A manufacturing company expanded its services to a new city. The success rate in bidding for work has been around 15%. The manager seeks for ways to improve the success rate. Improving the bid approach, and hence the success rate, could be a challenge. The bid needs to be profitable. The price needs to be competitive. Internally, the plant needs to have the capacity to deliver the order. Fortunately, the company has been diligent in compiling data on all its bids. The manager wants to review the records and explore how best to improve the bidding strategy.
The above is an operations problem many businesses face. It can be addressed by developing a model to distill insights that are useful for decision making. To tackle this business analytics problem, take the time to define the 3 key elements critical to the analysis.
What is the goal you want to achieve? For the manufacturing company, it wants to maximize the bid success rate. For your business, it could be to maximize the profitability of a retail location or to minimize the cost of customer support. Identifying the objective might seem trivial but you need to be as specific as possible. The objective determines the scope of data needed for the analysis. The idea is to keep the dataset you need as narrow as possible because the results would be more relevant. To mine intelligence from data, more data doesn’t equate to quality.More data doesn’t equate to quality. Click To Tweet
- Decision variables
These are the action variables that you have control. For example, the margin the manufacturing company applies to each bid. Understanding what the decision variables are and their relationships helps to reveal the influence each variable has on the outcome. Once the model is set up, you could perform sensitivity analysis. The results reflect the impacts as you test the limits and flexibility you might have. Recognizing that there are limitations to the model, it is important to note how you plan to apply the model and the context of the application.
These are the restrictions the business needs to work with. These restrictions range from safety requirements, labour law, to production capacity. By identifying the constraints, you ensure that the choice you make would not violate any critical boundary. For the manufacturing business, the machines have scheduled downtime for preventative maintenance. This affects the production capacity and delivery timeline. When constraints are incorporated in the analytics model, you would be able to optimize operations by adoption select combinations of your decision variables and actions.
The above analysis provide options for your business. It is not an exact science but the insights, when combined with business acumen, would facilitate informed decision making for operational optimization.