable tool in getting the most out of resources while keeping costs low and customer service levels high.

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The Opportunity
If you have many products which can be made with different recipes and on different facilities, how do you decide where and how to make them? Different manufacturing methods may have different yields, or some products can be produced at a higher rate on certain machines. Usually, each product will have a preferred manufacturing method, but often ingredients are scarce, or capacity is limited, preventing you from making all products in a preferred way. This means you will need to choose which products need to utilize secondary recipes in order to satisfy the demand. Different choices may lead to significant cost differences, so it’s important to plan all the products jointly to minimize the cost while meeting demand promptly.

Solution Description
An optimization model can be used to determine how best to manufacture all the products with the given resources, minimizing the sum of all costs, including unmet demand, cost of production and inventory cost. The different recipes are prioritized, but it is free to choose a secondary recipe if it lowers overall cost. Because it can compute automatically the amount of capacity used by each recipe, it stays within the limited number of available hours. An optimization model can even look ahead to future periods when capacity might be low and build inventory to meet future demand which could not otherwise be satisfied. By considering all the possible supply options, it can better utilize resources by developing a global plan.

Blending Problem

Summary
Satisfying the demand of customers with products of many different purities can be a complicated decision that can prove to be of very high value. An optimization tool provides a way to utilize all resources efficiently and in a cost-effective manner.

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Problem Description
Balancing the many different costs of manufacturing is a difficult task when you consider the almost limitless possibilities of the ‘what,’ ‘how,’ and ‘where’ to produce a product. Manufacturing resources are limited both in capability and capacity, meaning many products contain a key ingredient with varying concentrations and the supply of those key ingredients also comes in different concentrations. Transportation costs, inventory limits, inventory costs and contractual considerations, such as “Take-or-Pay,” are also all key factors in deciding the manufacturing plan.

Solution Description
An optimization model can take all the restrictions on supply and capacity and find the best way to satisfy the demand. It takes into account the purities and costs of all input streams to find the best way to get output with the desired purity at the lowest cost. It can also take into consideration any contractual obligations. It balances all the costs and revenues simultaneously to come up with the best answer possible, within the given constraints. It also allows users to evaluate impacts of changing suppliers, adding new capacity or changing demand.

Optimization vs. Spreadsheets
In the past, many companies have made decisions based on spreadsheets, requiring a user to input various production and sourcing decisions and letting the spreadsheet calculate costs. They would iterate until they found a satisfactory answer. This often can provide a good answer to a skillful user but can be hard to maintain in the long-run, especially if that person left the company. There might also be many hidden assumptions in the spreadsheet that would be difficult or impossible to override. Optimization models can be built with fewer assumptions about sourcing and production and can very quickly find the best solution without trial and error.

Cutting Problem

Summary
When part of a production process involves cutting larger sizes of products into smaller ones, the problem of how to do this, so waste is minimized while keeping productivity high is extremely important. Even a 1% reduction in waste can often mean substantial savings. An optimization tool can provide significant improvements in multiple aspects of these processes.

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The Opportunity
For businesses to be more profitable, they need to balance the costs of waste, manufacturing, and inventory, and come up with the best plan to satisfy their demand. There are many industries in which products are manufactured in large sizes that need to be cut down to smaller sizes which are then sold to customers; examples include paper, film, and glass. Often, industries cut their larger products into smaller sizes in many different ways and during the cutting process, it is always important to minimize the amount of product lost and maintain the productivity of resources. Some cutting patterns with very low waste may also take much longer to cut or cause major problems for material handling. And if semi-finished inventory is kept, it needs to be in sizes that can be cut efficiently.

Solution Description
An optimization model finds the combination of patterns that will produce the necessary number of each size with minimum cost. While minimizing waste is important, it would be a mistake to make it the sole component of the cost function. The goal should be to minimize total cost, which includes not only waste, but also resource usage and inventory. Some patterns with low waste may require longer setups and processing time. Even cutting strategies with the same waste may differ in how efficiently they can be cut. Unless the cost of using resources is considered, the plan is likely to be suboptimal.

It is also important to consider the cost of inventory. To keep waste loss low, it is common to produce extra of some sizes that fit well in a pattern. While this is fine if the extra will be usable and there is not too much inventory, the cost still needs to be considered. Many of the problems in cutting can be made a lot easier by having a size efficiently cut into smaller sizes. Planning should include the generation of the larger sizes, as well as the downstream pattern generation. Producing wider widths upstream may look more efficient, but may just translate into downstream waste. Production needs to be optimized across the whole process.