With the advances in logistics and supply chain management technology in recent years, there’s been an explosion of interest in the topic of “Supply Chain Optimization”. While many businesses leaders have heard the term used, few know exactly what Supply Chain Optimization is, or how it could help their business. In this article, I’m going to explain what supply chain optimization is, as well as help you decide how it could help your business.
The easy answer to this question is that supply chain optimization is just that – optimizing your supply chain. A more precise definition is that supply chain optimization is the practice of combining resources in a supply chain with the intent of eliminating bottlenecks and other problems that interfere with the process and helping the supply chain function in a more smooth, timely and cost-effective manner.
Generally speaking, supply chain optimization begins with the use of advanced planning and scheduling (APS) technology. This technology uses various methods to analyze supply chain data and create simulations that help supply chain planners make decisions that help them reach their goals more effectively.
For example, if a company’s supply chain managers want to pursue a course of action that wouldn’t be feasible based on available resources, APS technology can alert them to this beforehand, saving them precious tie and money. Furthermore, this technology allows planners to decide on an alternate course of action that would allow them to achieve the objective they wish to achieve through other means.
While the concept of supply chain optimization has been around for years, given the immense logistical complexities of today’s interconnected and global marketplace, supply chain optimization and the benefits that it provides become less of a luxury and more of a necessity with each passing day.
One of the key factors that has led to this rise in the importance of optimizing the supply chain system is customer preference and demand. As customers have grown more accustomed to instant-gratification-oriented services, such as e-commerce and express delivery, and as the internet has provided them with an ever-increasing range of products and vendors to choose from, businesses have been forced to adapt by maximizing the speed and efficiency with which they can get the product to the customer.
For example, just a few decades ago, a customer would have gladly been willing to wait several weeks for a piece of electronics equipment to arrive in the mail. Now, customers not only expect an option for next day delivery when they place the order, but the company that lacks the means to fulfill this expectation risks driving the customer straight into the arms of the competition.
Also, as mentioned earlier, the globalization of applications such as marketing and distribution has led to the creation of the global supply chain and added additional layers of complexity to the process. Companies now routinely have production and storage facilities, offices, and customers located on the other side of the world. All of these facilities and customers have needs that have to be fulfilled on time if the company wants to maintain its profits.
Many companies have come to realize the value of supply chain optimization not only for improving customer satisfaction, but also for keeping their supply chain costs to a minimum. Specifically, optimizing your supply chain is done with the purpose of eliminating or at least minimizing the supply chain issues that would normally arise when either time or resources are limited.
Because it takes time to acquire materials, manufacture products, and deliver these products to customers, and even the largest and most established companies have limited resources for performing these activities, a considerable effort must be made in order to keep customers happy. This effort begins with advanced and detailed planning, and continues with effective execution of that plan. However, even under the best of circumstances, problems arise. Supply chain optimization attempts to systematically prevent those problems from arising or to provide solutions to them if they do arise.
Problems in the supply chain process are typically either internal or external. An internal problem could be one that stems from decisions that the supply chain planner has to make, such as when to order and when to ship. An external supply chain problem, on the other hand, is one that stems from the supply chain itself, such as a supplier experiencing a shortage of materials or parts, or a lag-time in the distribution network.
Of the various types of problems that can affect the functioning of a supply chain, some are more serious than others. For example, if a delivery truck has a maximum carrying limit of 500 Widgets, deciding to pack 600 widgets in order to meet a deadline could be destructive to the widgets and the vehicle, or even dangerous to the driver and others.
On the other hand, a customer having to wait two extra days for his widget to arrive due to short supply is a much less pressing issue, provided that this is a very rare occurrence. Furthermore, in the event that a delivery gets delayed, a company can also choose to give the customer a reward (eg: a coupon towards their next purchase) as a gesture of apology and goodwill.
Typically, supply chain optimization efforts make use of models designed to represent how these internal and external factors (constraints) relate to the company’s desired objectives. These models need to be as realistic as possible in order to accurately describe the problem that the company is facing and improve supply chain efficiency.
