Needs Assessment

In the summer of 2019, there were some personnel changes in the Supply Chain Planning and Operations Team that caused an increase in the number of purchase orders that were not processed in a timely manner. When the global pandemic hit in early 2020, the global shipping ecosystem was disrupted and yet two years later, the number of purchase orders that were not being properly processed did not decrease. In the data reviewed for this project, it was determined that about $500,000 in purchase orders were at least two weeks late, with some purchase orders remaining open up to three months after the order had been delivered to the co-manufacturing facility.

When a purchase order remains open, the ERP system isn’t able to issue a processing job that requires the materials in that order. For example, if a certain facility is making pouches of applesauce and is waiting for a shipment of apples, it doesn’t matter if the apples are physically where they need to be if the ERP system doesn’t know if the apples are there. The $500,000 in late purchase orders can have ripple effects throughout the entire manufacturing process. Therefore, Joyful Belly Foods had a keen interest in finding a solution to this gap.

Methods

Models & Frameworks

Based on conversations with the client liaison, we used six different models or frameworks to gather and organize the data:

• Mager and Pipe’s Performance Analysis Flow Diagram (Mager, R., & Pipe, P., 1997) was used early on in the project to see if the problem was worth solving and if there were any quick fix interventions that could be implemented.

• Harless’ Front-End Analysis (Harless, J. H., 1973) was also applied to verify the findings from Mager and Pipe’s Performance Analysis Flow Diagram

• Rummler & Brache’s Nine Boxes Model (Stefaniak, J. E., 2021, pp. 92-93) was used for the bulk of the project. All of the data collected was coded using the Nine Boxes Model. We wanted a model that would work for a larger scope and early in the project we identified external personnel (e.g., staff of the co-manufacturing facilities) that would not easily fit into some other models.

• Bradshaw’s Taxonomy of Needs (Bradshaw, J., 1972) was used to help us determine the type of need based on data collected from the gap analysis.

• A Fault Tree Analysis (FTA; Watkins, R., Meiers, M. W., & Visser, Y., 2012, pp. 275-280) was used to identify potential causes of our performance gap. We then ranked and sorted the potential causes to identify the most probable leading causes.

• Once the FTA helped to identify potential causes, we considered a number of possible interventions and then used a Multicriteria Analysis (Watkins, R., Meiers, M. W., & Visser, Y., 2012, pp. 225-234) to rank our potential interventions in order to identify the interventions with the highest potential impact to the organization.

Data Collection Methods

We used three primary methods for collecting data. First, the member of our project team who acted as client liaison was also on the Supply Chain Planning and Operations Team. As such, she had a great deal of information regarding the processes and existing resources.

The second source we used was a series of reports. Some of these reports were generated by the ERP, but the reports that ended up being helpful were the spreadsheets maintained by the Operations Associates. These reports contained much more information regarding the timing of the purchase orders.

The final source of information was a set of interviews conducted with key members of the Supply Chain Planning and Operations Team. Based on the reports we had obtained from the ERP, we were able to identify top and bottom performers and made sure that they were included in those interviewed. We also crafted the interview questions to get as much information as possible regarding potential causes for the performance gap, particularly from the top and bottom performers.

Causes

The FTA revealed twelve potential root causes of the performance gap, and after consulting with key stakeholders, we narrowed the list down to four likely root causes:

• Insufficient structure or enforcement of a standardized process as outlined in the business contracts between Joyful Belly Foods and the co-manufacturing facilities

• No job aides or training is provided to external partners (co-manufacturing facilities)

• Insufficient job aides for internal employees and complete lack of standardized process

• The present condition of the global shipping environment

As a project team, we noticed that the majority of likely causes fell outside the scope of the data we had direct access to. Included in our recommendations was the suggestion that a follow-up assessment that included interviews with co-manufacturing staff and a review of external processes. This would shed much more light on the performance gap and potentially alter the solutions that we recommended.

Recommended Solutions

To address the issue of external partners not using a standardized process, we recommended altering the business contracts to require all co-manufacturing facilities to follow the same process. The data collected revealed one co-manufacturer in particular with an exemplary process that we recommended be duplicated across the other co-manufacturing facilities.

In conjunction with building a standardized process into the business contract, we also made the recommendation that Joyful Belly Foods prepare and distribute job aids and other external training materials to aid external partners with the newly standardized process. While it may be possible to create and distribute the job aids without making any alterations to the business contracts, the job aids alone lack any enforcement or incentives for the co-manufacturing facilities. Creating buy-in and adherence to the desired process would be difficult under such circumstances.

The final solution is potentially the most expensive up front. To address the global shipping environment, we suggested attempting to automate some or all of the process by implementing a data automation solution. One program that we discovered could be taught to recognize email attachments from specific senders (e.g., a co-manufacturing facility), pull the necessary data points from the attachment through optical character recognition (OCR), and then process the data by either sending it directly to the ERP or to an operational specialist. This solution doesn’t necessarily negate the need for the previously mentioned solutions, but has the potential to minimize the need for additional interventions.

References

Bradshaw, J. (1972). Taxonomy of social need. In: McLachlan, Gordon, (ed.) Problems and progress in medical care: essays on current research, 7th series. Oxford University Press, London , pp. 71-82.

Fortin, S., Farnsworth, M., & Harrison, L. (2021). Joyful Belly Foods Final Report. OPWL 529 Assignment. https://docs.google.com/document/d/1gveS4xiZufS0XKzHbqNS1WwibvWa_svG/view

Harless, J. H. (1973). An analysis of front-end analysis. Improving Human Performance: A Research Quarterly, 4, 229-244

Mager, R., & Pipe, P. (1997). Performance analysis flow diagram. In Analyzing performance problems, or you really oughta wanna: How to figure out why people aren’t doing what they should be, and what to do about it (3rd ed., p. 5). Center for Effective Performance.

Rummler, G. (2006). The anatomy of performance: A framework for consultants. In J. A. Pershing (Ed.), Handbook of human performance technology: Principles, practices, and potential (3rd ed., pp. 986-1007). Pfeiffer. https://doi.org/10.1002/pfi.20023

Stefaniak, J. E. (2021). Needs assessment for learning and performance: Theory, process, and practice. Routledge.

Watkins, R., Meiers, M. W., & Visser, Y. (2012). A guide to assessing needs: Essential tools for collecting information, making decisions and achieving development results. The World Bank.

Appendix

Final Report