There are many reasons, including:
Supply chain network software is usually configured for transactional efficiency and rarely incorporates supply chain effective optimization tools.
What's needed to be successful is a solution that offers a holistic view of the supply chain network with the ability to determine, in each instance, which of several possible solutions is best in terms of organizational goals.
Supply chain network design — or network optimization, as it is often called — can be defined as a process to establish a complete view of the organization's supply chain. This usually entails preparing some form of model that replicates the supply chain together with appropriate formulae that emulate actual transactions and transformations. Using this model, it's possible to evaluate different aspects of the supply chain and identify ways to optimize processes and reduce supply chain costs.
Depending on the complexity of the organization, this may entail using advanced mathematical and analytical supply chain network software to evaluate alternatives and determine optimal solutions.
Supply chain network design is a methodical process that determines the best combination of facilities, suppliers and products using mathematical modeling. It's a deliberate approach that avoids the usual ad-hoc growth process experience in most organizations.
The first step is to determine the organization's business objectives, such as the markets the organization is targeting, its growth plans and its financial objectives. Specific factors may include customer service levels, pricing, competition and cash flow.
These will determine supply chain network processes to be followed with an ultimate goal of determining the optimal combination of supply, production and distribution costs. This is an analytical process that avoids subjectivity and bias.
There are two aspects to consider when evaluating how often supply chain network optimization should be performed. On the one hand, there are warning signs that indicate it's needed, such as:
On the other hand, the effort and time needed to perform this exercise must be considered. This is directly related to how the supply chain is modeled. Manual modeling with spreadsheets is tedious, subject to error and requires significant resources. Conversely, supply chain network modeling using simulation software is easier to repeat and refine because the hard work of preparing and populating the model is largely done.
While practical considerations such as the effort needed to perform supply chain network optimization may dictate the frequency, rapid changes experienced in today's business world indicate that, ideally, supply chain network optimization should be performed as often as is possible.
Many organizations prefer external consultants because this imposes less workload on internal staff. While this is a practical solution, there are several reasons why external consultants are not always the best choice, including:
Other organizations prefer their own personnel for supply chain network optimization, even though this means increased manning. Benefits include:
Although treated as different functions, an organization's supply chain starts with materials procurement, continues through manufacturing, and then into sales and final product delivery. Many organizations use different software suites, such as procurement, manufacturing, CRM and logistics software, to handle these functions. This is not ideal because each of these functions should be considered as integral parts of the supply chain network. They all affect operational efficiency and must be part of supply chain network optimization process.
Sales and operations planning processes (S&OP) overcome some of these problems by eliminating the tendency for functional silos that hinder optimization. Integrated ERP also helps because it offers one view of the organization and an ability to track the network from start to finish.
The weakness of traditional S&OP is that it focuses on units of production, numbers of parts and sales volumes. While at first this appears logical, it excludes finance and represents a fundamental weakness, as the success or failure of a business is measured in financial terms.
The same concepts apply to supply chain network optimization processes because, ultimately, success is measured in terms of the costs the business incurs and how the process affects the bottom line and financial returns.
The ability to analyze complex supply chain networks is complicated by multiple factors that interact with each other in such a manner as to defy attempts to arrive at optimized solutions easily. This is why many older supply chain professionals place such a reliance on their gut reaction, although history shows that a gut feel, without strong underlying experience and knowledge, is likely to be wrong.
Fortunately, there is an answer, and that is through advanced analytics. Using advanced modeling techniques, it's possible to create a realistic model of the supply chain network and use the organization's data to determine the best decisions. This field, known as prescriptive analytics, uses advanced solver software to analyze supply chain network models and determine which of several scenarios offers the best payback.
The real advantage of using a prescriptive analytics model for supply chain network optimization is its ability to take into account constraints and operational limitations. This ensures that solutions proposed by mathematical modeling are real and feasible, something that isn't always the case when using less sophisticated modeling techniques.
The beauty of such a model is that it can be modified and adapted to reflect changing organizational realties, especially if the model is prepared using 5th generation modeling software that is intuitive in use and easy to update. Using a prescriptive analytics supply chain network model, it's possible to determine how to manipulate your supply chain network to achieve corporate strategic goals.
The key feature that separates prescriptive analytics-based supply chain optimization software from other techniques is the ability to determine the solution that offers the greatest payback or benefit rather than one that is simply feasible.
It's this ability that separates a prescriptive analytics solution from standard analytics often incorporated as part of procurement, MRP and ERP software suites. Their purpose is different, and that's to manage the multiple activities required to organize procurement and production efficiently. While such software will find a feasible solution, if one exists, they don't include the advanced modeling capabilities that prescriptive analytics have to determine which solution is best. A prescriptive analytics approach has a further benefit in that it can factor in the interdependencies and the complex trade-offs required that are vital for success as demonstrated by these supply chain network examples.