The models used in calculating a solution to an optimization problem are usually used to create a solver, which is a mathematical formula, or algorithm embedded into a computer program and designed to arrive at a logical solution that helps the company achieve its objectives.
There are three types of logical solutions that a computer can suggest to solve an optimization problem. These three types of solutions are known as feasible, optimal, and optimized. A feasible solution is one that solves the constraint, but may or may not accomplish the company’s objective. An optimum solution is one that achieves the company’s objective. Usually the best feasible solution becomes the optimum solution. An optimized solution is one that achieves the company’s objective in a way that is satisfactory, but not necessarily optimum. In other words, an optimized solution accomplishes at least part of the company’s objective, but isn’t necessarily the best solution to the problem.
So for example, a feasible solution to the problem of labor strike that causes a widget shortage which threatens to delay customer delivery dates might be for XYZ Widgets, Inc. to hire a labor negotiator to try and resolve the dispute. While this solution might resolve the immediate product constraint by ending the strike, it would not necessarily accomplish XYZ Widgets, Inc’s objective of getting the widget to the customer by the expected date.
An optimum solution might be to temporarily partner with a secondary widget supplier so that the widgets could still be delivered to the customer on time. This solution accomplishes the XYZ Widgets, Inc’s goal of fulfilling the expected delivery date.
An optimized solution to the problem might be to ship the widgets to the customers at a later date after the strike has ended. While this doesn’t accomplish the company’s objective of getting the widgets to the customers on time, it does accomplish their goal of getting the widgets to the customers. As such, this is a partial solution to the problem.
Solvers, data and models are the three most important elements in any supply chain optimization effort. The relationship between the three is that data is used to create a model, and the model is used to create a solver.
Because of this relationship, it is vital when creating a model that the data fed into the model be accurate. If the data is not valid, an incorrect model will be generated, resulting in an ineffective solution to the optimization problem.
However, depending on the level of supply chain planning, detailed data and models are not always necessary. Generally, the higher-up levels of supply chain planning, such as strategic planning, do not need extremely detailed models and source data. For these higher-up levels, data such as general demand trends may be sufficient. On the other hand, tactical planning usually does necessitate detailed models with specific amounts, dates and numbers.
In order for an optimization plan to be successful, the plan needs to take into account the various processes involved. Trying to develop one part of the plan separately from the rest of the plan can generate a less-than-optimized result. For example, if you develop an effective production plan, but fail to take into account the relevant distribution aspects, your plan may fall short of your desired optimization objectives.
As such, the trend has been for vendors to move towards more holistic approaches to optimization. These approaches take into account the various parts of optimization plans, and other aspects, such as supply chain integration, in order to form a synergistic whole. This larger overall trend has taken the form of three micro-trends within supply chain optimization.
The first of these micro-trends has been the movement towards synchronized concurrent planning. This differs from synchronized sequential planning, which is where one stage of planning was logically followed by a second stage (for example demand planning was followed by distribution planning, which was followed by manufacturing planning). By contrast, in synchronized concurrent planning, each stage of the planning is generated at the same time, and all constraints along the supply chain process are taken into account during the planning stage.
The next micro-trend has been towards synchronizing planning levels. As mentioned earlier, plans at a higher-up (example: strategic) level may not need to contain as much detail as plans at a lower (example: operational) level. However, the more synchronization there is between the various levels, the more optimized the overall plan will be. Three methods that are used to ensure this synchronization are: telescoping planning horizons (projecting timeframes for various planning levels), using a common data structure for all planning levels, and monitoring the synchronization process itself so that the planning levels stay in alignment.
The third micro-trend in optimization is towards planning and executing in real-time. Real-time planning and execution allows companies to eliminate long planning cycles. In turn, by reducing the planning cycles, these companies not only limit excess inventories but also get better customer service from their supply chains.
So as you’ve seen, supply chain optimization is a complex and evolving field with many applications. Taking advantage of these applications is a crucial part of ensuring that your business remains profitable and your customers remain happy. While it would be impossible to include all the nuances of this process in just one article, hopefully the information provided here will set you on the path to learning more about how this process can best fit your business.
Supply Chain Management